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.
"If you want to use Data Science to have a practical impact on
businesses (either as a current employee or someone looking to
build a career here), this book is an amazing way to get started.
"Data Science Practitioner's Guide to Data Science" offers a
refreshing perspective. It emphasizes practical skills and
real-world problem-solving over theoretical knowledge. This guide
covers everything from technical and soft skills, including project
management and communication. If you want to elevate your skills
and make a meaningful impact, I highly recommend this book."
- Mike Clarke, Director of Product Management, Shopify"As a data
scientist with nearly two decades of experience, I highly recommend
this book. Amidst the myriad publications in the constantly
evolving field of data science, "Practitioner's Guide to Data
Science" distinguishes itself as an indispensable resource for both
newcomers and seasoned professionals. The authors adeptly merge the
technical aspects of data science with practical guidance on career
development and soft skills, resulting in a well-rounded approach
to the subject. The book is precise, meticulously organized, and
easy to follow. The book encompasses a wide range of topics, from
linear regression and deep learning to data imputation and cloud
environments. It also thoroughly explores the data science project
cycle, including common pitfalls to avoid, ensuring readers are
well-prepared to confidently tackle real-world projects.
Additionally, the book delves into the data science job family,
providing valuable insights into various roles and career
trajectories. With its comprehensive approach and emphasis on
practical applications, "Practitioner's Guide to Data Science"
serves as a very useful guide for anyone aiming to excel in this
dynamic field, whether they are learning new concepts or refreshing
their knowledge."- Tianran Li, Director of Data Science, Coupang"As
a 20+ year practitioner with experience building high-performing
data science teams, I strongly recommend this book to anyone
aspiring to start or grow their career in data science. The readers
have practical access to R and Python notebooks to explore
independently. At the same time, they can review the data science
project cycle and familiarize themselves with common pitfalls. For
example, great code alone will not make a successful data
scientist, but understanding how to manage the entire project to
ensure adoption and business value creation is a differentiating
factor. The most common question I get from my mentees is about
making choices and tradeoffs as they start and build their careers.
In this book, the authors have done a great job discussing the
different roles within data science and organizational structures
that can help candidates select roles that align best with their
strengths and facilitate their career aspirations."
- Elpida Ormanidou, Analytics, and Insights Vice President,
PetSmart "Lin and Li have written an excellent book on data
science. As the title implies, it is designed for practitioners,
and combines very practical guidance on applications with sample R
and Python code, as well as providing theoretical underpinnings of
a wide variety of data science methods. Both authors combine solid
academic credentials with practical experience in leading data
science organizations, such as Google and Amazon. I found Chapters
1 and 2 to be particularly unique for data science books. While
most such texts provide some degree of introduction to the topic,
in Chapter 1 Lin and Li provide much more depth, for example by
discussing the different types of data science roles available in
business and industry. Chapter 2, on soft skills needed by data
scientists, provides some of the most important information that
future data scientists will need, in my opinion. For example, it
discusses common mistakes that are made in data science projects,
such as poor problem formulation and the use of the wrong data to
develop models. While most people tend to think of data quality as
a 'data are right' problem, the 'right data' question is just as
important, but often overlooked. I strongly recommend this book for
those planning careers in data science."
- Roger Hoerl, Associate Professor of Statistics, Union
College"Practitioner's Guide to Data Science" is a comprehensive
resource that bridges the gap between theory and practice in data
science. Drawing from their extensive industry experience, authors
Hui Lin and Ming Li provide invaluable insights into real-world
applications, career development, and the importance of soft
skills. With hands-on exercises and practical scenarios, this book
is an essential read for anyone looking to navigate and excel in
the dynamic field of data science."
- Todd Pearson, North America Commercial Data Science and
Engineering Lead, Corteva Agriscience
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