CS366 - Data Analytics
| TIMETABLE | TEACHING MATERIAL |
| Credits | 20 |
| Level | 3 |
| Semester | Term 1 |
| Availability | Available to participants taking the BSc Hons IT: Management for Business programme. |
| Prerequisites | CS121 Programming with Python |
| Learning Activities Breakdown | |
| Assessment | The class is assessed 100% by coursework consisting of two individual assignments (worth 40% and 30% respectively) and a class test (30%). |
| Lecturer | Georgi Nakov |
Aims and Objectives
The aim of this class is to provide participants with: an understanding of the challenges posed by the advent of big data (e.g. its modelling, storage, and access); an understanding of the key algorithms and techniques which are embodied in data analytics solutions; an understanding of the principles of Machine Learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques.
Learning Outcomes
On completion of this class, participants should be able to:
- understand how to use Python to enable the use of various big data technologies.
- understand how classical statistical techniques are applied in modern data analysis.
- understand the potential application of data analysis tools for various problems and appreciate their limitations.
- understand the aims and fundamental principles of Machine Learning.
- understand a range of the essential core algorithms and approaches to Machine Learning.
Syllabus
- Data Analytics: Quantitative methods for data analysis and knowledge extraction including classification, clustering, association rules, Bayesian approaches, decision trees.
- Machine learning basics: Overview of the machine learning process; Classifiers and classification measures; Training models; Support Vector Machines; Decision trees; Ensemble Learning; Dimensionality Reduction (Principal Components Analysis).
Recommended Reading
This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.
- Loshin, David. Big data analytics: from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph. Elsevier, 2013.
- Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow (2nd Edition). Geron, Aurelien at: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ The 2017 edition of this book is available in the library.
Transferable skills:
- Participants will develop their knowledge of mathematical concepts and numerical reasoning skills.
Last updated: 2023-09-10 20:22:01