IN THIS ARTICLE
- Great Data Science Books
- 1. Data Jujitsu: The Art of Turning Data into Product
- 2. R Cookbook
- 3. Big Data: A Revolution That Will Transform How We Live, Work...
- 4. Programming Collective Intelligence
- 5. The Signal and the Noise: Why So Many Predictions Fail - But...
- 6. Doing Data Science: Straight Talk from the Frontline
- 7. Smart Cities: Big Data, Civic Hackers, and the Quest for a New...
- 8. D3 Tips and Tricks
- 9. Privacy in the Age of Big Data
- 10. The Data Science Handbook
- 11. A Beginner's Guide to Getting Your First Data Science Job
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Many data science tools are so intuitive and flexible that beginners often jump into data manipulation before mastering the underlying principles that should guide their work. They should be reading more data science books to understand exactly what they’re doing. No matter what your skill level, put these data science books on your summer reading list so you can learn the fundamentals of data science and find some guiding principles for your work. (You can find artificial intelligence books here.)
Great Data Science Books
1. Data Jujitsu: The Art of Turning Data into Product
By DJ Patil
Get advice directly from the Chief Data Scientist of the United States. He’s the one credited with originating the term “data science.” His book shows the difference between problems in business that are merely complex and those that are functionally impossible. This guide covers a wide range of streamlined examples, and you will find actionable advice on just about every page. Highly recommended.
2. R Cookbook
By Paul Teetor
No matter how well you know R to begin with, this introductory volume from the O’Reilly publishers will fill in the gaps and propel you forward. With a little ramp-up time, you will be creating vectors, handling a host of variables, and working on interactive data manipulations. You will quickly learn how to put together statistical models with linear regressions or analysis of variance (ANOVA). Advanced topics include statistical analysis techniques that can reveal clusters in your data.
3. Big Data: A Revolution That Will Transform How We Live, Work and Think
By Viktor Mayer-Schonberger and Kenneth Cukier
Look into the future and prepare for where big data is taking business, society, and culture. Examine how predictive models are built, as well as the questions that they will never be able to address with absolute confidence. Security, privacy, health, economic implications, and much more are treated in depth. You’ll come away with a clear picture of what your role may be as a data scientist in the big data years to come.
4. Programming Collective Intelligence
By Toby Segaran
Topics in machine learning, IoT and AI all come together in this seminal work that has been read by many data scientists before you. Even though it is nearly a decade old, you will learn timeless concepts like collaborative filtering techniques that allow online AI to make recommendations, support vector machines that match people on dating sites, decision trees for automated responses, and non-negative matrix factorization so you can identify independent features within any data set. The field has progressed quite a bit since this came out, but you have to start here.
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5. The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t
By Nate Silver
Silver is the mind behind the eerily accurate political prediction site FiveThirtyEight. Here he explores the weaknesses of weather prediction models, the accuracy of Moneyball vs. traditional big league scouts, the housing crash, and the limits of human thought. The application of Goodhart’s Law to data management is also from this book. If you can learn how to separate signal from noise in raw data, your skills will be very high in demand.
6. Doing Data Science: Straight Talk from the Frontline
By Cathy O’Neil and Rachel Schutt
You could spend thousands of dollars taking an Intro to Data Science class at Columbia University, or you could read this book based on the information in the class. O’Neill took the course and is now a senior researcher at Johnson Research Labs. Shutt is a senior VP in data science at News Corp. Together, they show how Naive Bayes, data wrangling, and financial modeling apply to real problems in the world today. A great selection among this list of awesome data science books.
7. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia
By Anthony Townsend
A great deal of money all over the world is poured into making urban spaces more livable. More than half of the world’s population lives in cities now. Existing problems, from overcrowding to pollution, will only intensify as the population swells. This book takes you behind the scenes on how data is being used now and what you need to learn for the next stage of more intelligent living.
8. D3 Tips and Tricks
By Malcolm Maclean
Data means nothing to most people. Data visualization is what matters to most people, and D3.js is the most popular way of putting interactive visualization on the web. Skills with D3 will be even more valuable as the mobile web erases website real estate. At some point in your career, you’re going to need to create or advise colleagues on animation, tooltips, tables, MySQL database interfacing using PHP, sankey diagrams, force diagrams, maps, etc. This living, evolving guide should always be within reach.
9. Privacy in the Age of Big Data
By Theresa M. Payton and Ted Claypoole
Predictive analytics for business and personal privacy are on a collision course. Businesses want to be able to provide faster-than-real-time data on consumer behavior. People want goods and services immediately but not at the cost of surveillance and heightened exposure to ID theft. Anyone working with data needs to understand the issues and the risks.
10. The Data Science Handbook
What could you accomplish if you could talk to 25 data science experts whenever you had a question? The next best thing is this compendium of 25 interviews with both experienced experts and brilliant beginners. You’ll also discover critical information on the defining characteristics of related fields like data science, statistics and data engineering.
11. A Beginner’s Guide to Getting Your First Data Science Job
The most precious resource is your brain. There’s only one of them on Earth, so you have to be very choosy in selecting the right company, team or project that will maximize your potential. With interviews, advice, and checklists, you’ll learn how to network and put together a portfolio that commands attention. This book is a great choice among the data science books here because it covers not only where to look for the best jobs, but which soft skills will make you attractive to hiring managers.
These data science books will help set you on the path to further knowledge about a burgeoning field of data science. Happy reading!
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