Programming for Analytics will enable students to develop the knowledge and skillset to use the Python programming language to process and analyse data. This will include developing foundational skills in programming (control, iteration, data structures, string manipulation, handling dates and times) and skills for processing data such as regular expressions, reading and writing files, using libraries such as NumPy and Pandas, using Python for statistical analysis and data visualisation using Matplotlib and Seaborn.
Fundamentals of Programming
- Variables, Control, Iteration, String manipulation.
- Data structures, arrays, lists, objects.
Python Basics
- Setting up Python environment.
- Writing and running Python scripts.
- Setting up Git environments
- Managing and versioning code.
Data Programming Languages
- Common features - frames, matrices, graphics libraries
- Python packages - pandas, numpy, seaborn
Data Handling
- Reading and writing data (CSV, JSON).
- Introduction to libraries (NumPy, Pandas).
- Accessing data through web scraping.
- Accessing data through APIs.
- Data cleaning and preprocessing
Data Manipulation, Analysis
- Manipulating DataFrames with Pandas.
- Using Python for statistical analysis.
Data Visualisation
- Creating charts using Matplotlib and Seaborn
- Advanced chart types
- Interactive data visualisations
- Customising visualisations
- Combining multiple visualisations
This module will be delivered using lectures, practical laboratory exercises, and assignments. There will be a strong practical element.
The module's co-requisite module on Data Exploration will provide students with the knowledge of Exploratory Data Analysis that will be required for elements of the project that will be completed.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |