Coursera Plus Annual is 40% Off — Ends June 29.

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

DataCamp

Applied Statistics in Python

via DataCamp

Go to class Write review

Overview

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.

Syllabus

  • Experimental Design in Python
    • Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
  • 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.
  • 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.
  • 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!

Taught by

Michał Oleszak, Paul Savala, Moe Lotfy, PhD, and James Chapman

Reviews

Start your review of Applied Statistics in Python

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.