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Introduction to Python for Business and Finance

This course is a practical introduction to Python, built around a single goal: taking you from writing your very first line of code to working confidently with real business and financial data, cleaning it, analysing it, and presenting it clearly. Rather than treating programming as an abstract subject, each session builds directly on the last, so that by the end, Python feels like a natural tool for solving genuine business problems rather than a separate skill learned in isolation.

The course is organised into four sessions:

  • Session 1: Foundations of Python — what coding is, the core properties that define Python, and the first practical building blocks of the language: running code in Jupyter Notebooks, printing output, storing variables, performing calculations, writing comments, using loops, and organising code into functions.
  • Session 2: Working with Python Data — Python's core data types (numbers, strings, lists, dictionaries), then NumPy for fast numerical computing, and pandas for working with structured, tabular data.
  • Session 3: Empirical Data Analysis — a complete, realistic data workflow: building and importing business datasets, cleaning and preparing them, deriving meaningful new variables, exploring them statistically, and merging multiple sources together.
  • Session 4: Visualising Business Data — turning data into insight using Matplotlib, choosing the right chart for the question at hand, and visualising trends, group comparisons, and relationships within a business dataset.

Each session deliberately builds on the one before it. Session 1 gives you the vocabulary and tools to write and run Python at all. Session 2 introduces the data structures every later analysis depends on. Session 3 shows how those structures come together in a genuine, end-to-end business workflow. And Session 4 focuses on communicating what that analysis actually found, since even the most careful analysis has limited value if its results can't be understood at a glance.

Throughout the course, the emphasis is less on memorising individual commands and more on building the habits of a careful analyst: checking your data rather than assuming it's correct, documenting your reasoning as you go, and choosing the right tool, and the right chart, for the question you're actually trying to answer.