LSE DS202

Data Science for Social Scientists

Author

Published

13 November 2022

📓 Syllabus

A list of what happens every week.

Click on each Week’s link for more information (slides, lab instructions, recommended resources, etc.).

Intro
	🗓️ W01	Lecture	Introduction, Context & Key Concepts (James et al. 2021, chap. 2)
	🗓️ W01	Lab	No class this week. (Use this time to revisit basic R programming)
Supervised Learning
	🗓️ W02	Lecture	Simple and Multiple Linear Regression (James et al. 2021, chap. 3)
	🗓️ W02	Lab	Revision of `R`: data structures, basic commands and some tidyverse
	🗓️ W03	Lecture	Classifiers (Logistic Regression & Naive Bayes) (James et al. 2021, chap. 4)
		Lab	Linear Regression (James et al. 2021, chap. 3)
		Formative	Worth: knowledge! Problem set involving linear regression. Release date: 15 October 2022 (link to Moodle) Deadline: 25 October 2022 (Week 05)
	🗓️ W04	Lecture	Resampling methods (James et al. 2021, chap. 5)
	🗓️ W04	Lab	Classification Methods (James et al. 2021, chap. 4)
	🗓️ W05	Lecture	Non-linear algorithms (SVM & tree-based models) (James et al. 2021, chaps. 8–9)
		Lab	Cross-Validation and the Bootstrap (James et al. 2021, chap. 5)
		Summative	Worth: 20% of final marks Problem set involving regression, classification and resampling Release date: 28 October 2022 Deadline: 9 November 2022 (Week 07)
Unsupervised Learning
	🗓️ W07	Lecture	Unsupervised Learning: Clustering (James et al. 2021, chap. 12)
	🗓️ W07	Lab	Tree-based models (James et al. 2021, chap. 8)
	🗓️ W08	Lecture	Unsupervised Learning: Dimensionality Reduction (James et al. 2021, chap. 12)
		Lab	Support Vector Machine + `tidymodels` + recap of cross-validation (James et al. 2021, chaps. 5, 9)
		Summative	Worth: 20% of final marks Problem set about unsupervised learning Release date: 18 November 2022 Deadline: 29 November 2022 (Week 10)
Applications
	🗓️ W09	Lecture	Applications: Text as Data & Topic Modelling
		Guest:	Prof. Ken Benoit
		Lab	Unsupervised Learning: Clustering (James et al. 2021, chap. 12)
	🗓️ W10	Lecture	Applications: Predictive Modelling on Tabular Data
		Lab	Unsupervised Learning: Principal Component Analysis (James et al. 2021, chap. 12)
		Summative	Worth: 20% of final marks Problem set about ML applications (text mining/social media) Release date: 2 December 2022 Deadline: 15 December 2022 (Week 11+1)
	🗓️ W11	Lecture	Applications: Social Media Data
		Guests:	Sara Luxmoore, MSc in Applied Social Data Science (LSE) Anton Boychenko, MSc in Applied Social Data Science (LSE)
		Lab	We will explore unsupervised models using a couple of text datasets

				…
	🗓️ Jan/23	Exam	Worth: 40% of final marks Problem set about supervised + unsupervised learning + applications 3 hours + 1 hour for submission Online exam via Moodle Date: Sometime during the Exam Period. Exact timeslot has not yet been confirmed by LSE Exams team.

References

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. Second edition. Springer Texts in Statistics. New York NY: Springer. https://www.statlearning.com/.