What Is Data Analytics? Key Concepts, Skills, and Careers

Written by Coursera Staff • Updated on

Learn what data analytics is, how it’s used in business, skills needed to become a data analyst, and the salary you can expect in this fast-growing industry.

[Featured image] Two coworkers sit at a desk and analyze data on a computer screen. On the wall behind them is a large screen presenting more data sequences.

Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. 

Data analytics is often confused with data analysis. While these are related terms, they aren’t exactly the same. In fact, data analysis is a subcategory of data analytics that deals specifically with extracting meaning from data. Data analytics, as a whole, includes processes beyond analysis, including data science (using data to theorize and forecast) and data engineering (building data systems).

How data analytics is used

Data is everywhere; people use data every day, whether they realize it or not. Daily tasks, such as measuring coffee beans to brew your morning cup, checking the weather report before deciding what to wear, or tracking your steps throughout the day with a fitness tracker, can all be forms of analyzing and using data.

Data analytics is important across many industries, as many business leaders use data to make informed decisions. A sneaker manufacturer might look at sales data to determine which designs to continue and which to retire; or a health care administrator may look at inventory data to determine the medical supplies they should order. At Coursera, we may look at enrollment data to determine what kind of courses to add to our offerings.

Organizations that use data to drive business strategies often find that they are more confident, proactive, and financially savvy.

Data analytics: Key concepts

You may work with four key types of data analytics—descriptive, diagnostic, predictive, and prescriptive. Together, these four types of data analytics can help an organization make data-driven decisions. At a glance, each of them tells us the following: 

  • Descriptive analytics tell us what happened.

  • Diagnostic analytics tell us why something happened.

  • Predictive analytics tell us what will likely happen in the future.

  • Prescriptive analytics tell us how to act.

People who work with data analytics will typically explore each of these four areas using the data analysis process, which includes identifying the question, collecting raw data, cleaning data, analyzing data, and interpreting the results.

Data analytics skills

Data analytics requires a wide range of skills to be performed effectively. Coursera's 2022 Global Skills Report outlines seven key skills for data professionals:

  • Data management: Everything related to managing and accessing data for reporting, analysis, and model building

  • Data visualization: Creation and study of visual representations of data, such as charts and graphs, to tell a story with data

  • Machine learning: A branch of artificial intelligence that involves using algorithms to perform computational tasks without explicit instructions

  • Math: The study of numbers and their relationships (includes skills like linear algebra and calculus)

  • Statistical programming: Programming languages, like R and Python, used to create statistical models and algorithms

  • Statistics: The process of collecting, organizing, analyzing, interpreting, and presenting data and data trends

  • Data analysis: The process of inspecting, cleaning, transforming, and modeling data to discover useful information to drive decision making

While careers in data analytics require a certain amount of technical knowledge, approaching the above skills methodically—for example by learning a little bit each day or learning from your mistakes—can help lead to advanced knowledge; it’s never too late to get started. 

Data analytics careers

Typically, data analytics professionals make higher-than-average salaries and are in high demand within the labour market. According to the Information and Communications Technology Council’s eTalent, the average annual salary for a data analyst in Canada in 2022 is $68,001 [1]. 

The Information and Communications Technology Council (ICTC) reports that organizations are struggling to recruit and properly train data scientists and analytics professionals, which are currently employed in more than 33,000 occupations across the country [2]. According to Aston University, Canada will have 18,000 new data analyst job openings through 2028, but only 16,700 new job seekers [3]. 

Entry-level careers in data analytics include roles such as:

  • Junior data analyst

  • Associate data analyst

  • Junior data scientist

As you gain more experience in the field, you may qualify for mid- to upper-level roles like:

  • Data analyst

  • Data scientist

  • Data architect

  • Data engineer

  • Business analyst

  • Marketing analyst

Get started in data analytics

If you want to keep learning about data analytics, consider the Google Data Analytics Professional Certificate. This series of eight courses is designed to get you job-ready for an entry-level position in data analytics in approximately six months. You’ll learn key skills, like data cleaning and visualization, and you’ll get hands-on experience with common data analytics tools through video instruction and an applied learning project. Or take the next step in your data career with the Google Advanced Data Analytics Professional Certificate, where you'll get hands-on experience will in-demand skills like statistical analysis, Python, regression models, and machine learning.

Article sources


Glassdoor. “Data Analyst Salaries in Canada, https://www.glassdoor.ca/Salaries/data-analyst-salary-SRCH_KO0,12.htm.” Accessed November 13, 2022.

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