DS101A      Half Unit
Fundamentals of Data Science

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Mutually exclusive courses:

This course cannot be taken with DS101W at any time on the same degree programme.

This course, which is built around real-world case studies, is designed to be a gentle introduction to data science/AI and its practice: how it works, how it can produce insights from social, political, and economic data and where it can encounter issues in this process.

It is NOT designed to be a programming course (students interested in programming are advised to explore courses such as DS205, DS105A, DS105W, ST101, Digital Skills Lab workshops or self-paced pre-sessional courses in R or Python). By combining case studies in applications with the study of fundamental concepts of data science/AI, it aims for a coverage of contemporary issues and advances in data science and AI that is both pedagogic but accessible, as well as fundamentally applied and practical. It combines two main perspectives: 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;

- An introduction to the forms of data can take and a discussion of the challenges of working with data, 

- 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.

- An introduction to the basic principles of generative AI and large language models

- A discussion of the ethical issues linked to AI algorithms and ways to address them 

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

15 hours of classes and 20 hours of lectures in the Autumn Term.

This course has a reading week in Week 6 of Autumn Term.

Most sessions will be built around the analysis and discussion of real-world case studies. The active participation of students in the sessions is highly recommended. Students might be asked to perform some preparatory work for the sessions’ case studies. They will also be expected to produce 4 pieces of coursework.



 

• 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.
• Hare, Stephanie. Technology Is Not Neutral: A Short Guide to Technology Ethics. Perspectives. London: London Publishing Partnership, 2022.
• Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from data. Vol. 4. New York: AMLBook, 2012.

Presentation (30%)

This component of assessment includes an element of group work.

Case analysis / study (70%)