Certification programme
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.
What you will be able to do
Everything included
20 modules · 100 topics · 412 concepts
This is our Python course in full — the same 10 modules and 202 concepts, not an abridged version.
Installing Python
Running code
Virtual environments
Packages
You can run Python from the terminal and manage a project environment.
Variables
Numbers
Booleans and None
Type conversion
Input and output
You can predict the type of any expression and convert between types safely.
String basics
String methods
Formatting
Escape and encoding
Regular expressions
You can slice, search and reformat text without reaching for a loop.
Conditionals
Loops
Loop control
Comprehensions
Iterables and generators
You can express any loop idiomatically rather than translating from C.
Lists
Tuples
Dictionaries
Sets
Choosing a structure
You can pick the right structure and explain its lookup cost.
Defining functions
Argument handling
Scope
Functional tools
Modules and packages
You can write functions with clean signatures and organise them into modules.
Classes and instances
Methods
Dunder methods
Inheritance
Data classes
You can design a class with proper state and behaviour, and know when not to.
File handling
Structured formats
Exceptions
Useful standard modules
Logging
You can read and write real file formats and handle failure deliberately.
HTTP and APIs
Web scraping
Tabular data
Cleaning
Databases
You can pull data from a file, an API or a web page and reshape it.
Command-line tools
A web application
Testing
Version control
Deploying
You can build a small web application and put your work on GitHub.
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 five Python projects build the fluency the data science projects assume, so by the time you reach forecasting and deployment you are not also fighting the language.
Data you sourced and cleaned yourself, a model you validated honestly, served behind an API with drift monitoring. The thing you walk an interviewer through.
Hands on with
Before you enrol
The syllabus is identical — literally the same modules, because both pages are generated from one source. What you get here is the sequence, one enrolment, and a capstone that spans both rather than two unconnected final projects.
If you are comfortable with functions, dictionaries, files and pip, take Data Science on its own. If any of those made you hesitate, take this. The Python track here is the full course, so there is no gap.
It is, and we would rather say so than compress it into something that does not work. Data science on top of a language you cannot yet write is the most common way people stall. If the length is the problem, Data Analytics is a shorter route into a data role.
You need to be willing to work at it. The statistics and machine learning modules build what they need rather than assuming it, but this is the more mathematical of the data paths. Data Analytics asks less.
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.
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.