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

Data analytics is the collection, transformation, and organisation 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 the same. 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 theorise and forecast) and data engineering (building data systems).

How data analytics is used

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

Data analytics is important across many industries, as many business leaders use data to make informed decisions. A trainer manufacturer might look at sales data to determine which designs to continue and which to retire, or a healthcare 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 courses to add to our offerings.

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

Data analytics: Key concepts

There are four key types of data analytics: descriptive, diagnostic, predictive, and prescriptive.  These four types of data analytics can help an organisation 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, analysing data, and interpreting the results.

Data analytics skills

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:

• Structured Query Language (SQL), a programming language commonly used for databases

• Statistical programming languages, such as R and Python, are 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, to better analyse and interpret data trends

• Data management, or the practices around collecting, organising, and storing data

• Statistical visualisation, 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. 

Data analytics careers

Typically, data analytics professionals make higher-than-average salaries and are in high demand within the labour market across all sectors. The global big data analytics market is expected to grow at a rate of 13 percent CAGR over the next decade, increasing from 348.21 billion USD in 2024 to 924.39 billion USD in 2032, indicating an increased need for data analytics professionals. Data analysts average £35,000 per year, with entry-level positions starting around £28,000 and experienced workers earning up to £55,000 per year [2].

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

Continue learning

Consider the Google Data Analytics Professional Certificate to keep learning about data analytics. 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 visualisation and get hands-on experience with common data analytics tools through video instruction and an applied learning project.