Practitioner’s Guide to Data Science

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. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

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

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.