Dr Ghita Berrada COL.1.02

This module is designed for students in social science degree programmes who do not have A-level Mathematics (e.g., in Anthropology, Law, and Social Policy). Students with little to no experience in computer programming are welcome. 

This course can serve as an entry point or be taken concurrently with other DS courses, such as DS105 or DS202. However, please note that this course is not suitable for students who have already completed other DS courses.

This course is not capped. Any student who requests a place is likely to be given one.

Material from the previous year can be found on the course's dedicated public webpage: https://lse-dsi.github.io/DS101.

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 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 WT.

Reading Week in Week 6.

Students will be expected to produce 9 pieces of coursework in the WT.

Students will be expected to produce nine pieces of coursework in the Winter Term.

Throughout the course, students will receive guided questions related to the week's readings, which they are expected to answer and discuss with the rest of the group. Active participation is encouraged. Additionally, some sessions will include structured problem sets during staff-led classes, with solution examples provided at the end of each week.

• Mayer-Schönberger, Viktor, and Kenneth Cukier. Big Data: A Revolution That Will Transform How We Live, Work and Think. 1st edition. London: Murray, 2013.
• Shah, Chirag. A Hands-on Introduction to Data Science. Cambridge, United Kingdom; New York, NY, USA: Cambridge University Press, 2020.
• Schutt, Rachel, and Cathy O'Neil. Doing Data Science. 1st edition. Beijing [China]; Sebastopol [CA]: O'Reilly Media, 2013.
• Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley, 2015.
• Denning, Peter J., and Matti Tedre. Computational Thinking. The MIT Press Essential Knowledge Series. Cambridge, Massachusetts: The MIT Press, 2019.
• Bruce, Peter C., and Andrew Bruce. Practical Statistics for Data Scientists: 50 Essential Concepts. 1st edition. Sebastopol [CA]: O'Reilly, 2017. 
• Flach, Peter A. Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge: Cambridge University Press, 2012. 
• Shan, Carl, Max Song, Henry Wang, and William Chen. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists. 1st edition. The Data Science Bookshelf, 2015.
• Hare, Stephanie. Technology Is Not Neutral: A Short Guide to Technology Ethics. Perspectives. London: London Publishing Partnership, 2022.

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