Python for Data Analysis and Research: Beyond the Basics

Overview

This practical course develops existing Python programming skills and introduces learners to using Python for data analysis and research-oriented applications. It is intended for learners who have completed Python for Beginners: Programming Foundations or who already have equivalent knowledge of basic Python programming.

Learners will learn how to import, inspect, manipulate, analyse and visualise datasets using widely used Python libraries including NumPy, Pandas and Matplotlib. Emphasis will be placed on developing clear and reproducible workflows rather than simply reproducing code examples.

Through practical exercises and an individual data-analysis project, learners will develop the confidence to apply Python to real datasets drawn from scientific, professional and other data-rich contexts.

Programme

The module will build on fundamental Python programming skills and focus on applying Python to structured data and research-style problems.

1 - Python workflow

• Review of functions, lists, dictionaries and file handling
• Working effectively with notebooks and scripts
• Importing Python libraries
• Organising an analytical workflow

2 - Numerical computing with NumPy

• NumPy arrays
• Array creation and manipulation
• Indexing and slicing
• Numerical operations
• Vectorised calculations
• Comparison between Python lists and numerical arrays

3 - Working with data using Pandas

• Series and DataFrames
• Importing common data formats such as CSV
• Inspecting and describing datasets
• Selecting and filtering data
• Creating and modifying columns
• Handling missing or inconsistent values
• Sorting and grouping data
• Performing summary calculations

4 - Data visualisation

• Principles of effective data visualisation
• Creating plots using Matplotlib
• Line, scatter, bar and histogram visualisations
• Labels, legends and annotations
• Choosing appropriate visualisations for different types of data
• Producing clear publication- or presentation-ready figures at an introductory level

5 - Reproducible analytical practice

• Structuring analysis logically
• Reusing functions
• Documenting code
• Separating data processing and visualisation steps
• Testing and debugging analytical workflows

6 - Integrated data-analysis project

• Formulating a simple analytical question
• Importing and preparing a dataset
• Performing appropriate analysis
• Visualising key findings
• Interpreting and communicating results

Learning Outcomes

On completion of this module, students should be able to:  

Use NumPy to store, manipulate and perform numerical operations on structured numerical data.

Import, inspect, clean, select and manipulate tabular datasets using Pandas.

1. Apply appropriate numerical and descriptive methods to explore and summarise data.
2. Create clear and appropriately labelled visualisations using Python.
3. Organise Python code and analytical steps into a clear, reproducible data-analysis workflow.
4. Apply Python to investigate a real dataset and communicate the resulting analysis and findings effectively
5. ddress a practical problem.

Assessments

Applied Data Analysis. Students will complete a structured set of data-analysis exercises requiring the use of NumPy, Pandas and data visualisation techniques. (40%)

Individual Data Analysis Project. Students will independently undertake an analysis of an appropriate real

Reading Suggestions

Reading suggestions will be offered throughout the course. 

Entry Requirements

This course is for everyone. No previous experience is needed and there are no formal entry requirements.

What Do I Need?

As this is an online course, you would need the following:

• Internet access
• Access to a laptop or computer with a web camera and microphone; the use of headphones might also be of benefit
• Use of the Chrome web-browser where possible.