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3 Great Data Science Books for Aspiring Data Scientists

If you're new to Data Science or are just making the switch from some other study area you might find it quite bewildering. You've probably got a few questions floating around in the back of your head, such as:

  • How do I get started in Data Science?
  • What should I study first?
  • What should I expect from a career in Data Science?

I suggest that the 3 books in this blog post go a long way to helping you answer these questions and get started on your life-long journey.

 

Disclosure: all links in this post take you to the listed book at your local Amazon store. We may earn an affiliate commission for purchases you make when using the links to books on this page.

You can find further details in our TCs.

 

3 Great Data Science Books for Aspiring Data Scientists

3 Great Data Science Books for Aspiring Data Scientists 3 Great Data Science Books for Aspiring Data Scientists
21 Great Data Science Books for Aspiring Data Scientists 21 Great Data Science Books for Aspiring Data Scientists 21 Great Data Science Books for Aspiring Data Scientists 21 Great Data Science Books for Aspiring Data Scientists

 

In this post - the 2nd in a series of 8 in which we bring you 21 Inspirational Books for All Aspiring Data Scientists, we highlight 3 books to introduce you to the subject of Data Science:

  • Doing Data Science: Straight Talk from the Frontline
  • Data Science For Dummies
  • Data Smart: Using Data Science to Transform Information into Insight

They are all easy-readers, very entertaining and give you a great idea of what it's like to be a Data Scientist and what tools of the trade you're going to need.

 

Enjoy!

 


 

Doing Data Science: Straight Talk from the Frontline

by Cathy O’Neil and Rachel Schutt

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

Topics include:

  • Statistical inference, exploratory data analysis, and the data science process
  • Algorithms
  • Spam filters, Naive Bayes, and data wrangling
  • Logistic regression
  • Financial modeling
  • Recommendation engines and causality
  • Data visualization
  • Social networks and data journalism
  • Data engineering, MapReduce, Pregel, and Hadoop

 

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Data Science For Dummies

by Lillian Pierson

Discover how data science can help you gain in-depth insight into your business – the easy way!

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect:

  • Provides a background in big data and data engineering before moving on to data science and how it’s applied to generate value
  • Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL
  • Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things
  • Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate

 

 

Data Smart: Using Data Science to Transform Information into Insight

by John W. Foreman

Data Science gets thrown around in the press like it’s magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It’s a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the “data scientist” to extract this gold from your data? Nope.

Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that’s done within the familiar environment of a spreadsheet.

 


 

 


 

All 8 posts in the series:

 


 

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