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Please read this article to get a brief understanding of the concept of data science.

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Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The Data Science Life Cycle
The image represents the five stages of the data science life cycle: Capture, (data acquisition, data entry, signal reception, data extraction); Maintain (data warehousing, data cleansing, data staging, data processing, data architecture); Process (data mining, clustering/classification, data modeling, data summarization); Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis); Communicate (data reporting, data visualization, business intelligence, decision making).
The term "data scientist" was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. In a 2009 McKinsey&Company article, Hal Varian, Google's chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technology's influence and reconfiguration of different industries.

"The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that's going to be a hugely important skill in the next decades."
- Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

What Does a Data Scientist Do?

In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

They must also be able to utilize key technical tools and skills, including:

R

Python

Apache Hadoop

MapReduce

Apache Spark

NoSQL databases

Cloud computing

D3

Apache Pig

Tableau

iPython notebooks

GitHub

Why Become a Data Scientist?

Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.

The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies.

The statistics listed below represent the significant and growing demand for data scientists.

28%

Demand Increase by 2020

4,524

Number of Job Openings

$120,931

Average Base Salary

#1

Best Job in America 2016, 2017, 2018

Where Do You Fit in Data Science?

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

Data Scientist

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Career Outlook and Salary Opportunities

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.

Below are the average base salaries for the following positions:

Data analyst: $65,470

Data scientist: $120,931

Senior data scientist: $141,257

Data engineer: $137,776

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.

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Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills.

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Data Science is an interdisciplinary field where individuals take data, understand it, communicate it, and analyze it to find solutions, specifically business solutions.

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Data Scientists are people who may work for a company and are hired to find solutions using data that is collected to solve a company's problem or improve an already set initiative. For example, a data scientists may collect data and analyze it to see how sales could go up for a company, or how to better attract consumers to the product(s).

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Research is constantly being conducted and publish. However, if the data found is never analyzed and/or used to our benefit, then what is the point of collecting data?
Lesson Highlight: Data Science allows us to interpret data and use it to our benefit to find real-world solutions.

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Data Scientists find the __________?
Think about it:
-Help a company
-Communicate to collect data
-Analyze data
-?

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Data Scientists go through many steps when collecting data in order to find a proper solution.

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1. Understand the business problem
-Ask lots of questions and gather a lot of information
2. Data acquisition
-Find the right data
3. Data preparation
-Data cleaning and data transformation (most time-consuming process0
4. Exploratory data analysis
- Defines and refines data for model
5. Create a data model
-Identify the model that best fits business problem
6. Visualization and communication
-Discuss with business representatives
7. Develop and maintain
- final step to find solution

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Data Science In 5 Minutes | Data Science For Beginners | What Is Data Science? | Simplilearn