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Data Science & ML track

Python

Scientific Python stack: environments, typing, numpy-style thinking.

6 concepts3 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

Python for Data Science Setup

Concept · Hands-on lab included

2

Python Fundamentals Review

Concept · Hands-on lab included

3

Data Structures: Lists and Dicts

Concept · Hands-on lab included

4

Comprehensions and Built-ins

Concept · Hands-on lab included

5

Functions and Modules

Concept · Hands-on lab included

6

Jupyter Notebooks and Reproducibility

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

Build a Reproducible Env

20 min · beginner · Python for Data Science Setup

Lists, Dicts and Sets in Anger

25 min · beginner · Data Structures: Lists and Dicts

Comprehensions at Scale

25 min · beginner · Comprehensions and Built-ins

Continue

Next modules

Keep your learning streak going with related modules.

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Mathematics

Linear algebra, calculus and notation needed for machine learning.

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Statistics

Distributions, estimators, hypothesis testing and statistical inference.

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NumPy

Array programming, broadcasting, vectorization and linear algebra ops.

DevOpsForge - Data Science & ML · Python
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