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https://www.investopedia.com/terms/d/data-analytics.asp

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What Is Data Analytics?

Data analytics is the science of analyzing raw data to make conclusions about information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Key Takeaways

Understanding Data Analytics

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

Data Analysis Steps

The process involved in data analysis involves several steps:

1. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category.

2. The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.

3. The data must be organized after it's collected so it can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.

4. The data is then cleaned up before analysis. It's scrubbed and checked to ensure that there's no duplication or error and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Types of Data Analytics

Data analytics is broken down into four basic types:

1. Descriptive analytics: This describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?

2. Diagnostic analytics: This focuses more on why something happened. It involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?

3. Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?

4. Prescriptive analytics: This suggests a course of action. We should add an evening shift to the brewery and rent an additional tank to increase output if the likelihood of a hot summer is measured as an average of these five weather models and the average is above 58%,

Data analytics underpins many quality control systems in the financial world, including the ever-popular Six Sigma program. It's nearly impossible to optimize something if you aren't properly measuring it, whether it's your weight or the number of defects per million in a production line.

The sectors that have adopted the use of data analytics include the travel and hospitality industry where turnarounds can be quick. This industry can collect customer data and figure out where problems, if any, lie and how to fix them.

Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information that retailers collect and analyze can help them identify trends, recommend products, and increase profits.

Data Analytics Techniques

Data analysts can use several analytical methods and techniques to process data and extract information. Some of the most popular methods include:

Regression analysis entails analyzing the relationship between dependent variables to determine how a change in one may affect the change in another.

Factor analysis entails taking a large data set and shrinking it into a smaller data set. The goal of this maneuver is to attempt to discover hidden trends that would otherwise have been more difficult to see.

Cohort analysis is the process of breaking a data set into groups of similar data, often into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.

Monte Carlo simulations model the probability of different outcomes happening. They're often used for risk mitigation and loss prevention. These simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.

Time series analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.

Data Analytics Tools

Data analytics has rapidly evolved in technological capabilities in addition to a broad range of mathematical and statistical approaches to crunching numbers. Data analysts have a broad range of software tools to help acquire data, store information, process data, and report findings.

Data analytics has always had loose ties to spreadsheets and Microsoft Excel. Data analysts also often interact with raw programming languages to transform and manipulate databases.

Data analysts also have help when reporting or communicating findings. Both Tableau and Power BI are data visualization and analysis tools used to compile information, perform data analytics, and distribute results via dashboards and reports.

Other tools are also emerging to assist data analysts. SAS is an analytics platform that can assist with data mining. Apache Spark is an open-source platform useful for processing large sets of data. Data analysts have a broad range of technological capabilities to further enhance the value they deliver to their company.

The Role of Data Analytics

Data analytics can enhance operations, efficiency, and performance in numerous industries by shining a spotlight on patterns. Implementing these techniques can give companies and businesses a competitive edge. The process includes four basic steps of analysis.

Data Mining

As the name suggests, this step involves "mining" or gathering data and information from across a broad spectrum of sources. Various forms of information are then recreated into the same format so they can eventually be analyzed. The process can take a good bit of time, more than any other step.

Data Management

Data requires a database to contain, manage, and provide access to the information gathered through mining. The next step in data analytics is therefore the creation of such a database to manage the information. SQL was a common tool used for this purpose in the early days of data analytics and it's still widely used in 2023. Created in 1979, this computing language allows relational databases to be queried and the resulting data sets to be more easily analyzed.

Statistical Analysis

The third step is statistical analysis. It involves the interpretation of the gathered and stored data into models that will hopefully reveal trends that can be used to interpret future data. This is achieved through open-source programming languages such as Python. More specific tools for data analytics, like R, can be used for statistical analysis or graphical modeling.

Data Presentation

The results of the data analytics process are meant to be shared. The final step is formatting the data so it's accessible to and understandable by others, particularly those individuals within a company who are responsible for growth, analysis, efficiency, and operations. Having access can be beneficial to shareholders as well.

Importance and Uses of Data Analytics

Data analytics provide a critical component of a business's probability of success. Gathering, sorting, analyzing, and presenting information can significantly enhance and benefit society, particularly in fields such as healthcare and crime prevention. But the uses of data analytics can be equally beneficial for small enterprises and startups that are looking for an edge over the business next door, albeit on a smaller scale,

Why Is Data Analytics Important?

Implementing data analytics into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions.

What Are the 4 Types of Data Analytics?

Data analytics is broken down into four basic types. Descriptive analytics describes what has happened over a given period. Diagnostic analytics focuses more on why something happened. Predictive analytics moves to what is likely going to happen in the near term. Finally, prescriptive analytics suggests a course of action.

Who Is Using Data Analytics?

Data analytics has been adopted by several sectors where turnarounds can be quick, such as the travel and hospitality industry. Healthcare is another sector that combines the use of high volumes of structured and unstructured data, and data analytics can help in making quick decisions. The retail industry also uses large amounts of data to meet the ever-changing demands of shoppers.

The Bottom Line

Data analytics helps individuals and organizations make sure of their data in a world that's increasingly becoming reliant on information and gathering statistics. A set of raw numbers can be transformed using a variety of tools and techniques, resulting in informative, educational insights that drive decision-making and thoughtful management.

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Data Analytics: What It Is, How It's Used, and 4 Basic Techniques

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Data analytics is the science of analyzing raw data in order to make conclusions about that information. It helps businesses perform more efficiently and maximize profit.

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https://www.northeastern.edu/graduate/blog/what-does-a-data-analyst-do/#:~:text=The%20data%20analyst%20serves%20as,modeling%2C%20science%2C%20or%20math.

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Organizations in all industries increasingly rely on data to make critical business decisions-which new products to develop, new markets to enter, new investments to make, and new (or existing) customers to target. They also use data to identify inefficiencies and other business problems that need to be addressed.

In these organizations, the job of the data analyst is to assign a numerical value to these important business functions so performance can be assessed and compared over time. But the job involves more than just looking at numbers: An analyst also needs to know to use data to enable an organization to make more informed decisions.

These roles are in high demand. IBM has that there will be more than 2.7 million job openings for professionals with data skills by 2020, with nearly 40 percent of jobs in advanced data analytics requiring a master's degree or higher. Entry-level data analysts receive an average starting annual salary in the $60,000 range; however, success in the role can lead to senior positions with salaries exceeding $135,000.

If the role of a data analyst sounds like a good fit for you, here's what you need to know.

What is Analytics?

brings together theory and practice to identify and communicate data-driven insights that allow managers, stakeholders, and other executives in an organization to make more informed decisions. Experienced data analysts consider their work in a larger context, within their organization and in consideration of various external factors. Analysts are also able to account for the competitive environment, internal and external business interests, and the absence of certain data sets in the data-based recommendations that they make to stakeholders.

A Master of Professional Studies in Analytics prepares students for a career as a data analyst by covering the concepts of probability theory, statistical modeling, data visualization, predictive analytics, and risk management in the context of a business environment. In addition, a master's degree in analytics equips students with the programming languages, database languages, and software programs that are vital to the day-to-day work of a data analyst.

Download Our Free Guide to Breaking Into Analytics

A guide to what you need to know, from the industry's most popular positions to today's sought-after data skills.

Types of Data Analytics

Four types of data analytics build on each other to bring increasing value to an organization.

Descriptive analytics examines what happened in the past: Monthly revenue, quarterly sales, yearly website traffic, and so on. These types of findings allow an organization to spot trends.

Diagnostic analytics considers why something happened by comparing descriptive data sets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome.

Predictive analytics seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action-like reaching out to a customer who is unlikely to renew a contract, for example.

Prescriptive analytics attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.

In a 2016 of more than 2,000 business executives, the consultancy PwC found that organizations find descriptive analytics to be insufficient for informed, data-driven decision making. As such, diagnostic and predictive analytics are increasingly important to organizations.

Key Responsibilities of a Data Analyst

The answer to the question "What does a data analyst do?" will vary depending on the type of organization and the extent to which a business has adopted data-driven decision-making practices. Generally speaking, though, the responsibilities of a data analyst typically include the following:

Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.

Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.

Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.

Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.

Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data .

Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.

Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.

Most Valuable Skills for Data Analysts

Effective data analysts possess a combination of technical skills and leadership skills .

Technical skills include knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets; and data visualization software such as Tableau or Qlik. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data.

Learn more: Project Management Skills for Data Analysts

Leadership skills prepare a data analyst to complete decision-making and problem-solving tasks. These abilities allow analysts to think strategically about the information that will help stakeholders make data-driven business decisions and to communicate the value of this information effectively. For example, project managers rely on data analysts to track the most important metrics for their projects, to diagnose problems that may be occurring, and to predict how different courses of action could address a problem.

Data Analysis vs. Data Science vs. Business Analysis

The difference in what a data analyst does as compared to a business analyst or a data scientist comes down to how the three roles use data.

The data analyst serves as a gatekeeper for an organization's data so stakeholders can understand data and use it to make strategic business decisions. It is a technical role that requires an undergraduate degree or master's degree in analytics , computer modeling, science, or math.

The business analyst serves in a strategic role focused on using the information that a data analyst uncovers to identify problems and propose solutions. These analysts typically earn a degree in a major such as business administration, economics, or finance.

The data scientist takes the data visualizations created by data analysts a step further, sifting through the data to identify weaknesses, trends, or opportunities for an organization. This role also requires a background in math or computer science , along with some study or insight into human behavior to help make informed predictions.

At startups and other small organizations, it is not uncommon for a data analyst to take on some of the predictive modeling or decision-making responsibilities that may otherwise be assigned to a data scientist.

What Do Data Analysts Make?

The average annual can range from approximately $ salary of a data analyst 60,000 . According to the sites' job listings, roles at financial and technology firms tend to pay higher than average.

Learn more: How to Increase Your Salary as a Data Analyst

The data analyst role is also a natural stepping stone for more senior data-driven jobs. According to PayScale, data analysts move on to roles such as senior data analyst, data scientist, analytics manager, and business analyst. These roles come with substantial increases in pay, too. According to IBM's , the annual salary of data scientists will start at nearly $95,000, while analytics managers will make nearly $106,000 per year.
If you're interested in building a career in analytics , take the first step by downloading our free, comprehensive guide below.

