Data Analytics

Learn to turn a business question into a recommendation you can defend, using Python, clear charts and real analytical habits.

Welcome! I'm Aayush, your facilitator for this training programme.

We will explore and explain data from five areas of a business: retail and e-commerce, finance, operations, marketing and HR. Along the way you will also practise on sports, technology, government and other datasets.

This site is still being built

Right now you will find the Foundations section, the course roadmap and the first Python lessons. More lessons and cases are on their way, and the Tableau section is in draft; its steps have not yet been tried end to end.

The business cases are previews. They are not ready-to-run assignments yet.

Course at a glance

  • In-person, hands-on classes. Each session has a lecture followed by a lab. Bring your laptop.
  • Spreadsheet basics are enough. No coding experience is assumed. Session 1 starts from the very beginning.
  • Python in your browser. You run every lesson in Google Colab: nothing to install, and every result on these pages comes from the same code you run.
  • Synthetic and public data only. Never private workplace data.
  • Portfolio and briefing. You finish with a practical portfolio and an executive briefing grounded in checked evidence.
  • Tableau after Python. A separate Tableau section shows how to build the same analyses as interactive dashboards.

Where to start

Work through these pages in order:

  1. Foundations: why data analytics and clear presentation matter, with real examples.
  2. Learning roadmap: what each stage of the course covers.
  3. Setup and practice: getting started with Google Colab and keeping notes.
  4. Analytical workflow: the seven steps we use in every analysis.
  5. Business cases: the five business problems you will solve.
  6. Session 1 lecture: why data analytics matters, then its lab on Python and Colab basics.

How we will practise

Every topic follows the same rhythm:

  1. Watch a short demonstration.
  2. Run the working code yourself, step by step.
  3. Change a few lines to answer a related question on your own.
  4. Debug a deliberate mistake.
  5. Recommend an action to a stakeholder.

One habit to build from day one

Before you trust a chart, check three things: what each row means, how the metric is defined, and what evidence supports the number.

You will never be asked to write programs from scratch. Each lesson gives you working code to read, run and then modify. Instructor solutions are shared separately after each practice round.

Connect with Aayush on LinkedIn