Unit name | Introduction to Data Science |
---|---|
Unit code | EMAT20011 |
Credit points | 10 |
Level of study | I/5 |
Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
Unit director | Professor. Cristianini |
Open unit status | Not open |
Pre-requisites | |
Co-requisites |
None |
School/department | School of Engineering Mathematics and Technology |
Faculty | Faculty of Engineering |
This unit will introduce core data analysis skills and concepts. Students will acquire fundamental data science skills including importing and exporting data, data visualisation, detecting statistical patterns, and testing their statistical significance. They will be introduced to key concepts from statistics and machine learning such as classification and regression, clustering and linear manifolds, time series analysis, multi-dimensional probability distributions, feature selection and generation, and network analysis. They will be exposed to different types of data and patterns and gain experience in data analysis using suitable software tools and packages.
Upon successful completion of the course, students will be able to
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets.
1 Summative Assessment, 100% - Coursework. This will assess all ILOs.
Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press