Leanpub Book LAUNCH 🚀 Applied Statistics for Data Science: from visual diagnostics to drift detection by Gal Arav
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.
Welcome to the Leanpub Launch video for Applied Statistics for Data Science: from visual diagnostics to drift detection by Gal Arav!
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.
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