CS353 - Fundamentals of Data Analytics
| TIMETABLE | TEACHING MATERIAL |
| Credits | 15 |
| Level | 3 |
| Semester | Term 1 |
| Availability | Available to participants taking UG Graduate and Degree Apprenticeship programmes, e.g. BSc Hons IT: Software Development and BSc Hons Digital and Technology Solutions. |
| Prerequisites | Previous programming experience would be beneficial. |
| Learning Activities Breakdown | 12 tutorials, online study, and assignment preparation (see Assessment section for details). |
| Assessment | The class is assessed 100% by coursework which will consist of programming exercises (worth 30%) as well as a data analytics assignment (worth 70%). |
| Lecturer | Georgi Nakov |
Aims and Objectives
The aim of this class is to provide participants with: a) Python programming skills; b) 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.
Learning Outcomes
After completing this class participants will be able to:
- Understand and implement main control and flow structures of an imperative programming language (e.g. Python).
- Understand and implement simple data elements, basic data structures, and the main code structure constructs of an imperative programming language (e.g. Python).
- 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.
Syllabus
Introduction to Python: simple data types (numbers, strings, simple list data structures and booleans); simple operators (assignment, arithmetic and string manipulation); control flow (conditional and iteration); manipulating simple data structures (lists, sets, and dictionaries); structuring code (functions); error handing (exceptions); library classes and documentation; testing and debugging.
Data Analytics: Quantitative methods for data analysis and knowledge extraction including classification, clustering, association rules, Bayesian approaches, decision trees.
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.
Recommended reading suggestions will be made available via Myplace, the University's VLE.
Transferable skills:
During and after completing this class participants will:
- Develop their understanding of Mathematical concepts as well as their numerical reasoning skills.
Last updated: 2023-09-07 18:35:48