Introduction to Data Science (G6085)
Note to prospective students: this content is drawn from our database of current courses and modules. The detail does vary from year to year as our courses are constantly under review and continuously improving, but this information should give you a real flavour of what it is like to study at Sussex.
We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.
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 |
|---|---|---|
| Multiple Choice questions | 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.
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.