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What Does a Data Analyst Do: Responsibilities, Skills, and Salary

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Organizations in all industries increasingly rely on data to make critical business decisions-which new products to develop, new markets to enter, new investments to make, and new (or existing) customers to target. They also use data to identify inefficiencies and other business problems that need to be addressed.

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https://www.scnsoft.com/blog/4-types-of-data-analytics

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4 Types of Data Analytics to Improve Decision-Making

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Learn about different types of data analytics and find out which one suits your business needs best: descriptive, diagnostic, predictive or prescriptive.

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https://www.tibco.com/reference-center/what-is-data-analytics

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Whitepaper: The Spotfire A(X) Experience: The Future of Analytics is Now Orchestrate Analytics with an Agile Machine Learning Architecture Top 3 Ways to Use Data Science 11 Analytics Use Cases for Getting the Most From Smart Grid Data Actionable Marketing Analytics using Spotfire

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https://www.g2.com/articles/data-analysis-process

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Businesses generate and store tons of data every day, but what happens to this data after it's stored?

The short answer is that most of it sits in repositories and is almost never looked at again, which is quite counterintuitive.

Data can hold valuable insights into users, customer bases, and markets. When paired with analytics software, data can help businesses discover new product opportunities, marketing segments, industry verticals, and much more.

The problem isn't the lack of data available but the ambiguity in determining how exactly the data should be analyzed and used.

To clear up any uncertainties, businesses should understand the entire data analysis process in detail to make data-driven and informed business decisions.

What is data analysis?

Data analysis is a process of finding, collecting, cleaning, examining, and modeling data to derive useful information and insights and understand the derived information for data-driven decision-making.

Now that you have a general overview of the data analysis process, it's time to dig deeper into each step.

What are the 5 steps of the data analysis process

The data analysis process is a collection of steps required to make sense of the available data. Identifying the critical stages in a data analysis process is a no-brainer. However, each step is equally important to ensure that the data is analyzed correctly and provides valuable and actionable information. Let's take a look at the five essential steps that make up a data analysis process flow.

Step 1: Define why you need data analysis

Before getting into the nitty-gritty of data analysis, a business must first define why it requires a well-founded process in the first place. The first step in a data analysis process is determining why you need data analysis. This need typically stems from a business problem or question, such as:

How can we reduce production costs without sacrificing quality?

What are some ways to increase sales opportunities with our current resources?

Do customers see our brand positively?

In addition to finding a purpose, consider which metrics to track along the way. Also, be sure to identify sources of data when it's time to collect.

This process can be long and arduous, so building a roadmap will greatly prepare your data team for all the following steps.

Step 2: Collect data

After a purpose has been defined, it's time to begin collecting the data needed for analysis. This step is important because the nature of the collected data sources determines how in-depth the analysis is.

Data collection starts with primary sources, also known as internal sources. This is typically structured data gathered from CRM software, ERP systems, marketing automation tools, and others. These sources contain information about customers, finances, gaps in sales, and more.

Then comes secondary sources, also known as external sources. This is both structured and unstructured data that can be gathered from many places.

For example, if you're looking to perform a sentiment analysis toward your brand, you could gather data from review sites or social media APIs.

While it's not required to gather data from secondary sources, it could add another element to your data analysis. This is becoming more common in the age of big data.

Step 3: Clean unnecessary data

Once data is collected from all the necessary sources, your data team will be tasked with cleaning and sorting through it. Data cleaning is extremely important during the data analysis process, simply because not all data is good data.

Data scientists must identify and purge duplicate data, anomalous data, and other inconsistencies that could skew the analysis to generate accurate results.

80%

of a data scientist's time is spent on cleaning data than generating insights.

With advances in data science and machine learning platforms, more intelligent automation can save a data analyst's valuable time while cleaning data.

Step 4: Perform data analysis

One of the last steps in the data analysis process is analyzing and manipulating the data. This can be done in a variety of ways.

One way is through data mining, which is defined as "knowledge discovery within databases". Data mining techniques like clustering analysis, anomaly detection, association rule mining, and others could unveil hidden patterns in data that weren't previously visible.

There's also business intelligence and data visualization software, both of which are optimized for decision-makers and business users. These options generate easy-to-understand reports, dashboards, scorecards, and charts.

Data scientists may also apply predictive analytics, which makes up one of the four data analytics used today (descriptive, diagnostic, predictive, prescriptive). Predictive analysis looks ahead to the future, attempting to forecast what will likely happen next with a business problem or question.

What are the types of data analysis methods?

Data analysis methods can be broadly classified into the following categories:

Quantitative data analysis

Qualitative data analysis

Examples of data analysis techniques

Data analysts can use many data analysis techniques to extract meaningful information from aw data for real-world applications and computational purposes. Some of the notable data analysis techniques that aid a data analysis process are:

Exploratory data analysis

Exploratory data analysis is used to understand the messages within a dataset. This technique involves many iterative processes to ensure that the cleaned data is further sorted to better understand the useful meaning. Data visualization techniques such as analyzing data in an Excel sheet or other graphical format and descriptive analysis techniques such as calculating the mean or median are examples of exploratory data analysis.

Using algorithms and models

Algorithms have become an integral part of today's data environment and include mathematical calculations for data analysis. Mathematical formulas or models such as correlation or causation help identify the relationships between data variables.

Modeling techniques such as regression analysis analyze data by modeling the change in one variable caused by another. For example, determining whether a change in marketing (independent variable) explains a change in engagement (dependent variable). Such techniques are part of inferential statistics, the process of analyzing statistical data to draw conclusions about the relationship between different sets of data.

Step 5: Interpret the results

The final step is interpreting the results from the data analysis. This part is essential because it's how a business will gain actual value from the previous four steps.

Interpreting data analysis results should validate why you conducted it, even if it's not 100 percent conclusive. For example, "options A and B can be explored and tested to reduce production costs without sacrificing quality."

Analysts and business users should look to collaborate during this process. Also, when interpreting results, consider any challenges or limitations that may not have been present in the data. This will only bolster your confidence in the next steps.

Why is data analysis so important?

From small businesses to global enterprises, the amount of data businesses generate today is simply staggering, and this is why the term "big data" has become so buzzwordy.

However, this mountain of data hardly does much other than clog up cloud storage and databases without proper data analysis.

To uncover a variety of insights that sit within your systems, learn more about data analytics and implement it to extract valuable insights.

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What Is the Data Analysis Process? 5 Key Steps to Follow

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Businesses generate and store tons of data every day, but what happens to this data after it's stored? The short answer is that most of it sits in repositories and is almost never looked at again, which is quite counterintuitive. Data can hold valuable insights into users, customer bases, and markets.

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As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.

The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question "what happened?" This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.

The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. This information provides new insight from data. Advanced analytics addresses "what if?" questions.

The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. The collection of big data sets is instrumental in enabling these techniques. Big data analytics enables businesses to draw meaningful conclusions from complex and varied data sources, which has been made possible by advances in parallel processing and cheap computational power.

Types of Data Analytics

Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process. These are also the primary data analytics applications in business.

Descriptive analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance.

Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps:

Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.

Data that is related to these anomalies is collected.

Statistical techniques are used to find relationships and trends that explain these anomalies.

Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques, such as: neural networks, decision trees, and regression.

Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.

These types of data analytics provide the insight that businesses need to make effective and efficient decisions. Used in combination they provide a well-rounded understanding of a company's needs and opportunities.

What is the Role of Data Analytics?

Data analysts exist at the intersection of information technology, statistics and business. They combine these fields in order to help businesses and organizations succeed. The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data.

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The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis.

Data mining is an essential process for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases, or raw sensor data. The key steps in this process are to extract, transform, and load data (often called ETL.) These steps convert raw data into a useful and manageable format. This prepares data for storage and analysis. Data mining is generally the most time-intensive step in the data analysis pipeline.

Data management or data warehousing is another key aspect of a data analyst's job. Data warehousing involves designing and implementing databases that allow easy access to the results of data mining. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are becoming more common as well.

Statistical analysis allows analysts to create insights from data. Both statistics and machine learning techniques are used to analyze data. Big data is used to create statistical models that reveal trends in data. These models can then be applied to new data to make predictions and inform decision making. Statistical programming languages such as R or Python (with pandas) are essential to this process. In addition, open source libraries and packages such as TensorFlow enable advanced analysis.

The final step in most data analytics processes is data presentation. This step allows insights to be shared with stakeholders. Data visualization is often the most important tool in data presentation. Compelling visualizations can help tell the story in the data which may help executives and managers understand the importance of these insights.

Why Data Analytics is Important?

The applications of data analytics are broad. Analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an increasingly competitive world.

One of the earliest adopters is the financial sector. Data analytics has an important role in the banking and finance industries, used to predict market trends and assess risk. Credit scores are an example of data analytics that affects everyone. These scores use many data points to determine lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions.

The use of data analytics goes beyond maximizing profits and ROI, however. Data analytics can provide critical information for healthcare (health informatics), crime prevention, and environmental protection. These applications of data analytics use these techniques to improve our world.

Though statistics and data analysis have always been used in scientific research, advanced analytic techniques and big data allow for many new insights. These techniques can find trends in complex systems. Researchers are currently using machine learning to protect wildlife.

The use of data analytics in healthcare is already widespread. Predicting patient outcomes, efficiently allocating funding and improving diagnostic techniques are just a few examples of how data analytics is revolutionizing healthcare. The pharmaceutical industry is also being revolutionized by machine learning. Drug discovery is a complex task with many variables. Machine learning can greatly improve drug discovery. Pharmaceutical companies also use data analytics to understand the market for drugs and predict their sales.

The internet of things (IoT) is a field that is used alongside machine learning. These devices provide a great opportunity for data analytics. IoT devices often contain many sensors that collect meaningful data points for their operation. Devices like the Nest thermostat track movement and temperature to regulate heating and cooling. Smart devices like this can use data to learn from and predict your behavior. This will provide advance home automation that can adapt to the way you live.

