Books

  • Eremenko, Kirill. Confident Data Skills: Master the Fundamentals of Working with Data and Supercharge Your Career. Kogan Page, 2018.
  • From the creator of Super Data Science, this was an easy “for fun” read, light math, and provides an overview of data analytics and careers in this field. I swear I’m not on Kirill’s payroll(!)… he’s just a data science career guru with fresh perspectives. Amazon link
  • Provost, Foster and Fawcett, Tom. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc., 2013.
  • This was a textbook for Foundations of Data Science, an UCI graduate course that I recently completed. Examples in this textbook are denser and math-heavier than Confident Data Skills. This would be most appropriate for anyone actively trying to move into a data science role. Amazon link
  • O’Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing, 2016.
  • O’Neil argues that the predictive algorithms being used today are opaque, unregulated, and uncontestable, even when they’re wrong. She builds upon her career experiences as a Quantitative Engineer and aims to educate data scienctists about how to take more responsibility for their algorithms. Amazon link
  • Murray, Scott. Interactive Data Visualization for the Web: An Introduction to Designing with D3. O’Reilly Media, Inc., 2nd Edition, 2017.
  • Ideal for beginners, Scott Murray takes you through easy-to-consume fundamental concepts and methods of D3, the most powerful JavaScript library for web-based visualizations. Amazon link
  • Note: This 2nd Edition (October 2017) release was updated with D3’s v4 syntax and D3 v5 was released in April 2018. While some of the syntax in this 2nd Edition may be rendered obsolete, it’s still a valuable resource for learning the fundamentals of D3 and data design.
  • Abbott, Dean. Applied Predictive Analytics. Wiley, 2014.
  • This book is ideal for tech-savvy business and data analysts, and serves as a solid bridge between academia and professional scenarios. Amazon link
  • Burkov, Andriy. The Hundred-Page Machine Learning Book. Andriy Burkov, 2019.
  • This is one of my favorite reference books. It is best suited for beginners already comfortable with analytics terminology. In the author’s words, this is an “attempt to write an easy-to-read book on machine learning that isn’t afraid of using math,” and it is “also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.”
  • If you know the basics of R or Python and want to push your predictive modeling and ML education to the next level, you’ll love how succinct this book is. Amazon link
  • Foreman, John. Data Smart: Using Data Science to Transform Information into Insight. Wiley, 2013.
  • Contrary to popular belief (especially among us data folks), not every problem is a big data problem that requires programming via R or Python. This book explores the full capabilities of Excel for data science, and does a great job at it. Espcially for Excel power users, this book would be a first great step before plunging into R or Python (or vice versa, perhaps you’re an avid R user that barely knows Excel… this book would be great for you too). Amazon link