DSM
ProgrammingBeginner

Python for Data Science

Start from absolute zero. By the end you'll write clean, idiomatic Python that any data team would be proud of.

32h estimated36 lessons2240 XP total
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Learning journey

02

Section 02

Control Flow

Teach your programs to make decisions and repeat work — the conditionals and loops behind every data-cleaning script.

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03

Section 03

Functions

Package logic into reusable, testable functions — the unit of abstraction every data pipeline and transformation is built from.

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04

Section 04

Data Structures

Master the containers that hold real data — tuples, dictionaries, sets, and nested structures that map directly onto rows, records, and JSON.

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05

Section 05

Object-Oriented Python

Model data and behaviour with classes — the pattern behind pandas DataFrames, scikit-learn estimators, and every library you'll soon use.

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06

Section 06

Errors & File I/O

Handle failure gracefully and move data in and out of files — the resilience and reading skills every real dataset demands before analysis.

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07

Section 07

Python for Data Science

Bridge core Python to the data stack — packages, NumPy arrays, dates, and regex — then tie it all together in a first end-to-end pipeline project.

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More coming soon

Control Flow, Functions, Data Structures — this track is actively expanding.

Skills you're building

VariablesAhead
Data TypesAhead
Type ConversionAhead
Python ListsAhead
NumPy ArraysAhead
VectorizationAhead

What Python unlocks

📊Pandas — data analysis
🔢NumPy — numerical computing
🤖Scikit-learn — machine learning
🧠TensorFlow / PyTorch — deep learning

Where this leads

Python for Data ScienceNow
Data Analysis with Pandas
Machine Learning

First lesson · 25 mins · +50 XP

Variables & Data Types