Python for Data Science
Pandas, statistics and models that ship
What you'll learn
- Clean and reshape data that arrives broken
- Choose a model for the problem rather than the fashion
- Evaluate honestly, including on data the model has not seen
- Serve a trained model behind a monitored API
- Level
- Beginner
- Duration
- 14 weeks
- Material
- 3m
- Lessons
- 12
- Language
- English
- Access
- 365 days
About this course
Work with real, messy data: clean it, understand it, model it, and put the model behind an endpoint somebody else can call.
Most data courses stop at the notebook. This one does not. You will start with genuinely messy data, learn the statistics that stop you fooling yourself, train models that hold up on data they have never seen, and finish by serving one behind an API with monitoring on it.
Syllabus
4 modules · 12 lessons · 3m · 1 free to preview
Getting set up
- What you are going to build
- Setting up your machine
- Your first working version
The core ideas
- What you are going to build
- Setting up your machine
- Your first working version
Building it for real
- What you are going to build
- Setting up your machine
- Your first working version
Shipping and what comes after
- What you are going to build
- Setting up your machine
- Your first working version
Taught by
Gs DwiwediLead
Full-stack engineer, and the person who built this platform
Fifteen years building and running production systems, most of them in Java and JavaScript. Teaches the way he works: start with something that runs, then make it correct, then make it fast.
Data scientist — models that reach production
Spent eight years shipping models that other teams depend on, which is a very different job from winning a leaderboard. Teaches evaluation before algorithms.
What you'll get
- Lifetime accessCome back to the material whenever you need it.
- Reviewed projectsA person reads your code and tells you what to change.
- CertificateIssued on completion, verifiable by a link.
- Doubt sessionsLive weekly, recorded if you cannot make it.
Tools you'll use
- PythonThe language, properly — not just notebook snippets
- pandasReshaping and cleaning data at scale
- scikit-learnModels, pipelines and honest evaluation
- FastAPIServing a model as an endpoint
Before you start
- Basic programming in any language
- School-level mathematics
Who this is for
- Analysts moving into modelling
- Developers adding data work to their range