The applications of data analytics are seemingly endless. More and more data is being collected every day - this presents new opportunities to apply data analytics to more parts of business, science and everyday life.

Data Analytics FAQ

What is the role of data analytics?

Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.

What are the types of data analytics?

There are various types of data analysis including descriptive, diagnostic, prescriptive and predictive analytics. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, a data analyst would use diagnostic analytics to figure out why something happened.

What are the analytical tools used in data analytics?

There are various tools used in data analysis. Some data analysts use business intelligence software, such as Tableau. Others may use programming languages such as SQL or Python, which have various statistical and visualization libraries.

What is the career growth in data analytics?

According to O*NET, the projected growth for data analysts is 8% between 2019-2029. On average, data analysts earned $94,280 in 2019. However, salary compensation for data analysts varies depending on where they work and what industry they work in.

Last updated: February 2021

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As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals. The data analytics process has some components that can help a variety of initiatives.

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https://www.digitalvidya.com/blog/data-analytics-skills/

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The world has evolved so much that people have moved from being superstitious to being logical. Unlike before, people now correlate events and occurrences rationally to ascertain the cause of problems and proffer possible solutions.

Therefore, Data Analytics refers to a method in which data is collected and organized in such a manner that meaningful and helpful information can be derived from it. With the u

In other words, the main reason behind data analysis is to examine the information embedded in any data. This requires having data analytics skills.

Looking at it from another perspective, data analysis refers to the process of cross-checking, cleaning, reorganizing and modelling data for decision making in business. Data gotten from different sources are put together, examined and then analyzed to arrive at a conclusion.

Therefore, a data analyst gathers, arranges, processes and model data. He is capable of analyzing a large volume of data, be it structured or unstructured. At the end of the day, the main goal of data analysis is to prepare and present data in the right form (graphs, charts or tables) for decision-making and problem-solving process.

To analyze means to break down a whole into its components in order to examine each of them individually and generate an explanation for why it is the way it is.

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Over the years, people who are vast in mathematical and programming abilities have been getting handsomely rewarded. This is because these two abilities form the basis on which data analysis operates. As more and more companies generate huge data, new industries have joined in craving for these data analyst skill set, especially in the technology sector.

Skills Gap in Companies today

In recent times, employees seem to have little understanding of their companies' data. Aside from having enough data analysts, most companies have surprisingly few people who can analyze their data in the most basic manners.

This trend may be blamed partly on the system companies have in place. Many businesses, especially those that grew through acquisition and inherited several IT departments, save their data in systems that are difficult for non-technical employees to access or use.

That discourages the majority of people from going anywhere near their company's raw data. What is even the bigger obstacle is that, as much as decent tools may be available, it takes technical know-how and patience, which most people do not have, to use them?

Data analysts are in high demand in virtually all sectors including consulting, government, pharmaceuticals, manufacturing, finance and education. If you are curious, mathematically skilled, and analytical, then you should consider a career in data analytics.

Some of the areas where you can work as a data analyst include:

Responsibilities of a Data Analyst

Some major responsibilities you would be carrying out when employed as a data analyst are:

1. You would identify areas where your company needs to improve in efficiency and process automation

2. You would come up with records of the management policies and processes

3. You would initiate and maintain processes of automated data

4. You would develop and support various reporting processes.

5. You would monitor and audit the quality of data

6. You would identify, evaluate and implement services and tools from external sources to support validation of data and cleansing.

7. You would work with both internal and external clients to adequately understand data content.

8. You would Create dashboards, graphs and visualizations on data.

9. From time to time, you would prepare reports for both internal and external audiences using various business analytics reporting tools.

10. Design, conduct and analyze survey data.

Skills Required for Data Analyst

If you intend to become a data analyst, you must start by ensuring you get a good background in mathematics, technology, business intelligence, data mining and statistics. You also need to possess a couple of data analytics skills which include:

As the name implies, it is clear that data analytics skills are of huge importance in data analysis. These data analytics skills refer to the ability to gather, view and analyze all forms of information in details. They also mean the ability to view a challenge or situation from different perspectives.

Data Analytics skills are major data analyst skills that make it possible for you to address problems by making decisions in the most appropriate way. Hence, if you wish to become a successful data analyst, you need to acquire and improve your data analytics skills and thinking.

2.) Numeracy Skills

These refer to mathematical skills which include a wide range of abilities including:

1. Have good knowledge of figures and numbers

2. Understand the relationships existing between numbers

Interpret any mathematical information

1. Have the ability to organize information

2. Be able to schedule or budget

3. Be conversant with trends

4. Know how to measure and analyze data

5. Be able to work with graphical information

6. Possess calculation skills

3.) Technical and Computer Skills

Good computer and data analyst technical skills are among the important data analytics skills. You should possess a basic knowledge of statistics. Also, you need to be familiar with some computer software and tools including; scripting language (Matlab, Python), Querying Language (SQL, Hive, Pig), Spreadsheet (Excel) and Statistical Language (SAS, R, SPSS). Other computer skills include; programming (Javascript, XML), big data tools (Spark, Hive HQL) and so on.

These are also important data analytics tools that any intending data analyst must develop. Ability to pay attention to details allows a data analyst find and see initially unseen details and links. This ability is especially important at the point of solving problems and making decisions. One who pays attention to details to work better and stands a lower risk of making errors.

Employers do not joke with this strength, this explains why many people include attention to details ability in their résumés and job applications.

5.) Business Skills

You also need to possess certain business skills to function well as a data analyst. The major business skills you need include:

Decision-making and problem-solving skills: These two work together. This is the main crux of data analysis as data analytics jobs are to give the right information for decision-making and problem-solving process.

The ability to acquire knowledge of new things, especially with regards to technological developments.

Communication skills: As a data analyst, it is expected of you to work with different people in your team. You should be able to communicate effectively with directors and co-workers to prepare, present and explain data.

Time management and organization skills: Your ability to manage time effectively will enable you to handle your responsibilities and meet deadlines.

6.) Communication Skills

Data Analytics job requires you to be an excellent communicator. In other words, you must be able to facilitate meetings, make the right requests and be an active listener in order to assimilate new information. Your communication proficiency should also cut across different digital platforms such as the internet, conference calls, SMS among others. The nature of data analytics jobs requires you to spend a significant amount of time relating to management, users, developers and clients. You really do not want to disappoint any of these categories of people.

Aside all of the above, some experts have argued that essentially there are 7 data analytics skills required. The data analyst qualifications include:

1. Campaign Management (Adobe Campaign): To become a successful data analyst, you need to get a good grasp of this software. It allows you to manage your data and makes it possible for you to monitor your cross-channel campaigns from a particular place. Don't just get familiar with this important tool, get confident with it.

2. Analytical and Creative Mind: You need to show that you are passionate about the industry and that you possess a creative and analytical mind for anyone to take you seriously. Go ahead and create campaigns and projects of your own to showcase your creativity.

3. Advanced Microsoft Excel: You cannot separate Excel form data analysis as it plays a very important role in the process. Make sure you know your way around the numerous functions available on Excel to be successful in data analysis profession.

4. The Data and analytics industry is not different from other industries when it comes to preferring with relevant experience of working with people in a team over those who don't. In as much as the majority of data analytic work may be done independently, you still need a team to execute certain projects. Of course, you need to clearly spell this out in your curriculum vitae.

5. You need to know such scripting and analytical languages like Python, Matlab, R and Scripting and Statistical Language: SAS as a data analyst. The more of these languages you know, the more you can include in your CV, thereby increasing your chances of landing your dream job.

6. Though Adobe and Google Analytics: Google Analytics is the most popular analytical tool, Adobe Analytics is also very useful in powerful analytics. Acquiring an advanced understanding of these tools will definitely boost your chances of beating competitions in the market.

7. SQL is a very important language when it comes to data analysisStructured Query Language (SQL) Programming: . Most employers consider it to be among the most valuable data analytics skills. However, only very few data analysts possess this skill. Try and become skilled in this area and watch you beat your competitors to secure data analytics jobs. Though it may not be needed for some analysis, it is always important to possess this skill.

After acquiring the above-highlighted Data analytics skills through data analyst certification, you can use them to attract attention to yourself throughout your job search. To start with, state the names of the skills as keywords when putting your resume together. Do not assume or conclude that the Human Resource Personnel would figure out that you possess the data analytics skills they want.

When you see a job you want to apply for, carefully go through the job description and conduct a couple of research on the company. This way, you would be able to tell what skills the company prioritizes; then you can go ahead and highlight them in your cover letter (provided you possess the skills). In a situation whereby you don't possess all the skills the company need, feel free to mention as many as you possess, including the ones not mentioned but relevant to data analytics jobs.

You may be asked to give instances of situations where your skills turned out very useful, so be prepared to describe at least a scenario in which your skills saved the day.

A first degree is often required, not all the time though. To work as a data analyst, a degree in any of these relevant disciplines may go a long way:

As much as the above degrees are important to your success as a data analyst, what matters most is the data analytics skills you possess and this is why you should look forward to a data analyst certification.

These days, a postgraduate degree in data science is becoming more widespread among data analysts (or intending data analysts). You may find this academic qualification (postgraduate degree) helpful if you are changing career or you want to learn data analysis skills. Some of the subjects in which postgraduate qualifications are offered include MSc Business Analytics, MSc Big Data, MSc Data Science, MSc Data Science and Analytics.

There are also a couple of data analysis skills you can learn through data analyst certification which is available at universities and specialist data schools including:

Data analysis is a rapidly growing field and highly skilled analysts are in increased demand across all sectors. This is evident from the average salary of a data analyst in India.

However, this gives you a compelling reason to take up a data analyst certification.

This implies that you would find many opportunities but you will still have to be outstanding and exhibit excellent data analytics skills to be successful as a data analyst.

Data Analysis maybe a broad concept, a list of skills if acquired will make you excel in the field. Learning them is now easy with a full set of this Data Analytics Using Excel Course.

