Check this page every week for updates on course content and requirements.
π₯οΈ Week 01 19 Jan 2026 - 23 Jan 2026
π Formative
Setup and first data exploration
Reserve 1β2 hours to set up and interact with the platforms of communication (Slack) and for coding (Nuvolos) we will use throughout the course, and to experience the kind of data analysis you will be doing in the near future.
Youβll use a Jupyter notebook to explore yearly heatwave counts, then compare your step-by-step process with an AI-only approach to understand when each method works best.
Note
No need to install anything.
All assessed work in this course can be done on Nuvolos Cloud with VS Code preconfigured. You do not need to install anything on your machine unless you prefer to.
π Support
Click here to see how to get help this week
The best place for questions is Slack #help. For scheduled support, see Contact Hours.
For extensions and admin queries, email Kevin at .
From data to insight: the importance of clean data ποΈ Slides
Introduction to course goals and assessment. Reveal solution to the formative. Discuss the AI diagnostic, when AI helps versus when it fails. Notebooks as a professional tool.
Exploring daily temperatures with pandas π£οΈ Roadmap Tutorial
Build on Week 01βs yearly heatwave data by exploring raw daily temperature records. Learn to load CSV files into pandas, apply basic DataFrame operations like filtering and sorting, and connect daily observations to the summary data you worked with in practice.
π₯οΈ Week 02 26 Jan 2026 - 30 Jan 2026
π Formative
Python foundations and file management
Complete foundational Python lessons through DataQuest, focusing on variables, data types, and collections. Learn to organise your workspace with proper folder structures and document your learning using Markdown formatting in Jupyter notebooks.
Tip
Actively post questions to Slack or attend support sessions if you get stuck.
π Support
Click here to see how to get help this week
Use Slack #help for questions. For scheduled support, see Contact Hours.
From tables to live data: pandas and one API ποΈ Slides
Connect your DataQuest Python foundations to real data analysis. Explore how collections (lists and dictionaries) relate to DataFrames from Week 01. See a live API demonstration with weather data and preview next weekβs JSON work.
From collections to DataFrames π£οΈ Roadmap Tutorial
Practice connecting the Python collections you learned in DataQuest (lists and dictionaries) to pandas DataFrame operations. Apply bracket notation and indexing skills to real data analysis tasks.
π₯οΈ Week 03 02 Feb 2026 - 06 Feb 2026
π Formative
Terminal navigation and Python control flow
Play Shell-it game to learn file system navigation, practise terminal commands for professional workflows, and complete DataQuest lessons on loops and conditionals. Create your first notebook using terminal commands, explore different file formats, and document your learning journey.
Note
Warm-up: Shell-it (paths and terminals)
Play βShell-it: The London Episodeβ at least 10 times: https://shell-it.vercel.app/. Spend one minute reflecting on paths and directories before opening the Nuvolos terminal.
Tip
Actively post questions to Slack or attend support sessions if you get stuck.
π Support
Click here to see how to get help this week
Use Slack #help for questions. For scheduled support, see Contact Hours.
Operating systems, file I/O, and Git setup ποΈ Slides
Understand how operating systems organise files and why paths matter for reproducible code. Learn about environment variables and their role in software. See a live demonstration of collecting API data and saving to JSON and CSV files. After the break, hands-on Git setup session where you create your personal repository and practise the basic Git workflow.
Control flow practice with temperature data π£οΈ Roadmap Tutorial
Apply loops and conditionals from DataQuest to real temperature analysis. Fetch July 2024 weather data from Open-Meteo API, save to JSON files, then use control flow to categorise hot days.
π₯οΈ Week 04 09 Feb 2026 - 13 Feb 2026
π Formative
London heatwave analysis
Build your first complete data pipeline: fetch historical temperature data from Open-Meteo for London (1990β2025), transform the JSON data into a usable format, and create a heatwave summary table showing yearly counts.
π’ Release
Mini-Project 1 (20%) - Weather data analysis TODO: link + deadline details
π Support
Click here to see how to get help this week
Use Slack #help for questions. For scheduled support, see Contact Hours.
Working with DataFrames ποΈ Slidesπ₯οΈ Live Demo
Understand why tidy, reproducible steps matter for data analysis. Learn how Python collections (lists and dictionaries) connect to pandas DataFrames, and practice essential operations like selection, grouping, and creating simple plots.
Data quality and transformation workshop π₯ Pair Programming
Work with real-world messy data to understand when to clean versus when to investigate inconsistencies. Practice applying transformations to datasets and learn to document your data quality decisions clearly.
Data transformation and visualisation design ποΈ Slides
Master pandas transformations including .apply() with custom functions and .groupby() aggregations. Learn principles of seaborn visualisation design and standards for communication of insights.
Visual communication workshop π£οΈ Roadmap Tutorial
Practice creating multiple chart types from the same data, learn to justify your choices for different audiences, and develop skills in writing compelling narrative titles.
π₯οΈ Week 06 23 Feb 2026 - 27 Feb 2026
π Reading Week
Focus on Mini-Project 1. Support sessions available.
β²οΈ Deadline
Submit your Mini-Project 1 (20%) via GitHub until Thursday 8 pm.
JSON normalisation and data reshaping ποΈ Slides
Learn pd.json_normalize() to flatten nested JSON structures from API responses. Explore record_path, meta, and max_level parameters. See exposure-only examples of pd.concat(), .explode(), and .melt().
Normalising nested JSON for analysis π£οΈ Roadmap Tutorial
Apply pd.json_normalize() and .explode() to real OpenSanctions data. Build a complete data transformation pipeline, then engineer a plot_df DataFrame for visualisation.
π’ Release
Mini-Project 2 (30%) - London travel time analysis TODO: link + deadline details
SQL fundamentals for data projects ποΈ Slidesπ₯οΈ Live Demo
Transform your normalised JSON outputs into relational tables. Learn how to design simple schemas, load data into SQLite, and write core queries that support Mini-Project 2 analysis.
SQL basics: from SELECT to JOIN π£οΈ Roadmap Tutorial
Build SQL skills progressively from simple SELECT statements to complex aggregations. Compare SQL queries with pandas operations to understand when each approach works best.
Methodology design and exploratory data analysis ποΈ Slides
Refine your Mini-Project 2 methodology using structured frameworks and peer feedback. Learn essential exploratory data analysis principles including validation checks, outlier handling, statistical measure selection, and correlation versus causation boundaries.
Data exploration and visualisation workshop π οΈ Tech Support
Class teachers facilitate a conversation about dos and donβts of visualisation, building on the exploratory data analysis principles from the lecture.
Form project groups, set up team repositories via GitHub Classroom, and learn Git collaboration workflows including fetch, pull, and merge conflict resolution. Configure GitHub Pages for next weekβs pitch presentation.
Group project planning and Git collaboration π₯ Group Work
Finalise group formation and repository setup. Practice branches and pull requests for professional code review workflows. Set up GitHub Pages for your pitch presentation and begin project planning discussions.
β²οΈ Deadline
Submit your Mini-Project 2 (30%) via GitHub until Wednesday 8 pm.
Project communication and management ποΈ Slidesπ₯ Group Work
Learn from professional examples of effective data storytelling. Distinguish static insights from interactive dashboards. Master GitHub Issues, Pull Requests, and Project Boards for team coordination.
Group project pitch presentations (10%) π₯ Group Work
Deliver 5-minute pitch presentations to the teaching team, answer questions about your project planning and feasibility, and receive feedback on coordination and technical approach.
