Applied Statistics for Data Science

Description

Welcome to the Leanpub Launch video for Applied Statistics for Data Science: from visual diagnostics to drift detection https://leanpub.com/applied-statistics-for-data-science by Gal Arav! 0:00 Introduction to Applied Statistics for Data Science and who the book is for 0:51 Gal Arav's background in Applied Math, Operations Research and Decision Theory at Tel Aviv University 1:38 Early career work at NASA analyzing pilots' visual gaze using statistical methods 3:21 The 2012 AlexNet deep learning breakthrough and its impact on data science 6:30 Leading GM's EV battery lab using machine learning to detect thermal runaway in lithium-ion batteries 9:34 Overview of the book's foundational chapters covering probability, distributions, sampling, and hypothesis testing 10:27 Fisher's experimental design philosophy and the ANOVA framework for separating signal from noise 11:15 Advanced topics: resampling methods, bootstrap, permutation tests, drift analysis, and survival analysis 12:03 Book launch pricing, current 60% completion status, and timeline for finishing 12:52 Plans for a second book covering the history of probability and statistics About the Book Applied Statistics for Data Science is a practical guide to the statistical foundations every analyst and researcher needs to work confidently with real‑world data. Instead of overwhelming readers with theory, this book builds understanding through visual intuition, simulation and accompanying hands‑on Python notebooks. For details, check out my new site at: qikly.com What You Will Learn - Build a strong foundation in probability models, distribution families, and statistical intuition - Understand sampling, estimators, and the core ideas behind uncertainty and variability - Perform hypothesis tests, group comparisons, and regression diagnostics to support sound statistical reasoning - Design experiments, sampling strategies, and resampling‑based inference workflows - Detect and monitor data drift using both parametric and nonparametric methods - Analyze survival curves, reliability patterns and time‑to‑event behavior in dynamic systems About the Author For more about my work, visit my new site at qikly.com Gal Arav is a data scientist with a wide‑ranging career spanning industry, research and entrepreneurship. He has worked on data‑intensive projects at NASA, Google, Verizon, AT&T, and General Motors. Most recently, he managed one of GM's EV battery laboratories to prevent thermal runaway and fire hazards using machine learning algorithms and previously led data science work for autonomous vehicle triage and simulation. His contributions at GM earned him the company’s Critical Technical Talent Award. Earlier in his career he founded an internet‑based market research company that was featured in Barron’s and Bloomberg Businessweek, and later went on to explore fintech, working as a quantitative researcher in global currency markets. His work with NASA included developing and installing eye‑tracking systems at Langley Research Center. He also collaborated on fMRI medical research at leading hospitals and with Cornell and Duke Universities as part of his work at Applied Science Laboratories, a pioneering eye‑tracking company founded by two MIT professors. Another highlight was his onsite work at AT&T’s Kansas headend facility, where he integrated award winning video processing algorithms for cable broadcasting services he developed. Gal holds a master’s degree in applied mathematics from Tel Aviv University, specializing in Operations Research and Decision Theory. He is deeply curious about the rapid evolution of machine learning and artificial intelligence and has a keen interest in how ideas and actions across history have shaped the course of human progress. Outside of work he enjoys tennis, swimming, climbing and hiking with his kids and dog. Follow the author here! https://x.com/GA4198498563411 Thank you for watching, please like and leave a comment, we'd love to hear from you! Please Subscribe and Follow! YouTube: https://www.youtube.com/leanpub X: https://x.com/leanpub Instagram: https://www.instagram.com/leanpub Facebook: https://www.facebook.com/leanpub Create Your Own Leanpub Book! You can create your own book anytime here: https://leanpub.com/create/book Here's the tutorial showing how to write and publish a Leanpub book in your browser (it's free!): http://help.leanpub.com/en/articles/2932527-getting-started-writing-a-book-in-the-web-browser-writing-mode If you're a Leanpub author and you'd like to submit your own Launch video for us to publish, or if you'd like to record a Launch video with Len, please go here: https://leanpub.com/launch. #books #leanpublishing #selfpublishing #leanpub #writing #machinelearning #datascience #DataEngineering #python #DataScience #AppliedStatistics #HypothesisTesting #DataDrift #PythonStatistics