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Top Data Analytics Skills Required To Become A Data Analyst

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The world has evolved so much that people have moved from being superstitious to being logical. Unlike before, people now correlate events and occurrences rationally to ascertain the cause of problems and proffer possible solutions. Therefore, Data Analytics refers to a method in which data is collected and organized in such a manner that meaningful and helpful information can be derived from it.

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What is a Data Analyst?

A data analyst is someone who uses technical skills to analyze data and report insights.

On a typical day, a data analyst might use SQL skills to pull data from a company database, use programming skills to analyze that data, and then use communication skills to report their results to a larger audience.

It's a fulfilling job that pays well. Being a data analyst also provides experience that can be beneficial for stepping into more advanced roles like data scientist.

How to Become a Data Analyst

1. Learn the technical skills (SQL and some data analysis with Python or R)

2. Learn the fundamentals of statistics

3. Build data analysis projects that showcase your hard and soft skills

So you've decided you want to be a data analyst. Or maybe your goal is to be a data scientist, but you know many entry-level jobs are analyst roles. In either case, you're going to need to master data analyst skills to get you where you want to go.

But what are those skills? What are the things you need to know? In this article, you'll learn the eight key skills you'll need to get a job as a data analyst.

What Skills Does a Data Analyst Need?

We'll be focusing on skills and not on tools (like Python, R, SQL, Excel, Tableau, etc.) Our focus will be what you'll need to do as a data analyst, not how you do those things.

Tools - the how - will vary depending on the exact role, the company that hires you, and the industry you end up working in. You can take the data analyst skills from this article and apply them using the tools that you're learning with, or that suit the industry you're looking to break into.

The research for this article was taken from the planning for our Data Analyst paths. To make sure we teach the right mix of skills, we did a lot of research to understand what data analysts really do.

This research included interviews with data analysts, data scientists, and recruiters/hiring managers for data roles. We also conducted a review of existing research on the topic.

1: Data Cleaning and Preparation

Research shows that data cleaning and preparation accounts for around 80% of the work of data professionals. This makes it perhaps the key skill for anyone serious about getting a job in data.

Commonly, a data analyst will need to retrieve data from one or more sources and prepare the data so it is ready for numerical and categorical analysis. Data cleaning also involves handling missing and inconsistent data that may affect your analysis.

Data cleaning isn't always considered "sexy", but preparing data can actually be a lot of fun when treated as a problem-solving exercise. In any case, it's where most data projects start, so it's a key skill you'll need if you're going to become a data analyst.

2: Data Analysis and Exploration

It might sound funny to list "data analysis" in a list of required data analyst skills. But analysis itself is a specific skill that needs to be mastered.

At its core, data analysis means taking a business question or need and turning it into a data question. Then, you'll need to transform and analyze data to extract an answer to that question.

Another form of data analysis is exploration. Data exploration is looking to find interesting trends or relationships in the data that could bring value to a business.

Exploration might be guided by an original business question, but it also might be relatively unguided. By looking to find patterns and blips in the data, you may stumble across an opportunity for the business to decrease costs or increase growth!

3: Statistical Knowledge

A strong foundation in probability and statistics is an important data analyst skill. This knowledge will help guide your analysis and exploration and help you understand the data that you're working with.

Additionally, understanding stats will help you make sure your analysis is valid and will help you avoid common fallacies and logical errors.

The exact level of statistical knowledge required will vary depending on the demands of your particular role and the data you're working with. For example, if your company relies on probabilistic analysis, you'll need a much more rigorous understanding of those areas than you would otherwise.

4: Creating Data Visualizations

Data visualizations make trends and patterns in data easier to understand. Humans are visual creatures, and most people aren't going to be able to get meaningful insight by looking at a giant spreadsheet of numbers. As a data analyst, you'll need to be able to create plots and charts to help communicate your data and findings visually.

This means creating clean, visually compelling charts that will help others understand the data. It also means avoiding things that are either difficult to interpret (like pie charts) or can be misleading (like manipulating axis values).

Visualizations can also be an important part of data exploration. Sometimes there are things that you can see visually in the data that can hide when you just look at the numbers.

It's very rare to find data role that doesn't require data visualization, making it a key data analyst skill.

5: Creating Dashboards and/or Reports

As a data analyst, you'll need to empower others within your organization to use data to make key decisions. By building dashboards and reports, you'll be giving others access to important data by removing technical barriers.

This might take the form of a simple chart and table with date filters, all the way up to a large dashboard containing hundreds of data points that are interactive and update automatically.

Job requirements can vary a lot from position to position, but almost every data analyst job is going to involve producing reports on your findings and/or building dashboards to showcase them.

6: Writing and Communication Skills

The ability to communicate in multiple formats is a key data analyst skill. Writing, speaking, explaining, listening- strong communication skills across all of these areas will help you succeed.

Communication is key in collaborating with your colleagues. For example, in a kickoff meeting with business stakeholders, careful listening skills are needed to understand the analyses they require. Similarly, during your project, you may need to be able to explain a complex topic to non-technical teammates.

Written communication is also incredibly important - you'll almost certainly need to write up your analysis and recommendations.

Being clear, direct, and easily understood is a skill that will advance your career in data. It may be a "soft" skill, but don't underestimate it - the best analytical skills in the world won't be worth much unless you can explain what they mean and convince your colleagues to act on your findings.

7: Domain Knowledge

Domain knowledge is understanding things that are specific to the particular industry and company that you work for. For example, if you're working for a company with an online store, you might need to understand the nuances of e-commerce. In contrast, if you're analyzing data about mechanical systems, you might need to understand those systems and how they work.

Domain knowledge changes from industry to industry, so you may find yourself needing to research and learn quickly. No matter where you work, if you don't understand what you're analyzing it's going to be difficult to do it effectively, making domain knowledge a key data analyst skill.

This is certainly something that you can learn on the job, but if you know a specific industry or area you'd like to work in, building as much understanding as you can up front will make you a more attractive job applicant and a more effective employee once you do get the job.

8: Problem-Solving

As a data analyst, you're going to run up against problems, bugs, and roadblocks every day. Being able to problem-solve your way out of them is a key skill.

You might need to research a quirk of some software or coding language that you're using. Your company might have resource constraints that force you to be innovative in how you approach a problem. The data you're using might be incomplete. Or you might need to perform some "good enough" analysis to meet a looming deadline.

Whatever the circumstances, strong problem-solving skills are going to be an incredible asset for any data analyst.

Other Data Analyst Skills

The exact definition of "data analyst" varies a lot depending on whom you ask, so it's possible not all of these skills will be required for every data analyst job.

Similarly, there may be skills some companies will require that aren't on this list. Our focus here was to find the set of skills that most data analyst roles require in order to build the very best data analyst learning paths for our students.

Data Analyst Job Breakdown

So far in this article, we've looked at critical skills for data scientists from a broad perspective. Now, let's dig a little deeper into some of the specifics.

If you're looking for a job as a data analyst, what kinds of things will you need on your resume? And how much can you expect to get paid if you get the job? Let's take a look at some of the specifics.

Data Analyst Qualifications

Generally speaking, employers will expect data analysts to have a bachelors degree in something, and a degree in a quantitative/STEM field may help. However, a degree is not required. Data analysts are in high demand, and employers are concerned primarily with an applicant's actual skills - if you have the right skills and the projects to prove it, you can get a data analyst job without a degree.

People often ask whether some kind of data science certificate is required or helpful for getting jobs in data. The answer is no. Employers are primarily concerned with skills, and when we spoke to dozens of people who hire in this field, not a single one of them mentioned wanting to see certificates.

Certificate programs can be helpful if they teach you necessary skills, but employers aren't going to be scanning your resume looking for a data analyst certificate. Nor are they likely to care much about any certificates you've earned. They'll be looking for proof of actual skills.

Data Analyst Requirements

Above, we've talked about the skills data analysts need, and we've explained why you probably don't need any paper qualifications to become a data analyst.

What you do need, though, is proof of the skills you have. Simply listing that you know SQL and Python on your resume is not enough, even if those are the job requirements listed in the job posting. You need to prove you have those skills.

The easiest way to do this is with prior work experience, of course - if an employer can see you've already worked as a data analyst, and call up your old boss for confirmation, you're in good shape.

But if you're reading this article, you probably don't have prior experience in the field. In that case, what you need is a portfolio of data analysis projects that potential employers can peruse. Having an active Github account with relevant projects (and linking to this account from your resume) is probably the quickest and easiest way to set up a portfolio.

The projects you showcase should be your best, and they should demonstrate that you have the skills listed in this article. Use a format like a Jupyter Notebook or R Markdown document to showcase your code along with written explanations and charts that a non-technical hiring manager or recruiter can understand. (Remember, you need to be showcasing your communication skills in addition to the technical skills you used to do the analysis).

The more relevant you can make these projects to the companies where you're applying, the better your chances will be of getting a call back.

Data Analyst Salaries

According to Indeed.com as of April 6, 2021, the average data analyst in the United States earns a salary of $72,945, plus a yearly bonus of $2,500.

Experienced data analysts at top companies can make significantly more, however. Senior data analysts at companies such as Facebook and Target reported salaries of around $130,000 as of April 2021.

Sample Data Analyst Jobs

We looked at some open "Data Analyst" jobs (as of April 2021) and pulled together some lists of the technical and non-technical skills they list. As you can see, what's required does vary a bit from company to company.

That's just a tiny sample of what's available. And of course, we've simplified these job postings, boiling them down to just the most essential listed skills.

Even though there are differences, it's clear the same skills are required for many of these jobs:

Data analysis and data visualization, although the specific tools required vary from job to job). Two other skills we listed above, data cleaning and building dashboards, also fall under these headings.

Communication - every job posting mentioned this.

Statistics - most of the postings mentioned preferring someone with at least some knowledge of statistics, although many didn't list it as a requirement.

Problem solving or analytical thinking skills were also mentioned frequently.

Becoming a Data Analyst, Step by Step

If you're serious about becoming a data analyst and you're starting from scratch, don't worry! You can do this. It helps to take a step-by-step approach.

