LSE DS101 - Fundamentals of Data Science
🎯 Focus: theoretical concepts of data science
📂 How: reflections through reading and writing
📑 Course Content
This course introduces 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 of data science as a field of study with practical knowledge about data science as a career path. Combining case studies in applications of both with the study of the content of data science, it aims for coverage of data science that is both pedagogic and 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 (key concepts);
- 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 draw from the LSE’s social science fields and private and public sector non-academic examples.
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