Data & AI track

    Interactive roadmap

    Data & AI roadmap

    Learn to turn raw data into decisions and working AI models, one step at a time.

    Your progress0% · 0 of 22 topics
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    1. Step 1 · 0/4 done

      Python for data

      The language most data work is done in.

    2. Step 2 · 0/4 done

      Data wrangling and SQL

      Get messy data into a shape you can analyse.

    3. Step 3 · 0/5 done

      Statistics and visualisation

      Understand what the data says and show it clearly.

    4. Step 4 · 0/5 done

      Machine learning

      Teach computers to make predictions from data.

    5. Step 5 · 0/4 done

      Applied AI and capstone

      Use modern AI tools responsibly and deliver a full project.

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    Every topic explained

    Step 1: Python for data

    Variables, lists, dictionaries, loops and functions.The Python tutorial (official)
    Exploring data step by step in Jupyter, mixing code, results and notes.Project Jupyter
    Reading and writing CSV, Excel and JSON files.Input and output (Python docs)
    (optional)
    Fast arrays and maths: the foundation under most data libraries.NumPy: the absolute basics

    Step 2: Data wrangling and SQL

    Loading tables, selecting rows and columns, and adding new columns.Getting started with pandas
    Handling missing values, duplicates, wrong types and inconsistent labels.Working with missing data (pandas)
    Summarising with group-by, pivot tables and merging datasets.Group by: split-apply-combine (pandas)
    Querying databases directly: filters, joins and aggregates.PostgreSQL tutorial

    Step 3: Statistics and visualisation

    Averages, spread, distributions and outliers: summarising data honestly.Statistics and probability (Khan Academy)
    Why samples vary, and how confident you can be in a result.Statistics and probability (Khan Academy)
    Checking whether a difference is real or just chance, including A/B tests.Statistics and probability (Khan Academy)
    Choosing the right chart, labelling it clearly and avoiding misleading visuals.Matplotlib tutorials · seaborn tutorial
    (optional)
    Sharing live views of data with tools such as Power BI or Looker Studio.Power BI documentation

    Step 4: Machine learning

    Features, labels, training and testing: the ideas behind every model.Machine Learning Crash Course (Google)
    Predicting numbers, such as prices or demand.Getting started with scikit-learn
    Predicting categories, such as spam or not spam, or which customers will leave.Getting started with scikit-learn
    Accuracy, precision, recall and cross-validation: knowing if a model is actually good.Model evaluation (scikit-learn)
    Why a model can look perfect on training data and fail on new data, and how to prevent it.Machine Learning Crash Course (Google)

    Step 5: Applied AI and capstone

    What LLMs are good and bad at, and how to use them in real work.Introduction to large language models (Google)
    Writing clear instructions with context and examples to get reliable results.Prompt engineering guide (Google)
    Bias, privacy and checking outputs before anyone relies on them.AI principles (Google)
    An end-to-end project: question, data, analysis or model, and a clear write-up of what you found.