1. Learn the basics of programming in R or Python. At Dataquest, our data analyst learning paths in Python and R are designed to help you start from scratch, even if you've never written a line of code before.

2. Start building projects. As early as possible, start putting together data projects. These early projects will help you solidify the skills you're learning and keep you motivated. You should keep building projects of increasing difficulty and complexity as you work through the later steps here (Dataquest's learning paths have built-in guided projects to help with this).

3. Learn SQL (and other technical skills). Different data analyst jobs will have different specific requirements, but almost any analyst job will require some SQL skills. We've written a bit about why SQL skills are critical, so don't skip that, but there are other technical skills that can make your life easier, too. At Dataquest, our data analyst learning paths will take you through all of these skills in a logical sequence, so each skill builds on the previous one and you don't have to worry about what to learn next.

4. Share your work and engage with the community. This will help you learn, collaborate, and start building a "personal brand" as a data analyst. Sharing your work can feel intimidating, but you never know what kinds of job offers can come from the right person happening to come across a cool project you've shared.

5. Push your boundaries. Once you've mastered the basics, be sure you keep pushing with your projects so that you're learning new skills. Don't fall into the trap of doing similar projects over and over again because you're comfortable doing them. Try to include at least one thing you've never done before in each new project, or go back to old projects and try to improve them or add complexity.

You Can Become a Data Analyst!

In this article, we've covered what you need to learn to become a data analyst. If you want to learn the how, and build the technical skill set you need to successfully get a data analyst job, check out our interactive online data analysis courses.

Without ever leaving your web browser, you'll write real code to analyze real-world data as you learn interactively using our proven approach.

At Dataquest, our vision is to become the world's first option for learning data skills. In order to achieve that, we craft our curriculum to teach the students the skills students they need to get jobs in data.

Specifically, the data analyst skills we've covered in this article are the basis for our two "data analyst" learning paths:

You can start both paths for free and start your journey to being a data analyst today!

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Data Analyst Skills - 8 Skills You Need to Get a Job

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A data analyst is someone who uses technical skills to analyze data and report insights. On a typical day, a data analyst might use SQL skills to pull data from a company database, use programming skills to analyze that data, and then use communication skills to report their results to a larger audience.

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In recent years, the explosive interest in technologies such as Big Data and Artificial Intelligence has led to a significant increase in the demand for Data Analysts and Data Scientists. With Data Analytics, companies can leverage data to find out what works for them and what does not. By carefully collecting, classifying, and analyzing critical data, organizations are better equipped to set future goals and develop strategies and tactics to achieve them successfully. This implies that Data Analysts who have the skills to uncover insights from data are in high demand.

Upon a complete walkthrough of this article, you will have a decent understanding of the work and importance of a Certified Data Analyst. Furthermore, this article will take you through the skills and knowledge areas in which you should be well equipped to excel in this role. Read along to know more about this in-demand profession.

Table of Contents

Introduction to Data Analytics

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Data Analytics is a broad term that encompasses many different types of Data Analysis. Any kind of information can be subjected to Data Analytics techniques to produce insights that can be used to foster the decision-making process. Data Analytics techniques can uncover trends and patterns that would otherwise be lost in the enormous amount of data. This information can be used to optimize processes and increase the overall efficiency of an organization.

In Data Analytics, you need to analyze the data gathered from various sources, clean it up, and then turn it into information that can be interpreted. The data collected from multiple sources can be Structured, Unstructured, or Semi-Structured in nature. The final result can be visualized through Charts, Graphs, and other tools.

There is a great need for professionals who can help organizations turn raw data into meaningful information that can, in turn, drive business growth. There are various job roles in Data Analytics, and being a certified Data Analyst among them can bring out the most incredible career opportunities.

Introduction to Data Analyst

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Data Analysts are IT professionals who Collect, Process, and Perform Statistical Analysis on large data sets to draw meaningful insights that can be leveraged to foster data-driven decision-making. With the development of computers and an increasing inclination towards technological interdependence, Data Analytics has evolved by many folds. The relational database breathed new life into Data Analysts and enabled them to use SQL to pull data from databases.

Data Analyst: Typical Job Description

Most Data Analytics Jobs involve Collecting and Cleaning data to uncover business insights and trends. Since this job involves analyzing and interpreting data, it is analytical in nature. The day-to-day Data Analyst Job varies with the nature of the Industry or the Company, or the type of Data Analysis you're performing. Data Analysts are responsible for extracting hidden insights and channeling them visually in the form of Dashboards and Reports. Tableau, Google Analytics, Power BI are some of the tools used extensively by Data Analysts.

Most Data Analysts work closely with IT, Management, and Data Science teams to determine the company's goals. They extract and clean data from primary as well as secondary sources, and then analyze and interpret the results using standard statistical tools and techniques. In most cases, they identify trends, relationships, and patterns in complex data sets and identify new opportunities for process improvement. Data Analysts also need to report on their findings and effectively convey them to various teams and all the stakeholders involved.

Skills Required to Become a Certified Data Analyst

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To become a Certified Data Analyst, you need to have a broad and extensive range of skill sets. From domain expertise to various tools, here are all the skills you need to become a Certified Data Analyst:

1) Creative and Analytical Thinking

Curiosity and Creativity are the key qualities one should possess to become a Certified Data Analyst. Having a solid foundation in statistical methodology is important, but looking at problems with a creative and analytical lens is equally important to unravel different layers of a problem.

2) Data Visualization

Data Visualization is one of the most important skills one should possess to become a Certified Data Analyst. A Data Analyst must master the art of representing the data in an easy-to-understand manner. A good Data Analyst knows what types of Visualization Charts to use, how to scale visualizations, depending on the type of audience.

3) Data Warehousing

Sometimes you need to work in tandem with the Data Engineering team in the backend as a Data Analyst to connect databases from multiple sources, create a Data Warehouse, and use Queries to extract and manipulate the data. Hence, to become a Certified Data Analyst, you must be familiar with the key concepts associated with Data Warehousing.

4) Data Cleaning

Data extracted from a database is not always clean and ready for analysis and this is where Data Cleaning comes into the picture. To become a Certified Data Analyst, you must be able to remove corrupt records from a dataset which can hamper your analysis.

5) Mathematics and Statistics

Objective decision-making is a crucial part of problem-solving. To make decisions objectively, you need to rely on Mathematics and Statistics. You need to find patterns, segment them, and make predictions based on historical information. Therefore, you must have a strong foundation of Mathematics and Statistics to come up with the optimized solution to a problem and efficiently perform Data Analysis.

6) SQL Databases

SQL Databases are relational databases with Structured data stored in the form of tables and as a Data Analyst, you must know how to extract information from various tables to perform the analysis.

7) Database Query Languages

The most common Query Language used by Data Analysts is SQL and there are many variations of this language including PSQL, PL / SQL (Procedural Language / SQL). You must be proficient in Query languages to become a Certified Data Analyst.

8) Microsoft Excel

Microsoft Excel is a Spreadsheet program offered by Microsoft which is extensively used for performing Data Analysis. As a Data Analyst, you should be able to work with Microsoft Excel and understand advanced modeling techniques.

9) Machine Learning

Machine Learning is not a mandatory skill to become a Data Analyst but knowledge of Machine Learning is an added advantage when it comes to performing exploratory analysis.

10) Programming Languages

Data Analysts need to be fluent in one language and have some hands-on experience with it. Data Analysts use Programming Languages ​​such as R and Python for Data Collection, Data Cleansing, Statistical Analysis, and Data Visualization.

11) Strong and Effective Communication Skills

Once a Data Analyst is able to uncover hidden insights from a large data set, then the findings need to be communicated to the stakeholders who are involved and this is where Soft skills play a major role. As a Data Analyst, you should be able to communicate effectively and pitch your findings to the prospect teams.

Top 5 Certifications to Become a Certified Data Analyst

Before starting with any of the top Certifications you can opt for, to become a Certified Data Analyst, you need to know the difference between Certifications and Certificates, although they sound the same, there is a major difference between them. A Data Analytics Certification means that you have passed a specific assessment and are ready to work on a given job role as per industry standards whereas a Data Analytics Certificate simply indicates that you have education in a Data Analytics domain, it does not necessarily mean that you possess a certain skill set.

Given below are some of the best Data Analytics Certifications:

1) Microsoft Certified: Data Analyst Associate

Microsoft Certified: Data Analyst Associate Certification is one of the most valued Certifications that can help you in becoming a Certified Data Analyst. It mainly focuses on leveraging the potential of Power BI to optimize the value of a company's data assets. It provides the skills necessary to clean and transform data and to design and build scalable data models. Associate Analysts are proficient in Data Preparation, Data Modeling, Data Visualization, and Data Analysis in the context of Power BI. Ideal candidates for this certification are the candidates who have prior experience working with Power BI.

To know more about the Certification, click here to check out their official website.

2) Google Data Analytics Certificate

If you are a complete beginner and have no prior experience with Data Analytics and looking for a Certification to become a Certified Data Analyst then Google Data Analytics Certification is the best option for you. It mainly focuses on introducing you to the world of Data Analytics through a curriculum tailored by Google. It will help you develop all the mandatory skills required for Data Analytics using tools like SQL, Tableau, R, and more. Clearing the assessments will help you equip yourself with the skills that are needed to apply for an entry-level positionas a Certified Data Analyst.

To know more about the Certification, click here to check out their official website.

3) Cloudera Certified Associate Data Analyst

The Cloudera Certified Associate (CCA) Data Analyst Certification enables data professionals to use Hive and Impala to extract and generate reports on the Cloudera CDH environment.

Individuals who clear the CCA Data Analyst Certification know how to use Query Language Statements in Impala and Hive to analyze data in the cluster. They also become proficient in structuring the data.

To know more about the Certification, click here to check out their official website.

4) Certified Analytics Professional(CAP)

If you have good knowledge and hands-on experience in performing Data Analysis and are looking for an advanced level Certification then Certified Analytics Professional (CAP) is the right choice for you. Data professionals who earn Certified Analytics Professional are familiar with Business Problems, Analytical Problems, and various Analytical Methodologies. Implementation and lifecycle management are additional skills that certified individuals possess.

