Data Science with Machine Learning
| Code | School | Level | Credits | Semesters |
| COMP4030 | Computer Science | 4 | 20 | Spring UK |
- Code
- COMP4030
- School
- Computer Science
- Level
- 4
- Credits
- 20
- Semesters
- Spring UK
Summary
This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems.
Topics covered include: data collection, data visualisation techniques, data modelling, data pre-processing methods (including data imputation), clustering and classification methods, and model interpretation techniques to aid decision support. We will cover topics relating to ethics and AI (responsible research and ensuring equality, diversity and inclusion to address issues such as bias).
Spending around 4 hours each week in lectures and computer classes, appropriate software will be used to illustrate the topics you'll cover.
Target Students
Available to Level 3 and 4 students in the School of Computer Science. This module is not available to students not listed above without explicit approval from the module convenor(s). This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Classes
Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester
Assessment
- 25% Coursework 1: Series of in-lab exercises
- 75% Coursework 2: Data science study
Assessed by end of spring semester
Educational Aims
To introduce the principles, techniques and applications of a range of data science and machine learning techniques.To enable the students to appreciate some of the most widely used data science and machine learning techniques and to know which one to choose for their applications.To enable the students to understand and be able to put into practice computer-based data science and machine learning techniques.Learning Outcomes
Knowledge and Understanding
- Understanding the capabilities, strengths and limitations of data science and machine learning methods (A3).
- An appreciation of different data science and machine learning techniques (A4).
Intellectual Skills
- The ability to understand complex ideas and relate them to specific situations (B4).
Professional Skills
- The ability to implement selected data science and machine learning methods for real world applications (C1).
- The ability to evaluate data science and machine learning techniques and select those appropriate to a given task (C3).
Transferable Skills
- The ability to address real problems and assess the value of their proposed solutions (D1).
- The ability to retrieve and analyse information from a variety of sources, ensuring that it has been ethically collected, and produce detailed written reports on the result (D4).