Hui Lin, Ming Li

Practitioner's Guide to Data Science (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 402
• Erscheinungstermin: 24. Mai 2023
• Englisch
• ISBN-13: 9781351132909
• Artikelnr.: 67821241

Autorenporträt

Hui Lin is currently a Lead Quantitative Researcher at Shopify. She holds MS and Ph.D. in statistics from Iowa State University. Hui had experience across different industries (traditional and high-tech). She worked as a marketing data scientist at DuPont; the first data hire at Netlify to build a data science team, and a quantitative UX researcher at Google. She is the blogger of https://scientistcafe.com/ and the 2023 Chair of Statistics in Marketing Section of American Statistical Association.

Ming Li is a Director of Data Science at PetSmart and an Adjunct Instructor of the University of Washington. He was the Chair of Quality & Productivity Section of the American Statistical Association for 2017. He was a Research Science Manager at Amazon, a Data Scientist at Walmart and a Statistical Leader at General Electric Global Research Center. He obtained his Ph.D. in Statistics from Iowa State University at 2010. With deep statistics background and a few years' experience in data science, he has trained and mentored numerous junior data scientists with different backgrounds such as statisticians, programmers, software developers, and business analysts. He was also an instructor of Amazon's internal Machine Learning University and was one of the key founding members of Walmart's Analytics Rotational Program.

Inhaltsangabe

1. Introduction 2. Soft Skills for Data Scientists 3. Introduction to The
Data 4. Big Data Cloud Platform 5. Data Pre-processing 6. Data Wrangling 7.
Model Tuning Strategy 8. Measuring Performance 9. Regression Models 10.
Regularization Methods 11. Tree-Based Methods 12. Deep Learning Appendix A.
Handling Large Local Data Appendix B. R code for data simulation