To know more about the Certification, click here to check out their official website.

5) Springboard Data Analytics Certification

The Springboard Data Analytics Certification is specifically tailored for those who have a knack for Problem Solving and Critical Thinking. This is a completely supervised online course that offers a job guarantee. Hence, this certification demands a prior two years of professional experience from the candidate.

When you are enrolled in this program, you are assigned a mentor who guides you throughout your learning journey. It offers Project-based and assignments and real-life Case Studies to test your learnings of Data Analytics. To complete the program you are assigned a final project which is reviewed by your mentor and upon clearing the assessment you are ready to become a Certified Data Analyst.

To know more about the program, click here to check out their official website

Average Salary of a Certified Data Analyst

Data Analyst is one of the most sought-after career opportunities and one of the most evolved professionals in the Information Technology field. Large amounts of data is generated across companies every day, so there are several job opportunities for these professionals. As of July 2021, the average salary for an entry-level Certified Data Analyst is approximately $78000 per annum in the US whereas the average salary for an entry-level Data Analyst in India is ₹430000 per annum.

It's no wonder Data Analysis skills like Predictive Analytics, Python, SQL, and others are among the highest-paying skills in the world today. The demand for Certified Data Analysts is not only enormous, but it is also increasing every day.

Conclusion

This article explained the various important aspects that you need to consider before pursuing Data Analytics as a career. It discussed the work you will do and the salary that you may expect as a Certified Data Analyst. Also, the article elaborated on the skills that you need to master before you can compete for the role of a Certified Data Analyst in the current scenario.

A huge part of your work as a Certified Data Analyst involves collecting data from multiple sources and integrating the incoming data into the desired form. This cumbersome process can be simplified by using Hevo Data. Hevo Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully automated and secure manner without having to write the code repeatedly. Hevo, with its strong integration with 100+ sources & BI tools, allows you to not only export & load Data but also transform & enrich your Data & make it analysis-ready in a jiff.

Want to take Hevo for a spin? Sign up here for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.

Share with us your experience of learning about how to become a Certified Data Analyst. Tell us in the comments below!

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Becoming a Certified Data Analyst: Top 5 Certifications

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In recent years, the explosive interest in technologies such as Big Data and Artificial Intelligence has led to a significant increase in the demand for Data Analysts and Data Scientists. With Data Analytics, companies can leverage data to find out what works for them and what does not.

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https://www.mastersindatascience.org/careers/data-analyst/

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Here are five steps to consider if you're interested in pursuing a career in data science:

1. Earn a bachelor's degree in a field with an emphasis on statistical and analytical skills, such as math or computer science

2. Learn important data analytics skills

3. Consider certification

4. Get your first entry-level data analyst job

5. Earn a master's degree in data analytics

What Does a Data Analyst Do?

A data analyst collects, processes and performs statistical analyses on large dataset. They discover how data can be used to answer questions and solve problems. With the development of computers and an ever increasing move toward technological intertwinement, data analysis has evolved. The development of the relational database gave a new breath to data analysts, which allowed analysts to use SQL (pronounced "sequel" or "s-q-l") to retrieve data from databases.

Typical Data Analyst Job Description

Most jobs in data analytics involve gathering and cleaning data to uncover trends and business insights. The day-to-day data analyst job varies depending on the industry or company or the type of data analytics you consider your specialty. Data analysts may be responsible for creating dashboards, designing and maintaining relationship databases and systems for different departments throughout their organization using business intelligence software, Tableau, and programming.

Most data analysts work with IT teams, management and/or data scientists to determine organizational goals. They mine and clean data from primary and secondary sources then analyze and interpret results using standard statistical tools and techniques. In most cases, they pinpoint trends, correlations and patterns in complex data sets and identify new opportunities for process improvement. Data analysts must also create reports on their findings and communicate next steps to key stakeholders.

Data Analyst Qualifications

Skills Required for Data Analysts

Programming Languages (R/SAS): data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.

Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst. It's important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that will enhance a company's understanding of the matter at hand.

Strong and Effective Communication: Data analysts must clearly convey their findings - whether it's to an audience of readers or a small team of executives making business decisions. Strong communication is the key to success.

Data Visualization: Effective data visualization takes trial and error. A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience.

Data Warehousing: Some data analysts work on the back-end. They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.

SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables and a data analyst pulls information from different tables to perform analysis.

Database Querying Languages: The most common querying language data analysts use is SQL and many variations of this language exist, including PostreSQL, T-SQL, PL/SQL (Procedural Language/SQL).

Data Mining, Cleaning and Munging: When data isn't neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.

Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.

Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not expected skill of typical data analyst jobs.

Data Analyst Responsibilities

A Day in the Life of a Data Analyst

The day-to-day for a data analyst depends on where they work and what tools they work with. Some data analysts don't use programming languages and prefer statistical software and Excel. Depending on the problems they are trying to solve, some analysts perform regression analysis or create data visualizations. Experienced data analysts are sometimes considered "junior data scientists" or "data scientists in training." In some cases, a data analyst/scientist could be writing queries or addressing standard requests in the morning and building custom solutions or experimenting with relational databases, Hadoop and NoSQL in the afternoon.

"A big part of my job is creating player projections for Fantasy Baseball. These power the default rankings in our draft rooms and inform my preseason and in-season rankings of players. Our readers and customers of our Fantasy product rely on the accuracy of these projections, so it's important to have a sound statistical basis for making them.During the season, we have a high degree of interaction with our audience, as a large part of our responsibility is to respond to questions about player value and performance. Statistical analysis informs these recommendations, whether they are made through social media platforms, written and video content, or podcasts"
-Al Melchior, a Fantasy Sports Data Analyst for CBSSports.com
What tools do data analysts use?

Here are some other important tools data analysts use on the job:

Google Analytics (GA): GA helps analysts gain an understanding of customer data, including trends and areas of customer experience that need improvement on landing pages or calls to action (CTAs)

Tableau: Analysts use Tableau to aggregate and analyze data. They can create and share dashboards with different team members and create visualizations

Jupyter Notebook system: Jupyter notebooks make it simple for data analysts to test code. Non-technical folks prefer the simple design of jupyter notebooks because of its markdown feature

Github: Github is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming

AWS S3: AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large datasets

Data Analyst Job Outlook

Today's data analysts should be prepared for a change. Analyst's roles are increasingly becoming more complex. Experienced analysts use modeling and predictive analytics techniques to generate useful insights and actions. Then they have to explain what they've discovered to rooms of confused laymen. In other words, they have to transform themselves from data analysts into data scientists.

Market research analyst positions are expected to grow by 18% and management analyst positions are all expected to grow by 11%, which is much faster than the average job growth according to recent data from the Bureau of Labor Statistics. Because data analysts can fit in the majority of industries such as finance, healthcare, information, manufacturing, professional services, and retail - the growth of technology brings the growth of more analyst positions. We are collecting data at every turn, its organization, and implication of predictive analysis assists society in becoming a better version of itself.

Data Analyst Salary

Salary numbers are dependent on job responsibilities. A senior data analyst with the skills of a data scientist can command a high price.

Salaries for Data Analysts:

Average salary for entry-level data analysts: $83,750

Average salary for data analysts: $100,250

Average salary for senior data analysts: $118,750-$142,500

Interested in a different career? Learn more about bootcamps

If you're interested in a career change or deepening your understanding of data analytics, you may want to consider a bootcamp. Tech bootcamps are fast-paced training in specific programming languages such as Python, R, or SQL that are offered in a variety of formats. Online coding bootcamps offer intensive learning experiences that mimic the real world and learn how to create projects from scratch.

If you're interested in a career in analytics, attending a data analytics bootcamp may help prepare you for a new job opportunity. Data analytics bootcamps typically cover statistical analysis, analyzing data to uncover insights, using business intelligence software such as Tableau and other various tools data analysts may use on the job.

Another option is enrolling in a data science bootcamp. Data science bootcamps typically cover more advanced analytical concepts as well as machine learning, natural language processing and neural networks. If you're unsure of which bootcamp to enroll in, consider your career goals and what you want to achieve in your existing role or new role.

Bootcamps can last a week to a few months depending on whether you enroll in a part-time or full-time program. You can choose an option that works with your schedule and learning goals. Some bootcamps offer prep courses and workshops to help ensure students' success.

Data Analyst FAQs

What Should I Learn to Become a Data Analyst?

There are a variety of tools data analysts use day to day. Some data analysts use business intelligence software. Others may use programming languages and tools which have various statistical and visualization libraries such as, Python, R, Excel and Tableau. Other skills include:

Creative and analytical thinking

Communication

Database querying

Data mining

Data cleaning

Is Coding Required to be a Data Analyst?

Some data analysts are proficient in programming languages while others may use analytics software or Excel to analyze data and provide insights. Whether or not coding is required for a data analyst typically depends on the job or the employer. Employers may or may not list programming as a required skill for data analysts in job listings. It is important to look at the job description and consider your background before applying.

Is Being a Data Analyst a Good Career?

If you're weighing your options between becoming a data analyst or data scientist, both occupations are projected to grow faster than average. Data analysts have a bright job outlook according to O*NET OnLine (O*NET). The projected growth for data scientists and data analysts is 8% between 2019 and 2029. Data analysts salaries typically depend on where they work and their industry. According to O*NET, data analysts earned an average annual salary of $94,280 in 2019.

Last updated: March 2021
This page includes information from O*NET OnLine by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA.

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Here are five steps to consider if you're interested in pursuing a career in data science: Earn a bachelor's degree in a field with an emphasis on statistical and analytical skills, such as math or computer science Learn important data analytics skills Consider certification Get your first entry-level data analyst job Earn a master's degree in data analytics A data analyst collects, processes and performs statistical analyses on large dataset.

