LSE DS202A - Data Science for Social Scientists

2025/26 Autumn Term

Author

📣 NOTE (updated 29 September 2025):

The syllabus has been revised to take into account the experiences of students on the previous sessions of the DS202A/DS202W course (needs and difficulties).

📋 NOTE:

Ideally, you should also be taking the pre-sessional courses offered by LSE Digital Skills Lab in the first weeks of the Autumn Term:

R Pre-sessional workshops

Check this page every week to see more info on how to study for the course.

Part 01

The first half of the course focuses on the fundamentals of machine learning algorithms, with an emphasis on supervised learning.

🗓️ Week 01
29 Sep 2025 -
03 Oct 2025

💻 Lab

R/RStudio + tidyverse recap

To fully prepare for this lab, we highly recommend you go through the setup steps outlined in section 1 of the 📋 Getting Ready page.

👩🏻‍🏫 Lecture

Introduction, Course Logistics & R programming

📖 Revise

Click to see if you’re caught up

Ensure you have R installed on your computer
Ensure you have an IDE (RStudio or VSCode) installed on your computer.
Install tidyverse
Revisit base R vs tidyverse syntax equivalence
Skim the textbook references mentioned in the slides to find out more about the topics covered in the lecture.

🛟 Support

Click here to see how to get help this week

We love hearing from you! Truly! Don’t hesitate to contact us for help.

In this first week, the best ways to get help are:

Slack: Post any question you might have about the course (lab or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 02 October 2025 from 16.30-18.30. Also, check for availability of office hours of some of your class teachers.
📧 E-mail: Not sure if this course is for you? Or you have a valid reason to request a change of class? For these and other administrative queries, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 02
06 Oct 2025 -
10 Oct 2025

💻 Lab

Practice data manipulation with dplyr and tidyr

👩🏻‍🏫 Lecture

Supervised Learning: Introduction to Regression Algorithms

What is Supervised Learning? What is Regression?
Algorithm: Linear Regression (simple and multiple)

📣 Assignment Reveal

To help you familiarise yourself with the style of the summative assignments, we will announce a formative (practice) assignment this week.

This assignment will be about dplyr and tidyr as well as regression. The precise requirements will be announced in the lecture.
You will submit your assignment via GitHub Classroom.

🛟 Support

Click here to see how to get help this week

We’re steadily adding to your data scientist knowledge toolbox. If things start to feel confusing in any way, don’t hesitate to contact us for help!

In this second week, the best ways to get help are:

Slack: Post any question you might have about the course (lab or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 09 October 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as class change, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 03
13 Oct 2025 -
17 Oct 2025

💻 Lab

Linear regression (simple and multiple), a tidymodels tutorial

👩🏻‍🏫 Lecture

Supervised Learning: Fundamentals of Classification

What is classification?
Classification vs regression
Algorithm: Logistic Regression
From binary to multi-class classification
K-nearest neighbours

📚 Homework (W04 lab prep)

Tutorial on tidymodels recipes and workflows

Spend some time working on this homework before week 4’s lab. It would help you prepare for it!

🆘 Drop-in session

A drop-in session will be organized this week to help you understand the nuts and bolts of GitHub and Quarto Markdown.

🛟 Support

Click here to see how to get help this week

We’re starting to pick up speed here. If things are confusing in any way, don’t hesitate to contact us for help!

In this third week, the best ways to get help are:

Slack: Post any question you might have about the course (lab, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 16 October 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 04
20 Oct 2025 -
24 Oct 2025

💻 Lab

How to solve a classification problem: logistic regression and k-nearest neighbours

👩🏻‍🏫 Lecture

Supervised Learning: Resampling methods

How to evaluate a model?
What is overfitting?
What is resampling?
Method: The Bootstrap
Method: Train-Test Split
Method: Cross-Validation

⌛ Deadline

Your first formative will be due this week!

🛟 Support

Click here to see how to get help this week

Another exciting week in terms of data science knowledge. If you find any new concept you learned unclear, need help with your upcoming formative or want to talk to us about some other pressing issue, don’t hesitate to contact us for help!

In this fourth week, the best ways to get help are:

Slack: Post any question you might have about the course (lab or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 23 October 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 05
27 Oct 2025 -
31 Oct 2025

💻 Lab

Resampling, model evaluation and an introduction to tree-based models

👩🏻‍🏫 Lecture

Supervised Learning: Non-linear algorithms and ensemble methods

What is non-linearity?
Why can’t linear models capture non-linearity?
Algorithm: Support Vector Machines
Algorithm: Decision Trees
Algorithm: Random Forests

📚 Homework

Problem set: hyperparameter tuning, resampling, model evaluation and comparison

This should help you practice (and revise!) the concepts related to supervised learning you’ve learnt so far. Consider that a good preparation for your upcoming summative.
Solutions will be provided right after Reading Week.

📣 Assignment Reveal

Your Summative 01, worth 30% of your final grade will be announced in the lecture of this week.

🛟 Support

Click here to see how to get help this week

Another exciting week in terms of data science knowledge. If you find any new concept you learned unclear, need help with your homework or upcoming summative or want to talk to us about some other pressing issue, don’t hesitate to contact us for help!

