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Applied Statistics in Python

Dive into the world of Python-based statistical analysis with this comprehensive track designed to equip you with the essential skills to make data-driven decisions. With its user-friendly syntax and robust libraries, Python has become a favorite tool for data scientists and statisticians. Whether it’s improving business strategies with A/B testing, drawing conclusions from companies' data, or developing probabilistic models in Bayesian frameworks, you will be well-prepared to tackle challenges in any industry. Start this track today to elevate your capabilities and become a critical contributor to any data-centric team.

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1
Experimental Design in Python

Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

4 hours

James Chapman Headshot

James Chapman

Curriculum Manager, DataCamp

2
A/B Testing in Python

Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.

4 hours

Moe Lotfy, PhD Headshot

Moe Lotfy, PhD

Principal Data Science Manager

3
Foundations of Inference in Python

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

4 hours

Paul Savala Headshot

Paul Savala

Assistant Professor of Mathematics

4
Bayesian Data Analysis in Python

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

4 hours

Michał Oleszak Headshot

Michał Oleszak

Machine Learning Engineer

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