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Based on the documents I have read, I will tell you the skills required to become a data analyst. First of all, it must be an analytical skill. As the name implies, it is clear that data analytics skills are of huge importance in data analysis. Data Analytics is a key skill of a data analyst that enables you to solve problems by making decisions in the most appropriate way. That's why data analysis skills and mindsets are so important. As for data cleaning and preparation, studies show that it accounts for about 80% of the work of data analysts. That's probably also why it's such an important skill for anyone in the field. Next, a strong foundation in probability and statistics is an important data analyst skill. It will help you navigate your data analysis as well as help you avoid common mistakes. To be able to do this job you also need to have technical and computer skills. You need to be familiar with some software and computer tools in terms of languages and programming. Another skill to serve in data analysis for different projects is domain knowledge. Domain knowledge changes from one industry to another, so you may find yourself needing to research and learn quickly. No matter what you're working on you need to understand what you're analyzing, so domain knowledge becomes a really essential skill for a data analyst. Finally, in my opinion, communication skills as well as problem solving are also particularly important skills. As an analyst you face problems every day, you need to be able to solve them, make appropriate inquiries and listen intelligently to others. Good communication skills can also help you advance faster at work. Those are the skills required to become a data analyst as far as I know...

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https://www.tutorialspoint.com/excel_data_analysis/data_analysis_process.htm

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Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making. Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same.

Data Analysis Process consists of the following phases that are iterative in nature −

Data Requirements Specification

Data Collection

Data Processing

Data Cleaning

Data Analysis

Communication

Data Requirements Specification

The data required for analysis is based on a question or an experiment. Based on the requirements of those directing the analysis, the data necessary as inputs to the analysis is identified (e.g., Population of people). Specific variables regarding a population (e.g., Age and Income) may be specified and obtained. Data may be numerical or categorical.

Data Collection

Data Collection is the process of gathering information on targeted variables identified as data requirements. The emphasis is on ensuring accurate and honest collection of data. Data Collection ensures that data gathered is accurate such that the related decisions are valid. Data Collection provides both a baseline to measure and a target to improve.

Data is collected from various sources ranging from organizational databases to the information in web pages. The data thus obtained, may not be structured and may contain irrelevant information. Hence, the collected data is required to be subjected to Data Processing and Data Cleaning.

Data Processing

The data that is collected must be processed or organized for analysis. This includes structuring the data as required for the relevant Analysis Tools. For example, the data might have to be placed into rows and columns in a table within a Spreadsheet or Statistical Application. A Data Model might have to be created.

Data Cleaning

The processed and organized data may be incomplete, contain duplicates, or contain errors. Data Cleaning is the process of preventing and correcting these errors. There are several types of Data Cleaning that depend on the type of data. For example, while cleaning the financial data, certain totals might be compared against reliable published numbers or defined thresholds. Likewise, quantitative data methods can be used for outlier detection that would be subsequently excluded in analysis.

Data Analysis

Data that is processed, organized and cleaned would be ready for the analysis. Various data analysis techniques are available to understand, interpret, and derive conclusions based on the requirements. Data Visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.

Statistical Data Models such as Correlation, Regression Analysis can be used to identify the relations among the data variables. These models that are descriptive of the data are helpful in simplifying analysis and communicate results.

The process might require additional Data Cleaning or additional Data Collection, and hence these activities are iterative in nature.

Communication

The results of the data analysis are to be reported in a format as required by the users to support their decisions and further action. The feedback from the users might result in additional analysis.

The data analysts can choose data visualization techniques, such as tables and charts, which help in communicating the message clearly and efficiently to the users. The analysis tools provide facility to highlight the required information with color codes and formatting in tables and charts.

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Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making. Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data.

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https://www.youtube.com/watch?v=yZvFH7B6gKI

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What Is Data Analytics? - An Introduction (Full Guide)

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https://searchdatamanagement.techtarget.com/definition/data-analytics

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By

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly data analytics is used with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions. It is also used scientists and researchers to verify or disprove scientific models, theories and hypotheses.

As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing ( OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data. The difference is that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category.

Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts. It can also be used to respond quickly to emerging market trends and gain a competitive edge over rivals. The ultimate goal of data analytics, however, is boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that have been processed for real-time analytics. In addition, it can come from a mix of internal systems and external data sources.

Types of data analytics applications

At a high level, data analytics methodologies include exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA aims to find patterns and relationships in data, while CDA applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis.

Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves the analysis of numerical data with quantifiable variables. These variables can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, common phrases, themes and points of view.

At the application level, BI and reporting provide business executives and corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers who worked in IT. Now, more organizations will use self-service BI tools that let executives, business analysts and operational workers run their own ad hoc queries and build reports themselves.

Consultant Claudia Imhoff on analytics-driven organizations

An advanced type of data analytics include data mining, which involves sorting through large data sets to identify trends, patterns and relationships. Another type is called predictive analytics, which seeks to predict customer behavior, equipment failures and other future events. Machine learning can also be used for data analytics, using automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modeling. Big data analytics applies data mining, predictive analytics and machine learning tools. Text mining provides a means of analyzing documents, emails and other text-based content.

Data analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyze withdrawal and spending patterns to prevent fraud and identity theft. E-commerce companies and marketing services providers will use clickstream analysis to identify website visitors who are likely to buy a particular product or service -- based on navigation and page-viewing patterns. Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases. Mobile network operators also examine customer data to forecast churn. This allows mobile companies to take steps to prevent defections to business rivals. To boost customer relationship management efforts, other companies can also engage in CRM analytics to segment customers for marketing campaigns and equip call center workers with up-to-date information about callers.

Inside the data analytics process

Data analytics applications involve more than just analyzing data. Particularly on advanced analytics projects. Much of the required work takes place upfront, in collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results. In addition to data scientists and other data analysts, analytics teams often include data engineers, whose job is to help get data sets ready for analysis.

The analytics process starts with data collection. Data scientists identify the information they need for a particular analytics application, and then work on their own or with data engineers and IT staff to assemble it for use. Data from different source systems may need to be combined via data integration routines, transformed into a common format and loaded into an analytics system, such as a Hadoop cluster, NoSQL database or data warehouse.

In other cases, the collection process may consist of pulling a relevant subset out of a stream of data that flows into, for example, Hadoop. This data is then moved to a separate partition in the system so it can be analyzed without affecting the overall data set.

Once the data that's needed is in place, the next step is to find and fix data quality problems that could affect the accuracy of analytics applications. That includes running data profiling and data cleansing tasks to ensure the information in a data set is consistent and that errors and duplicate entries are eliminated. Additional data preparation work is then done to manipulate and organize the data for the planned analytics use. Data governance policies are then applied to ensure that the data follows corporate standards and is being used properly.

From here, a data scientist builds an analytical model, using predictive modeling tools or other analytics software -- using languages such as Python, Scala, R and SQL. The model is initially run against a partial data set to test its accuracy. Typically, it's then revised and tested again. This process is known as "training" the model until it functions as intended. Finally, the model is run in production mode against the full data set, something that can be done once to address a specific information need or on an ongoing basis as the data is updated.

In some cases, analytics applications can be set to automatically trigger business actions. For example, stock trades by a financial services firm. Otherwise, the last step in the data analytics process is communicating the results generated by analytical models to business executives and other end users. Charts and other infographics can be designed to make findings easier to understand. Data visualizations often are incorporated into BI dashboard applications that display data on a single screen and can be updated in real-time as new information becomes available.

Data analytics vs. data science

As automation grows, data scientists will focus more on business needs, strategic oversight and deep learning. Data analysts who work in business intelligence will focus more on model creation and other routine tasks. In general, data scientists concentrate efforts on producing broad insights, while data analysts focus on answering specific questions. In terms of technical skills, future data scientists will need to focus more on the machine learning operations process, also called MLOps.

Next Steps

Consultant David Loshin explains what big data analytics tools can do for companies

Corporate lawyers increasingly play a role in customer data analytics programs

CIO Celso Mello on why human curiosity is a key to effective data analytics

Continue Reading About data analytics (DA)

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What is Data Analytics? - Definition from WhatIs.com

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By Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly data analytics is used with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions.

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https://www.investopedia.com/terms/d/data-analytics.asp

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What Is Data Analytics?

Data analytics is the science of analyzing raw data to make conclusions about information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Key Takeaways

Understanding Data Analytics

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

Data Analysis Steps

The process involved in data analysis involves several steps:

1. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category.

2. The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.

3. The data must be organized after it's collected so it can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.

4. The data is then cleaned up before analysis. It's scrubbed and checked to ensure that there's no duplication or error and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Types of Data Analytics

Data analytics is broken down into four basic types:

1. Descriptive analytics: This describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?

2. Diagnostic analytics: This focuses more on why something happened. It involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?

3. Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?

4. Prescriptive analytics: This suggests a course of action. We should add an evening shift to the brewery and rent an additional tank to increase output if the likelihood of a hot summer is measured as an average of these five weather models and the average is above 58%,

Data analytics underpins many quality control systems in the financial world, including the ever-popular Six Sigma program. It's nearly impossible to optimize something if you aren't properly measuring it, whether it's your weight or the number of defects per million in a production line.

The sectors that have adopted the use of data analytics include the travel and hospitality industry where turnarounds can be quick. This industry can collect customer data and figure out where problems, if any, lie and how to fix them.

Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information that retailers collect and analyze can help them identify trends, recommend products, and increase profits.

Data Analytics Techniques

Data analysts can use several analytical methods and techniques to process data and extract information. Some of the most popular methods include:

Regression analysis entails analyzing the relationship between dependent variables to determine how a change in one may affect the change in another.

Factor analysis entails taking a large data set and shrinking it into a smaller data set. The goal of this maneuver is to attempt to discover hidden trends that would otherwise have been more difficult to see.

Cohort analysis is the process of breaking a data set into groups of similar data, often into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.

Monte Carlo simulations model the probability of different outcomes happening. They're often used for risk mitigation and loss prevention. These simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.

Time series analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.

Data Analytics Tools

Data analytics has rapidly evolved in technological capabilities in addition to a broad range of mathematical and statistical approaches to crunching numbers. Data analysts have a broad range of software tools to help acquire data, store information, process data, and report findings.

Data analytics has always had loose ties to spreadsheets and Microsoft Excel. Data analysts also often interact with raw programming languages to transform and manipulate databases.

