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• Software Developer: Creating software, writing code in various programming languages (Java, Python, C++, JavaScript, etc.).

• Web Developer: Developing web applications and websites, working with HTML, CSS, JavaScript, as well as server-side languages (PHP, Ruby, Python).

• Mobile Developer: Creating applications for mobile devices (iOS, Android).

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• System Administrator: Managing and supporting servers, network equipment, and operating systems.

• Network Administrator: Setting up and maintaining computer networks, ensuring their security and performance.

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Information security (InfoSec) refers to the practice of protecting information by preventing unauthorized access, use, disclosure, disruption, modification, or destruction. It encompasses various strategies and tools designed to safeguard the confidentiality, integrity, and availability of data, particularly in digital formats.

Key Principles of Information Security:

1. Confidentiality: Ensuring that information is accessible only to those who are authorized to access it. This prevents sensitive data from being exposed to unauthorized individuals.

2. Integrity: Protecting information from being altered in unauthorized ways, ensuring that data remains accurate and trustworthy over its lifecycle.

3. Availability: Ensuring that authorized users have reliable access to information and associated assets when needed.

Main Components of Information Security:

- Risk Management: Identifying, assessing, and mitigating risks to the organization’s data and systems.

- Threats: These include malware, phishing, hacking, insider threats, and physical breaches.

- Vulnerabilities: Weaknesses in systems, processes, or technology that could be exploited to compromise security.

- Policies and Procedures: Implementing and enforcing rules to safeguard information. This includes access control, encryption, data backup, and incident response plans.

Common Information Security Measures:

- Firewalls: Devices or software that monitor and control incoming and outgoing network traffic to protect against unauthorized access.

- Encryption: Scrambling data into unreadable formats that can only be decrypted by authorized users with the correct key.

- Multi-Factor Authentication (MFA): Adding extra layers of security, such as requiring a password and a code sent to a phone.

- Antivirus and Anti-Malware: Programs designed to detect and remove harmful software.

- Security Audits: Regularly reviewing and testing systems for vulnerabilities and ensuring compliance with security standards.

Importance of Information Security:

- Protects Privacy: Ensures personal, financial, and sensitive data is secure from breaches.

- Prevents Data Breaches: Safeguards companies and individuals from the damage caused by data theft.

- Regulatory Compliance: Many industries are required by law (e.g., GDPR, HIPAA) to implement proper security measures to protect data.

- Reputation Management: Helps maintain trust with customers and stakeholders by preventing public security incidents.

Overall, information security is crucial for protecting data in today’s highly digital world and preventing significant financial, legal, and reputational harm

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• Data Analyst: Collecting, processing, and analyzing data for business decision-making.

• Big Data Specialist: Working with large volumes of data, developing solutions for storage and analysis.

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"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we're living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2020 listed data analysts and scientists as the top emerging job, followed immediately by AI and machine learning specialists, and big data specialists [ 1]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let's take a closer look at each.

Collect the raw data sets you'll need to help you answer the identified question. Data collection might come from internal sources, like a company's client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Watch this video to hear what data analysis how Kevin, Director of Data Analytics at Google, defines data analysis.

Kevin, Director of Data Analytics at Google, defines what data analysis is and why it's important.

Learn more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we'll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

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Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.

Descriptive analysis answers the question, "what happened?"

Diagnostic analysis

If the descriptive analysis determines the "what," diagnostic analysis determines the "why." Let's say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent-the "why"-led to the influx of patients.

Diagnostic analysis answers the question, "why did it happen?"

Predictive analysis

So far, we've looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, "what might happen in the future?"

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.

Prescriptive analysis answers the question, "what should we do about it?"

This last type is where the concept of data-driven decision-making comes into play.

Read more: Advanced Analytics: Definition, Benefits, and Use Cases

Frequently asked questions (FAQ)

What are the top skills for a data analyst?‎

What is a data analyst job salary?‎

Do data analysts need to be good at math?‎

Get started with Coursera

If you're interested in a career in the high-growth field of data analytics, you can begin building job-ready skills with the Google Data Analytics Professional Certificate. Prepare yourself for an entry-level job as you learn from Google employees - no experience or degree required. Once you finish, you can apply directly with more than 130 US employers (including Google).

Average time: 6 month(s)

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Related articles

Article sources

1. World Economic Forum. " The Future of Jobs Report 2020, https://www.weforum.org/reports/the-future-of-jobs-report-2020." Accessed July 28, 2022.

2. McKinsey & Company. " Five facts: How customer analytics boosts corporate performance, https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed July 28, 2022.

3. Glassdoor. " Data Analyst Salaries, https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed July 28 2022.

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"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia. This idea lies at the root of data analysis.

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• Machine Learning Engineer: Developing algorithms and models that allow systems to learn from data.

• AI Specialist: Creating intelligent systems capable of performing tasks that require human intelligence.

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AI, Machine Learning, Deep Learning and Generative AI Explained

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• Game Developer: Creating video games, working with game engines (Unity, Unreal Engine).

• Game Designer: Developing concepts, mechanics, and designs for games.