Introduction to Data Science (G6085)
Introduction to Data Science
Module G6085
Module details for 2024/25.
15 credits
FHEQ Level 5
Module Outline
This module provides an introduction to the theory, techniques and practices of data science, using the Python programming language. Students will be grounded in probability theory and statistics and be given a practical introduction to data management, processing and visualisation. The module will build upon these foundations to introduce data analysis and some basic machine learning pipelines, covering regression, classification and clustering.. Throughout, a blend of practical, example-based approaches combined with the theoretical background will be adopted to enable students to ask and answer questions of real-world data
Module learning outcomes
Knowledge of good data science practices, in terms of code organisation, data management, processing, and visualisation.
Understanding of core concepts in probability and statistics for data science: how probability distributions can be characterised and estimated and how they inform analysis choices; hypothesis testing and Bayesian inference.
Demonstrate knowledge of the basic machine learning pipeline from data pre-processing to model training, selection and evaluation, for both classification and regression; understanding of a few standard methods such as linear regression, logistic regression, random forest and k-means clustering.
Use machine learning toolboxes to solve classification and regression problems with real-world data.
| Type | Timing | Weighting |
|---|---|---|
| Computer Based Exam | Semester 1 Assessment | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 1 hour | 22222222222 |
| Autumn Semester | Laboratory | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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