Data Analytics vs. Data Science: What's the Difference?

The short answer: data analytics examines existing data to answer specific business questions about what has already happened, while data science builds predictive models, using statistics, programming and machine learning, to forecast what is likely to happen or to automate decisions. The two overlap heavily, and most beginners start with analytics.

Side by side

• Main question. Analytics asks "what happened, and why?" Data science asks "what is likely to happen next, and what should we do?"
• Typical work. Analytics: cleaning data, building reports and dashboards, spotting trends. Data science: building and testing predictive models, working with larger or messier datasets, automating decisions.
• Common tools. Analytics: Excel, SQL and visualisation tools such as Power BI or Tableau. Data science: Python or R, statistics libraries, machine-learning frameworks and big-data tools.
• Maths and programming. Analytics needs solid spreadsheet and SQL skills and basic statistics. Data science needs stronger statistics and real programming ability.
• Typical output. Analytics: a report, dashboard or recommendation for a decision-maker. Data science: a model or system that produces predictions.

A simple example

Imagine a shop with a year of sales records. An analyst would summarise which products sold best each month and present the pattern in a chart. A data scientist might build a model that predicts next month's demand per product so the shop can plan stock. The first uses the data to explain the past; the second uses it to anticipate the future.

Which should you learn first?

Most people benefit from learning analytics fundamentals before the programming and machine learning that data science needs:

1. Spreadsheets. Get strong at formulas, lookups, PivotTables and charts.
2. SQL. Learn to pull and combine data from databases.
3. Basic statistics. Averages, spread, correlation and what a sample can and cannot tell you.
4. A visualisation tool. Turn results into something a decision-maker can read in seconds.
5. Programming and machine learning. Add Python or R once the earlier steps feel natural.

Courses that follow this path at APTECH Abeokuta

• Advanced Excel 2019: one month, intermediate level, covering lookup and logical formulas, PivotTables, data validation, conditional formatting and dashboards.
• Data Management with SQL Server 2016: four months, covering database design, SELECT queries with joins and aggregates, stored procedures and basic administration.
• Smart Pro (ACNPRO): a 146-hour shared foundation in financial data analysis with Excel, Python and R programming and large data management, followed by a 200-hour specialisation in Data Science, AI & Machine Learning, or Software Testing. The Data Science track includes big-data systems, Hadoop, Hive, Pig Latin and Tableau.

If you are still deciding how long a programme to commit to, read Short Course or Diploma?. For the broader skills that help in any of these roles, see IT skills students should learn before graduating.

Further reading

• Microsoft Learn: Power BI documentation
• The Python Tutorial (official documentation)