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).
Data is everywhere, and people use data every day, whether they realize it or not. Daily tasks such as measuring coffee beans to make 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.
There are 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.
Read more: What Is Data Analysis? (With Examples)
Data analytics requires a wide range of skills to be performed effectively. According to search and enrollment data among Coursera’s community of 87 million global learners, these are the top in-demand data science skills, as of December 2021:
Structured Query Language (SQL), a programming language commonly used for databases
Statistical programming languages, such as R and Python, commonly used to create advanced data analysis programs
Machine learning, a branch of artificial intelligence that involves using algorithms to spot data patterns
Probability and statistics, in order to better analyze and interpret data trends
Data management, or the practices around collecting, organizing and storing data
Statistical visualization, or the ability to use charts and graphs to tell a story with data
Econometrics, or the ability to use data trends to create mathematical models that forecast future trends based
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 mastery, and it’s never too late to get started.
Read more: Is Data Analytics Hard? Tips for Rising to the Challenge
Typically, data analytics professionals make a higher than average salary and are in high demand within the labor market. According to 2017 research by CrowdFlower, for example, there are more open roles for data analysts than people with the skills to perform those jobs, a trend that ensures data analytics professionals are much sought after by employers . More recently, the US Bureau of Labor Statistics (BLS) has projected that careers in data analytics fields will grow by 23 percent between 2021 and 2031—much faster than average – and are estimated to pay a higher than average annual income of $82,360 .
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:
Click through the links above to learn more about each career path, including what the roles entail as well as average salary and job growth.
Read more: How Much Do Data Analysts Make? Salary Guide
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 get hands-on experience with common data analytics tools through video instruction and an applied learning project.
This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.
1,487,597 already enrolled
Average time: 6 month(s)
Learn at your own pace
Skills you'll build:
Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study
CrowdFlower. "2017 Data Scientist Report, https://visit.figure-eight.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport.pdf." Accessed November 3, 2022.
US Bureau of Labor Statistics. "Occupational Outlook Handbook: Operations Research Analysts, https://www.bls.gov/ooh/math/operations-research-analysts.htm." Accessed November 3, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.