Data Science and Big Data Analytics are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods.
This course integrates prior training in quantitative methods (statistics) and coding with substantive expertise and introduces the fundamental concepts and techniques of Data Science and Big Data Analytics.
Typical students will be advanced undergraduate and postgraduate students from any field requiring the fundamentals of data science or working with typically large datasets and databases. Practitioners from industry, government, or research organisations with some basic training in quantitative analysis or computer programming are also welcome. Because this course surveys diverse techniques and methods, it makes an ideal foundation for more advanced or more specific training. Our applications are drawn from social, political, economic, legal, and business and marketing fields.
Dates: 29 July – 16 August 2019
Lecturers: Professor Kenneth Benoit, Professor Slava Mikhaylov and Dr Jack Blumenau
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:
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.
The Department of Methodology is an internationally recognized 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 research student training and of methodological research. The department is heavily involved in the School-wide Master's programme in Social Research Methods, and provides courses for research students from all parts of the School, with the aim of making LSE 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.
- 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