Level: 300 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours 

Classes: 18 hours

Assessment*: Two take-home assessments

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)

*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment

Students should already be familiar with quantitative methods at an introductory level, up to linear regression analysis. Familiarity with computer programming or database structures is a benefit, but not formally required.

The course will cover the following topics:

• an overview of data science and the challenge of working with big data using statistical methods
• how to integrate the insights from data analytics into knowledge generation and decision-making
• how to acquire data, both structured and unstructured, and to process it, store it, and convert it into a format suitable for analysis
• approaches to normalising data, using a database manager (SQLite), and working with SQL database queries

• the basics of statistical inference including probability and probability distributions, modelling, experimental design

• an overview of classification methods and related methods for assessing model fit and cross-validating predictive models
• supervised learning approaches, including linear and logistic regression, decision trees, and naïve Bayes
• unsupervised learning approaches, including clustering, association rules, and principal components analysis
• quantitative methods of text analysis, including mining social media and other online resources
• social network analysis, covering the basics of social graph data and analysing social networks
• data visualisation through a variety of graphs.

This course aims to provide an introduction to the data science approach to the quantitative analysis of data using the methods of statistical learning, an approach blending classical statistical methods with recent advances in computational and machine learning. We will cover the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods we cover. 

LSE’s Department of Methodology is an internationally recognised centre of excellence in research and teaching in the area of social science research methodology. The Department coordinates and provides a focus for methodological activities at the School, in particular in the areas of graduate student (and staff) training and of methodological research.

Through its graduate programmes, and the Department's provision of courses for research students from all parts of the School, the aim is to make the School the pre-eminent centre for methodological training in the social sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s methodology faculty.

• James et al. (2013) An Introduction to Statistical Leaning: With applications in R . Springer.
• Zumel, N. and Mount, J. (2014). Practical Data Science with R. Manning Publications.

The following are supplemental texts which you may also find useful:

• Lantz, B. (2013). Machine Learning with R. Packt Publishing.
• Conway, D. and White, J. (2012) Machine Learning for Hackers . O'Reilly Media.
• Leskovec, J., Rajaraman, A. and Ullman, J. (2011). Mining of Massive Datasets . Cambridge University Press.
• Zafarani, R., Abbasi, M. A. and Liu, H. (2014) Social Media Mining: An introduction . Cambridge University Press.

*A more detailed reading list will be supplied prior to the start of the programme

**Course content, faculty and dates may be subject to change without prior notice