LSE DS202A - Data Science for Social Scientists

2023/24 Winter Term

Author

Dr. Ghita Berrada

Here is how you will be assessed in this course.

Context

Your grade in this course consists of two main components:

  1. COURSEWORK (60%): This component includes three individual problem sets, each contributing to a different percentage of your final grade (10%, 20%, and 30%). You’ll need to submit these assignments via Moodle or GitHub Classroom, following the deadlines specified in the 📓 Syllabus.

  2. ONLINE EXAM (40%): This will scheduled centrally by LSE and will take place in the Summer Exams period. The exam is a take-home, open-book assignment, and will be based on all the material covered in the course. Different to past years, you will have 24 hours to complete the exam and we might require a bit of coding. More details will be provided in due course.

📝 Individual Problem Sets

We aim to provide feedback on your work within two to three weeks after the submission deadline (🤞).

These problem sets will have a similar style to the formative problem sets, and exercises done in the labs – although a bit more challenging. Typically, this will involve a mix of R tasks and your written interpretation of the analyses.

Formative

Simple practice problem set
(due Week 03 and to be announced in Week 02’s lecture)

We want you to practice:

  • Submitting work via GitHub Classroom
  • Using Quarto markdown
  • Writing simple R code (using tidyverse)


10%

Problem Set 01 — Linear Models
(due Week 04)

See the 🎯 Learning Objectives involved


  • Use applied computer programming (to modify data)
  • Know how to fit and apply supervised machine learning models for prediction (regression)


20%

Problem Set 02 — Supervised learning
(due Week 07)

See the 🎯 Learning Objectives involved


  • Know how to fit and apply supervised machine learning models for classification and prediction.
  • Apply the methods learned to real data through hands-on exercises.
  • Know how to evaluate and compare fitted models, and to improve model performance.


30%

Problem Set 03 — Unsupervised learning
(due Week 11+1)

See the 🎯 Learning Objectives involved


  • Understand how classical methods such as regression analysis or principal components analysis can be treated as machine learning approaches for prediction or data mining.
  • 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.

✍️ Exam (40%)

  • An open-book take-home online exam, taken during the Summer exams period.
  • The exam questions will be released either via Moodle or GitHub Classroom.

40%

Online Exam
(Summer Exam Period)

  • The exam will be based on all the material covered in the course, including the lectures, labs, problem sets and applications (W09-W11).