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Dr Jonathan Cardoso Silva COL.1.03

This course is compulsory on the BSc in Politics and Data Science and BSc in Psychological and Behavioural Science. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course cannot be taken with ST201 Statistical Models and Data Analysis.

Material from the previous year can be found on the course's dedicated public webpage: https://lse-dsi.github.io/DS202/

A-level maths or equivalent.

An important note on programming: While programming is not strictly a pre-requisite for this course, basic programming knowledge, preferably in Python or R, is highly recommended. Students should be comfortable creating and updating variables, creating simple functions, and using flow control expressions like if-else statements, for and while loops, etc. Those who are new to coding may find the course challenging, and we encourage them to consider the Winter iteration of the course, DS202W. This will provide additional time to improve their programming skills. We recommend that students with limited programming experience explore courses such as ST101, the Digital Skills Lab workshops or the self-paced pre-sessional course listed on the DS202 Moodle page.

The main goal of this course is to provide students with a hands-on introduction to the most fundamental machine learning algorithms, as well as the metrics commonly used to assess algorithmic performance and decision-making aspects in real-life scenarios. The course will be taught through a combination of staff-led lectures and classes, with a primary focus on practical applications. R will be the primary programming language, and there will be a recap of the tidyverse set of packages in the first weeks of the course. 

In terms of content, the learning objectives of this course are to:

• Understand the fundamentals of the data science approach, with an emphasis on social scientific analysis and the study of the social, political, and economic worlds;
• Understand how classical methods such as regression analysis or principal components analysis can be treated as machine learning approaches for prediction or for data mining;
• Know how to fit and apply supervised machine learning models for classification and prediction;