Master Data Science,
From the Ground Up
A structured, self-paced course covering statistics, machine learning, exploratory data analysis, and Python — everything you need to become a confident data scientist or pass your certification exam.
Why this course?
Most data science courses are either too theoretical or skip the fundamentals. This course is different — it gives you exactly what you need, explained clearly, with code you can run right in your browser.
Structured curriculum
From statistical foundations to Python and ML — everything in the right order.
Hands-on Python exercises
Run real Python code in your browser — no setup, no installs required.
Self-paced learning
Learn at your own speed. Your progress is saved automatically as you go.
Practice exam included
Test your knowledge with a full practice exam before your certification.
Course curriculum
7 modules · 41 lessons · First lesson of each module is free
Statistical Foundations
Python for Data Science
- ○2.1 NumPy — arrays, vectorization, and broadcastingPython
- ○2.2 Pandas fundamentals — Series, DataFrames, and I/OPython
- ○2.3 Data cleaning with PandasPython
- ○2.4 Data manipulation — groupby, merge, and reshapePython
- ○2.5 Scikit-learn — preprocessing, pipelines, and evaluationPython
- ○2.6 End-to-end project — raw data to trained modelPython
Statistical Modeling & Predictive Analytics
Exploratory Data Analysis & Insight Generation
SQL & Data Modeling
- ○5.1 Relational modeling and normalisationPython
- ○5.2 SQL fundamentals — SELECT, WHERE, JOIN, GROUP BYPython
- ○5.3 SQL for data science — EDA, aggregations, date functionsPython
- ○5.4 Advanced SQL — window functions, CTEs, subqueriesPython
- ○5.5 Dimensional modeling and star schemaPython
- ○5.6 Slowly changing dimensions — SCD 1, 2, and 3Python
Data Wrangling, ETL & Data Quality
Data Science Tutorial — End-to-End Project
- ○7.1 Data science tutorial — define the problem and load the data
- ○7.2 Exploratory data analysis tutorial — understanding the churn dataset
- ○7.3 Pandas tutorial — data cleaning and feature engineering
- ○7.4 Hypothesis testing tutorial — are churners statistically different?
- ○7.5 Logistic regression tutorial — building a churn prediction model
- ○7.6 SQL tutorial — querying the churn database
- ○7.7 Data science project tutorial — communicating your findings
What learners say
Feedback from people who completed the course.
“Exactly what I needed before my data science certification. The Python exercises made everything click.”
“The statistics module explained p-values better than any textbook I've read. Clear, concise, practical.”
“Free, well-structured, and no fluff. I went through the ML module in a weekend and learned a lot.”
Frequently asked questions
Everything you need to know before you start.
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