Data Scientist Path
Analysts and engineers who want to model data, run experiments, and drive decisions with science.
4 stepsHands-on labsCapstone + portfolioA named role
Assessed work, a capstone and a verified credential
What the path adds
Membership covers foundation reading. A path is where the work is assessed and the credential is earned.
- Marked work
- Your written submissions are assessed against a rubric, not ticked off.
- Cumulative assessment
- One exam across the whole path, not a quiz per course.
- Capstone
- A final piece of real work, reviewed.
- Portfolio artefact
- Something you can put in front of a hiring manager.
- Verified credential
- A Professional Path Credential with a public verification page.
By the end, you can
What this path makes you able to do.
- Apply the data science process with statistics and Python
- Build and evaluate machine learning models with scikit-learn
- Design experiments, communicate results, and deploy models responsibly
A guided journey, not a pile of courses
The 4-step path.
- 1View course
Data Science Foundations
Process, stats, and Python
- 2View course
Machine Learning for Data Science
Models and evaluation
- 3View course
Data Science in Practice
Experiments, deployment, ethics
Capstone: deliver a data science project
An end-to-end project with exploratory analysis, a trained and evaluated model, and a stakeholder writeup
Every path is backed by
Hands-on labs. A final exam & capstone. A real role.
You finish with a portfolio you can show, a cumulative exam, and the skills to land the Data Scientist role.
Not sure this is the one? Take the free fit and aptitude check first: twelve short questions, instant answer.