You’ll find a large selection of books available on the topic of data analytics and business intelligence, but how do you choose the ones that will best support your career? Capella University faculty members provide their top five recommended books that every data professional should read.

 

1. Signal: Understanding What Matters in a World of Noise by Stephen Few (2015)

Stephen Few is a data visualization guru who teaches practical techniques for analyzing and presenting quantitative information. In this age of big data, organizations are implementing new technologies in order to increase the amount of information they can collect and store. However, the vast amount of collected data makes it harder to find important bits of information within.

Few provides straightforward instruction on how to differentiate useful information (signals) from the noise. He teaches readers how to apply statistics and visual methods to gain comprehensive understanding of data, and encourages professionals to look for ways to detect changes in the patterns that characterize data.

Additional Stephen Few books that are worth reading include:

 

2. The Visual Display of Quantitative Information by Edward Tufte (2001)

This book covers statistical graphics, charts, and tables, discussing practices and theories behind some of the best and worst statistical graphics. The author discusses how to communicate statistical data through the simultaneous presentation of words, numbers, and statistics in a precise manner optimal for quick analysis.

 

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3. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners by Jared Dean (2014)

Big data shapes critical decision-making processes in the business world. In order for organizations to maintain profitable business, they must learn to utilize big data to its full potential.

This book is a comprehensive resource for technology and marketing executives looking to drive efficiency and produce positive results. Dean provides an engaging overview of the current state of data analytics and growing trends towards high performance analytics tools.

 

4. R for Everyone: Advanced Analytics and Graphics by Jared P. Lander (2013)

Lander’s book introduces the open source R language for building statistical models, offering extensive hands-on practice and sample code. Readers will download and install R, navigate the environment, master basic program control, import and manipulate data, and practice several essential tests. Even non-statisticians will easily acquire the foundation necessary to construct several of their own models and use data mining techniques.

Lander’s intuitive guide allows any data professional to understand and write R programs to tackle all manner of statistical problems.

 

5. Principles of Data Integration by AnHai Doan, Alon Halevy, Zachary Ives (2012)

This book provides a thorough introduction to the theory and concepts of today’s data integration techniques, including tips for application. Data integration is the problem of extracting data from multiple sources, whether it is across a large enterprise, query processing on the Web, coordination between government agencies, or collaboration between scientists.

Through a range of data integration exercises, readers will learn how to build new algorithms and implement data integration applications.

To become a leader in data analytics, it’s important to know how to find the significant kernels of information, present them persuasively, and creatively solve business challenges. These recommended books will provide you with some useful tools to help you get ahead in a growing field.

 

Capella offers a variety of programs in data analytics and business intelligence including:

 

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