🖥️ Week 11 Lecture

Databases, Dashboards, and Agentic Coding

🥅 Learning Goals

By the end of this lecture, you should be able to: i) design and create a SQLite database with primary keys and foreign keys, ii) populate it from pandas and read back with pd.read_sql(), iii) build a Streamlit dashboard with interactive filters, iv) judge when a dashboard adds value versus when a static report is more appropriate, v) apply the 4D AI Fluency Framework when using AI coding tools.

📍 Logistics

📍Location: Thursday, 2 April 2026, 4-6 pm at CKK.LG.03

Today is the last lecture of DS105W. It covers three topics that feed directly into your 📦 Group Project work over Easter and beyond: designing and populating a SQLite database, building a Streamlit dashboard, and working productively with AI coding tools.

Note: The third part of this lecture (agentic coding with GitHub Copilot) will not be recorded. It is available only for students attending in person.

📋 Preparation

• You attended the 🖥️ W10 Lecture and 💻 W10 Lab
• You completed the formative group pitch on Monday or Tuesday this week
• Your group has a GitHub repository set up through GitHub Classroom

Well, we tried but still only a minority responded to the survey

ℹ

(39 out of 103 have completed the course survey. We did not reach the green zone.)

38%

0% – 50%

50% – 75%

75% – 100%

The LSE runs a course survey every term, and your feedback genuinely shapes how this module is taught next year. It takes about 3 minutes. 🐼

Click here to complete the survey

💡 Note: Please assess all the instructors you have interacted with
(Jon counts as a teacher too!).

Last updated: 2 April 2026

🗣️ Lecture Overview

Part 1: Designing a Database for Your Project

• Why split data into separate tables (the IMDb example from W09/W10)
• CREATE TABLE with column types, primary keys, and foreign keys
• Column type affinity in SQLite (connecting back to bits and bytes from W02)
• The DS105 way: build DataFrames in pandas, then df.to_sql(..., if_exists='append')
• Managing your connection and reading back with pd.read_sql()

Part 2: Building Dashboards with Streamlit

• From Markdown to HTML to Streamlit: the connection
• A minimal Streamlit app and interactive widgets
• Caching and keeping your code clean with utils.py
• When a dashboard earns its place vs when a static report is more appropriate
• Live walkthrough of an ONS report

Part 3: Agentic Coding with GitHub Copilot (not recorded)

• How coding workflows have changed with AI tools
• GitHub Copilot in VS Code: inline suggestions, chat, agentic mode
• The 4D AI Fluency Framework (Delegation, Description, Discernment, Diligence)
• Live demo and implications for your Group Project

📓 Lecture Materials

Today uses facilitation slides across all three parts. No separate lecture notebook this week; the code examples are embedded in the slides and designed for you to adapt to your own project data.

🎬 Facilitation Slides

Use keyboard arrows to navigate. Select the slides below or view fullscreen.

Or download the slides directly as a PDF:

🔖 Appendix

Key References

• 📖 Normalization in SQL: A Beginner’s Guide (DataCamp)
• 📖 SQLite Datatypes In SQLite Version 3
• 📖 W3Schools SQL Data Types
• 📖 Streamlit Documentation
• 📖 Streamlit st.cache_data reference
• 📖 LSE version of the AI Fluency course (LSE Digital Skills Lab)
• 📖 ONS: Why do children in smaller towns do better academically?

Useful Links

• 📦 Group Project
• 💻 W10 Lab
• 📔 Syllabus
• ✋ Contact Hours

Looking Ahead

• Easter break: Front-load your 📦 Group Project work. This is your biggest block of uninterrupted time before the deadline.
• ~20 April: Virginia Leape (WFP) visits LSE. WFP track groups should have API access and initial data collection working by then.
• 26 May, 8 pm UK time: 📦 Group Project final submission (40% group + 10% individual reflection)
• Slack: Check #announcements for updates, use your group channels to coordinate.