Module 1 · Unit 3 · 9 tutorials · Week 3
The Data Science Environment
Python, Jupyter and the core data science libraries. The toolchain you will use for the rest of the course and beyond.
Overview
This unit installs and introduces the toolchain. Whether or not you have written Python before, you will review the syntax you need and set up Jupyter notebooks, then meet each of the four libraries that carry the rest of the course: NumPy, pandas, Matplotlib and Seaborn.
The unit closes with project organisation — the conventions that keep a data analysis reproducible and shareable rather than a folder of mysteriously-named notebooks.
Learning outcomes
By the end of this unit you will be able to
- Write and run Python code for basic data manipulation.
- Create, structure and share Jupyter notebooks.
- Use NumPy arrays for numerical computation.
- Load, inspect and manipulate tabular data with pandas.
- Produce basic charts with Matplotlib and Seaborn.
- Organise a data analysis project reproducibly.
Tutorial sequence
Python Review
Variables, control flow, functions, data structures and comprehensions.
t-02Jupyter Notebooks
Cells, kernel state, markdown, magic commands and reproducibility.
t-03NumPy Introduction
Arrays, broadcasting, vectorised operations and indexing.
t-04Pandas Introduction
Series, DataFrames, reading files, selection and basic operations.
t-05Matplotlib
Figures, axes, plots, labels and saving outputs.
t-06Seaborn
Statistical plotting at a higher level of abstraction.
t-07Project Organization
Folder structure, naming conventions and version control basics.
t-08Practical Exercises
Hands-on practice combining all five tools on a small dataset.
t-09Unit Summary
Consolidated key points and a forward look to Module 2.
Key concepts
- Jupyter notebook
- An interactive document combining code, output, prose and visualisation in a single shareable file.
- NumPy array
- A fixed-type, multi-dimensional container for numerical data, supporting fast vectorised operations.
- DataFrame
- A pandas object representing a tabular dataset with labelled rows and columns.
- Reproducible project structure
- A folder layout and naming convention that allows another analyst to understand and rerun your work.
Assessment alignment
Foundation for every assignment
Every subsequent assignment is submitted as a Jupyter notebook. The skills in this unit — writing clean Python, structuring a notebook, loading data with pandas — are assumed from here on.