Data analysts also have help when reporting or communicating findings. Both Tableau and Power BI are data visualization and analysis tools used to compile information, perform data analytics, and distribute results via dashboards and reports.

Other tools are also emerging to assist data analysts. SAS is an analytics platform that can assist with data mining. Apache Spark is an open-source platform useful for processing large sets of data. Data analysts have a broad range of technological capabilities to further enhance the value they deliver to their company.

The Role of Data Analytics

Data analytics can enhance operations, efficiency, and performance in numerous industries by shining a spotlight on patterns. Implementing these techniques can give companies and businesses a competitive edge. The process includes four basic steps of analysis.

Data Mining

As the name suggests, this step involves "mining" or gathering data and information from across a broad spectrum of sources. Various forms of information are then recreated into the same format so they can eventually be analyzed. The process can take a good bit of time, more than any other step.

Data Management

Data requires a database to contain, manage, and provide access to the information gathered through mining. The next step in data analytics is therefore the creation of such a database to manage the information. SQL was a common tool used for this purpose in the early days of data analytics and it's still widely used in 2023. Created in 1979, this computing language allows relational databases to be queried and the resulting data sets to be more easily analyzed.

Statistical Analysis

The third step is statistical analysis. It involves the interpretation of the gathered and stored data into models that will hopefully reveal trends that can be used to interpret future data. This is achieved through open-source programming languages such as Python. More specific tools for data analytics, like R, can be used for statistical analysis or graphical modeling.

Data Presentation

The results of the data analytics process are meant to be shared. The final step is formatting the data so it's accessible to and understandable by others, particularly those individuals within a company who are responsible for growth, analysis, efficiency, and operations. Having access can be beneficial to shareholders as well.

Importance and Uses of Data Analytics

Data analytics provide a critical component of a business's probability of success. Gathering, sorting, analyzing, and presenting information can significantly enhance and benefit society, particularly in fields such as healthcare and crime prevention. But the uses of data analytics can be equally beneficial for small enterprises and startups that are looking for an edge over the business next door, albeit on a smaller scale,

Why Is Data Analytics Important?

Implementing data analytics into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions.

What Are the 4 Types of Data Analytics?

Data analytics is broken down into four basic types. Descriptive analytics describes what has happened over a given period. Diagnostic analytics focuses more on why something happened. Predictive analytics moves to what is likely going to happen in the near term. Finally, prescriptive analytics suggests a course of action.

Who Is Using Data Analytics?

Data analytics has been adopted by several sectors where turnarounds can be quick, such as the travel and hospitality industry. Healthcare is another sector that combines the use of high volumes of structured and unstructured data, and data analytics can help in making quick decisions. The retail industry also uses large amounts of data to meet the ever-changing demands of shoppers.

The Bottom Line

Data analytics helps individuals and organizations make sure of their data in a world that's increasingly becoming reliant on information and gathering statistics. A set of raw numbers can be transformed using a variety of tools and techniques, resulting in informative, educational insights that drive decision-making and thoughtful management.

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Data Analytics: What It Is, How It's Used, and 4 Basic Techniques

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Data analytics is the science of analyzing raw data in order to make conclusions about that information. It helps businesses perform more efficiently and maximize profit.

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https://www.tibco.com/reference-center/what-is-data-analytics

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Whitepaper: The Spotfire A(X) Experience: The Future of Analytics is Now

Orchestrate Analytics with an Agile Machine Learning Architecture

Top 3 Ways to Use Data Science

11 Analytics Use Cases for Getting the Most From Smart Grid Data

Actionable Marketing Analytics using Spotfire

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Whitepaper: The Spotfire A(X) Experience: The Future of Analytics is Now Orchestrate Analytics with an Agile Machine Learning Architecture Top 3 Ways to Use Data Science 11 Analytics Use Cases for Getting the Most From Smart Grid Data Actionable Marketing Analytics using Spotfire

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https://www.mastersindatascience.org/learning/what-is-data-analytics/

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Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed

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Data Analytics is the process of analyzing raw data and draw conclusions about the information they contain

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Data Analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making

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1. What is Data Analytics ?
2. What are data analytics used for/What is the role of data analytics?
3. Type of data analytics

+) Descriptive analytics
+) Diagnostic analytics
+) Predictive analytics
+) Prescriptive analytics

4.  Data analysis process
+) Define the questions
+) Collect the data
+) Clean the data
+) Analyze the data
+) Interpret and share the result
5. The skill needed to become a data analyst
+) Analytical Skills
+)Data Cleaning and Preparation
+) Foundation in probability and statistics
+) Technical and Computer Skills
+) Domain Knowledge
+) Communication and problem solving skills

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at how data analytics is used so what does data  analytics actually look like in action and how is  it used in the real world so the fact is data is  everywhere so it actually has an infinite amount  of uses across all kinds of businesses and  organizations globally broadly speaking data analytics is used to make faster and better  business decisions to reduce overall business  costs and to develop new and innovative products  and services in more specific terms data analytics might be used to do the following to predict  future sales or purchasing behaviors for security
purposes to help and protect against fraud to  analyze the effectiveness of marketing campaigns to boost customer acquisition and retention or  to increase supply chain efficiency so that gives .you a little bit of an overview of what data  analytics can be used for in the real world

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at how data analytics is used so what does data  analytics actually look like in action and how is .It used in the real world so the fact is data is  everywhere so it actually has an infinite amount

of uses across all kinds of businesses and  organizations globally broadly speaking data

analytics is used to make faster and better  business decisions to reduce overall business

costs and to develop new and innovative products  and services in more specific terms data analytics

might be used to do the following to predict  future sales or purchasing behaviors for security

purposes to help and protect against fraud to  analyze the effectiveness of marketing campaigns

to boost customer acquisition and retention or  to increase supply chain efficiency so that gives

you a little bit of an overview of what data  analytics can be used for in the real world

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https://www.rasmussen.edu/degrees/technology/blog/what-does-a-data-analyst-do/

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What Does a Data Analyst Do? Exploring the Day-to-Day of This Tech Career

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Jobs for data analysts are on the rise. But what does this position actually entail? Join us as we take a behind-the-scenes look at this up-and-coming tech career.

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https://www.northeastern.edu/graduate/blog/what-does-a-data-analyst-do/

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What Does a Data Analyst Do: Responsibilities, Skills, and Salary

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There will be more than 2.7 million job openings in data analytics by 2020. Learn more about the role of a data analyst and the skills employers need to fill these jobs.

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What Does a Data Analyst Do?

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Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it's important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we'll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we'll also use examples and highlight a few tools to make the journey easier. When you're done, you'll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we'll take you through:

On popular request, we've also developed a video based on this article. Scroll down to watch that.

Ready? Let's get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the 'problem statement'.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization's senior management might pose an issue, such as: "Why are we losing customers?" It's possible, though, that this doesn't get to the core of the problem. A data analyst's job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let's say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, "Why are we losing customers?" but, "Which factors are negatively impacting the customer experience?" or better yet: "How can we boost customer retention while minimizing costs?"

Now you've defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you've noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don't come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you'll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and Dasheroo. However, you'll also find open-source software like Grafana, Freeboard, and Dashbuilder. These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you've established your objective, you'll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let's explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company's customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data.

Tools to help you collect data

Once you've devised a data strategy (i.e. you've identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you'll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP, SAS, and the data integration platform, Xplenty. If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm.

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics.

3. Step three: Cleaning the data

Once you've collected your data, the next step is to get it ready for analysis. This means cleaning, or 'scrubbing' it, and is crucial in making sure that you're working with high-quality data. Key data cleaning tasks include:

Removing major errors, duplicates, and outliers-all of which are inevitable problems when aggregating data from numerous sources.

Removing unwanted data points-extracting irrelevant observations that have no bearing on your intended analysis.

Bringing structure to your data-general 'housekeeping', i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.

Filling in major gaps-as you're tidying up, you might notice that important data are missing. Once you've identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one...so don't rush it! You'll find a step-by-step guide to data cleaning here.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let's use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning's clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually-especially large ones-can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine, are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder, which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you've cleaned your data. Now comes the fun bit-analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you're hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened. It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let's refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they'll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened. It is literally the diagnosis of a problem, just as a doctor uses a patient's symptoms to diagnose a disease. Remember TopNotch Learning's business problem? 'Which factors are negatively impacting the customer experience?' A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let's imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they're losing customers because they lack expertise in this sector. And that's a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data. In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn't stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they'll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It's also the most complex. This is because it incorporates aspects of all the other analyses we've described. A great example of prescriptive analytics is the algorithms that guide Google's self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.
Learn more: What are the different types of data analysis?
5. Step five: Sharing your results

You've finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization's stakeholders!) This is more complex than simply sharing the raw results of your work-it involves interpreting the outcomes, and presenting them in a manner that's digestible for all types of audiences. Since you'll often present information to decision-makers, it's very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That's why it's very important to provide all the evidence that you've gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it's important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts, Tableau, Datawrapper, and Infogram. If you're familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly, Seaborn, and Matplotlib. Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial. We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it's really like to work as a data analyst!

6. Step six: Embrace your failures

The last 'step' in the data analytics process is to embrace your failures. The path we've described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you'd never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don't be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What's important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn't be as interesting. Use the steps we've outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

7. Summary

In this post, we've covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst's work:

Define the question-What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.

Collect data-Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?

Clean the data-Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don't rush...take your time!

Analyze the data-Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.

Share your results-How best can you share your insights and recommendations? A combination of visualization tools and communication is key.

Embrace your mistakes-Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we've described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course. You might also be interested in the following:

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Contributor to the CareerFoundry blog

A British-born writer based in Berlin, Will has spent the last 10 years writing about education and technology, and the intersection between the two. He has a borderline fanatical interest in STEM, and has been published in TES, the Daily Telegraph, SecEd magazine and more. His fiction has been short- and longlisted for over a dozen awards.

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A Step-by-Step Guide to the Data Analysis Process [2021]

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Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it's important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.