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A/B Testing for Data Science (with Python and R)

Carlos Afonso
Instructor at NYC Data Science Academy
Panelist Spotlight

Carlos Afonso has 5+ years of experience using Data Science and Machine Learning to solve business problems for diverse organizations (from nonprofits to Fortune 100 companies) in various industries, including healthcare, finance, education, and technology.

Carlos has a multidisciplinary STEM academic and research background, with a BSc and MSc in Physics Engineering from IST, Portugal; one year of doctoral training in Computational Biology at IGC, Portugal; and experience conducting postgraduate research in Biomedical Engineering at the University of Oxford, England.

Passionate about the power of Education to transform people’s lives, Carlos has been teaching and helping others achieve their goals throughout his whole career.

A/B Testing for Data Science (with Python and R) on June 30th, 2021

Learn essential concepts used in data science including A/B testing, hypothesis testing, p-value, permutations, and fundamental coding topics in Python and R. Interested in learning Data Science but apprehensive about the statistics and coding requirements? This workshop will be an example of how you can confidently learn fundamental statistics and coding skills needed to understand and practice Data Science. In particular, you will learn the following important concepts:  
  • Statistics: A/B testing, hypothesis testing, p-value, permutations, and permutation test.
  • Coding: variables, loops, and functions.
  We start by discussing “What is an A/B test?” and “Why do we use A/B testing?”, followed by several examples of A/B tests in Data Science. Then, we answer the main question of “How is an A/B test done?” with a detailed explanation of the steps involved in the A/B testing process (using didactic animations to illustrate those steps).   Afterward, we introduce permutations and the Permutation Test as a good method to do the hypothesis testing part of an A/B test. We explain the Permutation Test process in detail, using didactic animations to illustrate the steps involved.   Throughout this process, we provide intuitive explanations for hypothesis testing and the associated statistical concepts. In particular, we provide a clear graphical/visual interpretation of what is the p-value.   Finally, we show how to code the learned concepts (from scratch) using both Python and R.   This is an introductory-level webinar perfect for anyone interested in learning Data Science. You don’t need prior experience or background in coding nor statistics.  
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