Prof Kenneth Benoit PEL.4.01C

This module is designed for students on social science degree programmes who do not have A-level Mathematics (e.g. in Anthropology, Law, and Social Policy).

This course is designed to introduce students to data science and its practice: how it works and how it can produce insights from social, political, and economic data. It combines accessible knowledge in data science as a field of study, with practical knowledge about data science as a career path. By combining case studies in applications of both with the study of the content of data science, it aims for a coverage of data science that is both pedagogic but accessible, as well as fundamentally applied and practical. It combines three perspectives: inferential thinking, computational thinking, and real-world relevance.

The topics covered include:

• the fundamentals of the data science approach, with an emphasis on social scientific analysis and the study of the social, political, and economic worlds;
• a survey of the forms of data and the challenges of working with data, including an overview of databases;
• the basis of computational thinking and algorithmic design;
• an introduction to the logic of statistical inference including probability and probability distributions and how they form the basis for statistical decision-making;
• a survey of the basic techniques of statistical learning and machine learning, including a comparison of different approaches, including supervised and unsupervised methods;
• how to integrate the insights from data analytics into knowledge generation and decision-making;
• examples of methods for working with unstructured data, such as text mining.

Our applications are drawn from the social science fields represented at the LSE but also from private and public sector non-academic examples.

16 hours and 40 minutes of lectures and 7 hours and 30 minutes of classes in the MT.

A combination of classes and lectures totalling 30 hours across Michaelmas Term.

Reading week in Week 6.

Students will be expected to produce 9 other pieces of coursework and 1 other piece of coursework in the MT.

Students will be presented with guided questions to answer in completing each week's reading, and discuss these in each class session. They will participate actively in presenting the answers of the questions to the group.

• Saltz, J. S., & Stanton, J. M. (2017). An introduction to data science. Sage Publications.
• Denning, P. J., & Tedre, M. (2019). Computational thinking. MIT Press.
• Shan, C. (2015). The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists. Data Science Bookshelf.
• Schutt, R., & O'Neil, C. (2014). Doing data science: Straight talk from the frontline. O'Reilly.
• Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
• Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

Essay (30%, 1500 words) and presentation (10%) in the MT.
Essay (60%, 2000 words) in the LT.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.