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DataCamp

Applied Statistics in Python

via DataCamp

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Overview

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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

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