Certification programme
The analyst toolkit and the scientist toolkit, in the order employers actually hire for. Get employable as an analyst first, then add the modelling and deployment that moves you up.
What you will be able to do
Everything included
18 modules · 94 topics · 375 concepts
This is our Data Analytics course in full — the same 8 modules and 165 concepts, not an abridged version.
Framing a question
Metrics
Data literacy
The analysis workflow
Stakeholders
You can take an ambiguous request and return a defined, answerable question.
Cleaning
Lookups and joins
Summarising
Power Query
Statistical functions
You can clean, join and summarise a dataset entirely in Excel.
Core querying
Joins
Aggregation
Window functions
CTEs and structure
Interview SQL
You can answer a business question directly against a database.
Descriptive statistics
Distributions
Sampling and uncertainty
Hypothesis testing
A/B testing
Regression
You can quantify uncertainty and test whether a difference is real.
Choosing a chart
Design principles
Power BI
Tableau basics
Storytelling
You can build a dashboard a stakeholder reads correctly without you present.
Python essentials for analysts
pandas fundamentals
Transforming
Cleaning at scale
Visualising in Python
Exploratory analysis
You can do an end-to-end analysis in pandas and explain why it beats a spreadsheet.
APIs
Web scraping
File formats
ETL thinking
Data quality
You can acquire and consolidate data from files, APIs and web pages.
Building a portfolio
The case interview
Take-home assignments
SQL and Excel rounds
Positioning
You can present a project and answer a case interview convincingly.
This is our Data Science course in full — the same 10 modules and 210 concepts, not an abridged version.
Python for data work
NumPy
pandas
Reproducibility
Version control for data work
You can manipulate real datasets fluently in pandas and NumPy.
SQL for data science
APIs and scraping
File formats at scale
Data warehouses and lakes
Pipelines
You can assemble a dataset from databases, APIs and files yourself.
Descriptive and exploratory
Probability
Estimation
Hypothesis testing
Experiment design
Causal thinking
You can quantify uncertainty and design an experiment that answers a question.
The learning framing
Preprocessing
Regression and classification
Evaluation
Ensembles
Interpretability
You can build and validate a model without leaking information into it.
Clustering
Dimensionality reduction
Anomaly detection
Recommenders
You can segment a population and build a working recommender.
Neural network basics
Training
Regularisation
Convolutional networks
Sequence models
Practicalities
You can train a network and diagnose why one is not learning.
Classical NLP
Embeddings
Transformers in practice
Large language models
Text tasks
You can solve a text problem with embeddings or a fine-tuned model.
Why single-machine fails
Distributed processing
Cloud data platforms
Streaming in outline
You can process data that does not fit on one machine.
Packaging
Serving
MLOps
Monitoring
Cost and reliability
You can deploy, monitor and retrain a model responsibly.
Responsible use
Communicating results
Working with stakeholders
Portfolio
Interview preparation
You can present a result to non-technical stakeholders and defend your choices.
2 builds you keep
The analytics half ends in a defensible dashboard and a case read-out. The science half ends in a served model. Both are things you can show, and they show different skills.
The capstone revisits a dataset you analysed in the first half and asks what a model adds — including, sometimes, the honest answer that it adds nothing.
Hands on with
Before you enrol
Because analyst roles are where most people actually get hired first, and data science roles usually want someone who can already do the analyst work. Taking them in this order means you are employable partway through rather than only at the end.
Yes, exactly. Both pages are generated from the same source, so there is no abridged version here. You can compare the two course pages module by module and find them identical.
Some, deliberately. Statistics and SQL appear in both, at different depths. We teach the analytics treatment first and the science treatment builds on it rather than repeating it.
No. The analytics half starts with spreadsheets and SQL, and introduces Python partway through. If you would rather have the full Python course first, Data Science with Python is the other way round.
Where this leads
One skill on a CV is a hobby. These are the courses students take alongside this one, in the order that builds an actual role.
Statistics, machine learning and big data tooling, taught through projects that end in deployed models rather than notebooks. Six months, with placement preparation built in.
Everything on the modelling side, in one programme. Data science end to end, the machine learning algorithms understood rather than imported, and applied AI through to generative models and guardrails.
The full route for someone who has never programmed: Python properly first, then the whole data science programme built on it. Same syllabus as both courses taken separately, in the order that actually works.