Five Must-reads for Data Scientists

Need a reading suggestion for that rainy afternoon? Well if you are an aspiring Data Scientist, then here is a list of books that you definitely need to read:

1.     The Data Science Starter Kit

This gives you the tools you need to get started with data from basic statistics to machine learning and new ways to think about visualization. And if you’re already experienced with data, the Starter Kit will push you further. The package includes (13) titles on R, data analysis, Python, machine learning, and visualization. One could also look at purchasing a singular book depending on the need:

  • Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt
  • Data Science for Business by Foster Provost, Tom Fawcett
  • R Cookbook by Paul Teetor
  • Machine Learning for Hackers by Drew Conway & John Myles White
  • R Graphics Cookbook by Winston Chang
  • Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
  • Agile Data Science: Building Data Analytics Applications with Hadoop by Russell Jurney
  • Bad Data Handbook by Q. Ethan McCallum
  • Data Analysis with Open Source Tools By Philipp K. Janert
  • Mining the Social Web, 2nd Edition by Matthew A. Russell
  • R in a Nutshell, 2nd Edition by Joseph Adler
  • Interactive Data Visualization for the Web by Scott Murray
  • Feedback Control for Computer Systems by Philipp K. Janer

2.   Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

One of the most influential personalities in this domain, Eric Siegel talks of the power and perils of prediction in this entertaining book by including case studies from across the globe. Meant for the common man, the book explains predictive modeling and its basics in lay man terms.

Perfect for new data scientists, Predictive Analytics offers tangible and easy-to-understand insights into the complex world of data analysis. Read this book to find out how institutions are increasingly predicting human behavior – whether you’re going to click, buy, lie, or die, as the title suggests. Predictive Analytics also shares the “why” and the “how” of behavior prediction – highlighting the many ways in which predictive analysis is able to improve healthcare, fight crime and boost sales – all through the careful analysis of big data.

3. Big data: A revolution that will transform how we live, work and think by Mayer-Schonberger and Cukier

Aimed to give comprehensive understanding of big data to people new to the subject, this book shows us how enormous, complex and messy collections of data can be used to predict everything from shopping patterns to flu outbreak. It provides a highly detailed introduction to the emerging science of big data, while also uncovering some of the most pressing issues related to both its current and future applications. Exploring big data in business, health, politics and more, you’ll learn all about how big data is transforming the way we process the information around us. Big Data also reveals the threats of data science, including the pervasive erosion of personal privacy. Overall, the book offers a strong introduction to the big data revolution and is an excellent resource for budding data scientists exploring the field.

4.  The Signal and the Noise: The Art and Science of Prediction by Nate Silver

Political forecaster Nate Silver recently won a lot of accolades for his accurate prediction of the results of every single state in the 2012 US election. In this book, he reveals how one can develop better foresight in this uncertain world. From the stock market to the poker table, from earthquakes to the economy, he takes us on an enthralling insider’s tour of the high-stakes world of forecasting, showing how we can use information in a smarter way amid a noise of data – and make better predictions in our own lives. Without accurate methods, the sheer abundance of data can make predictions go bad, especially when confronted with the limits of human cognition. Read ‘The Signal and the Noise’ to find out how forecasters are able to overcome biases and unpredictability to uncover accurate, meaningful predictions in a vast sea of noisy data.

5. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas Davenport

One of big data’s most fascinating applications is in the world of business. Big data can illuminate decision making, improve customer relationships and streamline organizations. Thomas Davenport‘s Big Data at Work explains the opportunities, impact and critical factors for successfully using big data in business. This book is an excellent guide for businesses interested in harnessing the power of big data, illustrating how leading organizations are using data science to improve the ways they do business.

While there a plenty more authors and books one can look into, the above list is comprehensive in terms of covering many basic fundamentals and for amazing case studies. We hope this proves useful!

Interested in a career in Data Science?
To learn more about Jigsaw’s Data Science with SAS course – click here.
To learn more about Jigsaw’s Data Science with R course – click here.
To Learn more about Jigsaw’s Big Data Course – Click here

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