🧑‍🏫 Week 01, Day 01 - Lecture

Foundations of Data Wrangling with Python

Author

Dr Jon Cardoso-Silva

Last updated

14 July 2025

🥅 Learning Objectives

By the end of this session, you should be able to: i) Articulate the importance of data engineering for reproducible social science research, ii) Outline the key stages of a data science workflow, from collection to reporting, iii) Develop an intuition about what a high-level data processing plan for a real-world case study might look like, iv) Locate essential course information regarding the syllabus and assessments, v) Justify the use of core course technologies, such as Python and Git, for data-intensive projects.

⏰ Date and Time: Monday, 14 July 2025 | 10.00am - 1.00pm 📍 Location: CKK.2.06 (see LSE’s 🗺️ campus map)

Welcome to ME204!

This first lecture sets the stage for our three-week journey into data engineering. We’ll start with the big picture: why these skills matter for social scientists and what you’ll be capable of doing by the end of this course. Everything we do today is about showing you the destination before we hand you the map.

🗣️ Lecture Overview

This first session is structured around the key sections, which you can find in the morning’s slides below:

Introductions: Meet the teaching team and your fellow students.
Course Structure & Syllabus: A detailed look at what we’ll cover, how you’ll be assessed, and our approach to using AI.
Group Activity: Map the data workflow for a real-world case study.
Our Toolbox: An overview of the key technologies we will use throughout the course (Python, Git, Nuvolos).
Next Steps: Find out what to expect from this afternoon’s lab and the week ahead.