Certification programme
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.
What you will be able to do
At a glance
Full syllabus
10 modules · 51 topics · 210 concepts
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.
5 builds you keep
Over-forecast and you waste stock, under-forecast and you lose sales, and the two costs are not equal. Time-aware validation, a seasonal baseline to beat, and a loss function that reflects the asymmetry.
Collaborative filtering on real interaction data, with a cold-start strategy and an evaluation that does not quietly use future information to predict the past.
Chunking, embeddings, a vector store and a grounded answer that cites its source. Includes measuring how often it answers from the documents rather than from the model.
Take a model you built earlier, measure how its error rate differs across groups, and decide what to do about it. The uncomfortable project, and the one that comes up in interviews.
Trained pipeline, containerised API, drift detection, and a dashboard showing prediction volume and input distributions over time. Runs without you, and tells you when it stops being trustworthy.
Hands on with
Before you enrol
Analytics explains what happened using SQL, visualisation and statistics. Data science predicts what will happen and puts that prediction into a system. This course assumes you can already program; the analytics course does not.
For this material, yes. Statistics, machine learning, deep learning and deployment each need real time, and the shorter programmes get there by skipping validation discipline and deployment, which are exactly the parts interviews probe.
Yes, at least basic Python: functions, loops, lists and dictionaries. If you do not have that, our Python course is the right starting point and makes this course far easier.
The course is built to run on free tiers and free notebook GPUs, and we show you how to stay inside them. If you choose to deploy something permanently, there may be a small cost, and we are explicit about what that would be before you commit.
No course can honestly guarantee that, and we will not. What we do is build the portfolio and interview skills the last module covers, because the gap for most candidates is not knowledge but being able to defend a project under questioning.
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.
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.
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.
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.