Data Analytics
Turn raw data into decisions — Excel, SQL, Python and dashboards, built for beginners.
Why learn data analytics?
Almost every company now runs on data, and someone has to make sense of it. Data analytics is one of the most accessible ways into that world — practical, in demand, and a natural entry point even if you've never coded before.
- Beginner-friendly — you start in Excel and SQL before any programming.
- Immediately useful — the skills apply to almost any industry: retail, finance, healthcare, marketing.
- A stepping stone — a natural path toward our Data Science course if you want to go further.
- Real job demand — Data Analyst is consistently one of the most-hired entry-level data roles.
Data analytics syllabus
Six modules, from spreadsheets to your first dashboard.
Module 01
Data analytics foundations
- What a data analyst actually does
- The data lifecycle: collect, clean, analyse, report
Module 02
Excel for analytics
- Core formulas and functions
- Pivot tables
- Building a simple dashboard
Module 03
SQL for analytics
- Querying and filtering real datasets
- Joins and aggregations
- Writing reporting queries
Module 04
Python for data
- NumPy basics
- Pandas: loading, cleaning and exploring data
Module 05
Data visualisation
- Charting with Matplotlib / Seaborn
- Principles of a clear dashboard
- Intro to BI tools (Power BI / Tableau concepts)
Module 06
Capstone project
- Taking a real dataset from raw data to a finished dashboard
What's next?
Once you're comfortable with analytics, Data Science is the natural next step — it builds directly on the Python and SQL foundations from this course.