🖥️ Week 01, Day 01 - Lecture
What We Mean by Data Engineering, and What You’ll Build
By the end of today’s lecture, you should be able to: i) Describe the steps of a data engineering workflow, from collection to communication, ii) Name the tools you will use across the three weeks and roughly when you meet each one, iii) Explain what changes and what stays with you when an AI runs a data analysis unaided, iv) Locate the syllabus, the assessments, and this course’s AI use policy, v) Find your class teacher’s group and today’s afternoon lab.
⏰ Monday, 13 July 2026 | 10:00am - 1:00pm 📍 CBG.2.01 (see LSE’s 🗺️ campus map)
Welcome to ME204. Over the next three weeks you will collect data from the web, clean it, put it into tables, analyse it, and turn what you find into something other people can read and trust. This morning is about the shape of that journey before you write a single line of code: what data engineering means for a social science question, the tools you will pick up along the way, and what an AI can and cannot do for you when you ask it to do all of this at once.
🗣️ Lecture Overview
- What we mean by data engineering: the steps of a data workflow, and why each one still needs a human decision
- Where you’ll be by week 3: a tour of the tools this course builds up, from today’s terminal to the dashboard you submit at the end
- “Can’t AI do all of this for us?” a live test of that question, and what the room makes of the answer
- Course structure and AI policy: the midterm, the final project, and how AI fits into your work here
- This afternoon and the week ahead: what today’s lab asks of you, and how each day this week builds on the last
🎬 Lecture Slides
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After the Lecture
This afternoon you meet the terminal for the first time and start finding your way around the filesystem. No Python yet: that starts tomorrow.
Questions?