In this fifth week, the best ways to get help are:

Slack: Post any question you might have about the course (lab, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 30 October 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 06
03 Nov 2025 -
07 Nov 2025

📚 Practice and homework time

Reading Week

There are no classes or lectures this week.

Use this time to:

review what you learnt so far
do the homework you were given in week 5 (it will help you review and practice what you learnt so far)
start on your upcoming W08 summative

🛟 Support

Click here to see how to get help this week

TBC

Part 02

In the second half of the course, the focus shifts to unsupervised learning.

🗓️ Week 07
10 Nov 2025 -
14 Nov 2025

💻 Lab

Dimensionality reduction: a tutorial

👩🏻‍🏫 Lecture

Unsupervised Learning: Introduction and Dimensionality reduction

What is unsupervised learning? How does it differ from supervised learning?
What is dimensionality reduction, and why is it useful?
Algorithm: PCA
Algorithm: MCA
Algorithm: FAMD
Algorithm: UMAP
Algorithm: autoencoders

🛟 Support

Click here to see how to get help this week

In this seventh week, the best ways to get help are:

Slack: Post any question you might have about the course (lab, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 13 November 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
🆘 Drop-in sessions: We will host drop-in sessions on Week 07 to help support you with your Summative 01 (due in Week 8).

🗓️ Week 08
17 Nov 2025 -
21 Nov 2025

💻 Lab

Unsupervised Learning: Obtaining Insights via Clustering

👩🏻‍🏫 Lecture

Unsupervised Learning: Clustering

What is clustering?
Algorithm: k-means
Variants of k-means
Algorithm: DBSCAN
What is the distinction between clustering (e.g k-means) and dimensionality reduction (e.g PCA)?

⌛ Deadline

Your Summative 01 will be due this week. The topic of the summative will be announced in the lecture of Week 05.

🛟 Support

Click here to see how to get help this week

Another exciting week in terms of data science knowledge. If you find any new concept you learned unclear, need help with your upcoming summative or want to talk to us about some other pressing issue, don’t hesitate to contact us for help!

In this eighth week, the best ways to get help are:

Slack: Post any question you might have about the course (lab or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 20 November 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 09
24 Nov 2025 -
28 Nov 2025

💻 Lab

Anomaly detection – A tutorial

👩🏻‍🏫 Lecture

Unsupervised Learning: Anomaly detection

Unsupervised learning goes beyond clustering: Outliers/anomalies can be important too! Examples of anomaly detection use cases
Algorithm: Anomaly detection through clustering (e.g., DBSCAN)
Algorithm: Tree-based anomaly detection with isolation forests
Algorithm: Anomaly detection through density estimation (Local outlier factor (LOF))

🛟 Support

Click here to see how to get help this week

Another exciting week in terms of data science knowledge. If you find any new concept you learned unclear or want to talk to us about some other pressing issue, don’t hesitate to contact us for help!

In this nineth week, the best ways to get help are:

Slack: Post any question you might have about the course (lab or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 27 November 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.

Part 03

Finally, you will be introduced to the basics of text mining, and then we will look at some applications of the algorithms we’ve learned so far.

🗓️ Week 10
01 Dec 2025 -
05 Dec 2025

💻 Lab

A tutorial of quanteda

👩🏻‍🏫 Lecture

Applications: Text as Data & Topic Modelling

📣 Assignment Reveal

In this week’s lecture, we will announce your Summative 02, worth 30% of your final grade.

🛟 Support

Click here to see how to get help this week

In this tenth week, the best ways to get help are:

Slack: Post any question you might have about the course (lab, lecture or assignments) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 04 December 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.

🗓️ Week 11
08 Dec 2025 -
12 Dec 2025

💻 Lab

Decision-making time: a case study that ties everything you’ve learnt together!

👩🏻‍🏫 Lecture

Applications: Predictive Modelling on Tabular Data, a walkthrough

⌛ Deadline

Your Summative 02 will be due sometime the week following the end of the term. The exact deadline will be announced in the lecture of Week 10.

🛟 Support

Click here to see how to get help this week

Another exciting week in terms of data science knowledge. If you find any new concept you learned unclear, need help with upcoming summative or want to talk to us about some other pressing issue, don’t hesitate to contact us for help!

In this last week of term (time flies!), the best ways to get help are:

Slack: Post any question you might have about the course (lab, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week.
💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Thursday, 11 December 2025 from 16.30-18.30.

Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page).
📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
🆘 Drop-in sessions: We will host drop-in sessions on Week 11 to help support you with your Summative 02 (due in Week 11+1).

Part 04

Finally, it’s time to put everything you’ve learnt in practice in the final group project that spans two weeks in Winter term.

🗓️ Group project
26 Jan 2025 -
09 Feb 2025

🚀 Launch (29 Jan 2025)

“Speed-dating” with datasets : you’re presented with datasets (with associated research questions) and asked to rank them by order of preference by 4pm
Groups (no more than 4 students per group) will be assigned by the end of the day. So stay tuned!

🔎 First check-in session (28 Jan 2025)

Quick drop-in session to check your research plans are realistic and you’re not going off-track!

🔎 Second check-in session (4 Feb 2025)

Drop-in session to discuss the analysis you’ve conducted so far and check you’re still on track

⌛ Deadline (09 Feb 2025)

Your final project report will be due on Feb 09 at 5pm.