LSE DS202 – Data Science for Social Scientists

Author

Dr. Ghita Berrada & Dr. Jon Cardoso-Silva

Image Created with Microsoft Designer. Prompt: 'A website favicon design using a mix of Persian indigo (#360568) and celadon (#A5E6BA) shades, created in an impressionist style. The image captures the magical experience of analyzing and modeling data with machine learning. It features abstract representations of interconnected data nodes and flowing patterns transforming into sparkling, gem-like insights. The design uses soft, painterly brushstrokes and harmonious blending of colors to evoke an artistic, dreamy, and Monet-inspired atmosphere, while being simple and clear for a favicon format.'

📑 Course Brief

Focus: learn and understand the most fundamental machine learning algorithms

How: practical use of machine learning techniques and its metrics, applied to relevant data sets

🎯 Learning Objectives

  • Understand the fundamentals of the data science approach, with an emphasis on social scientific analysis and the study of the social, political, and economic worlds;
  • Understand how classical methods such as regression analysis or principal components analysis can be treated as machine learning approaches for prediction or for data mining.
  • Know how to fit and apply supervised machine learning models for classification and prediction.
  • Know how to evaluate and compare fitted models, and to improve model performance.
  • Use applied computer programming, including the hands-on use of programming through course exercises.
  • Apply the methods learned to real data through hands-on exercises.
  • Integrate the insights from data analytics into knowledge generation and decision-making.
  • Understand an introductory framework for working with natural language (text) data using techniques of machine learning.
  • Learn how data science methods have been applied to a particular domain of study (applications).

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