Professional Data Scientist
Build complete machine learning workflows from raw data to model validation.
At a glance
Who should enroll
Analysts, developers and quantitative professionals ready to advance.
What you will achieve
Engineer features; select models; evaluate risk of overfitting; communicate findings; build deployable workflows.
Prerequisites
Python basics and introductory statistics.
Learn. Apply. Build.
Each module builds toward a practical final project. Contents may be refined before your cohort begins.
Capstone project
Build and evaluate an end-to-end predictive model, explaining trade-offs to business stakeholders.
Sampling, inference and experimentation
Linear models, trees, ensembles
Categorical variables, leakage and pipelines
Cross-validation, discrimination, calibration, interpretability
Clustering, time series and model monitoring
Customer churn or credit risk prediction
Invest in your next career step.
Create or verify your account, select this program, and receive a secure checkout link by email.