If you are doing research on data analytics or business analytics you may want to look at these choices collected by the GGU Business Library. Writing a paper on this topic or finding articles about it can be difficult because it is a huge topic and much of it geared toward a technical audience. People that want to learn about data analytics from the perspective of business will find much of value here, including how to use popular applications like Google Analytics or data-visualization application Tableau (Jumpstarting Tableau was written by a professor in the master’s degree in business analytics program at GGU.). Want to dig deeper? Check out the peer-reviewed journals or technical sources on SQL and R programming on this list.
|Big Data at Work by Thomas H. Davenport (2014)|
Everybody Lies: big data, new data, and what the Internet can tell us about who we really are by Seth Stephens-Davidowitz (2017) Predictive Analytics: the power to predict who will click, buy, lie, or die by Eric Siegel; Thomas H. Davenport (2013)
How-To & Guides
Getting Started with Business Analytics by David Roi Hardoon; Galit Shmueli (2013) Data Science for Business by Foster Provost; Tom Fawcett (2013) Naked Statistics: stripping the dread from the data by Charles Wheelan (2013) The Value of Business Analytics by Evan Stubbs (2011) Big Data by Bernard B. Marr (2015) Business analytics: methods, models, and decisions by James R. Evans (2015) Data Analytics: Practical guide to leveraging the power of algorithms, data science, data mining, statistics, big data, and predictive analysis to improve business, work, and life by Arthur Zhang (2017) A Practitioner’s Guide to Business Analytics by Randy Bartlett
(2013) Key Business Analytics: the 60+ business analysis tools every manager needs to know by Bernard Mar (2016) Key Performance Indicators (KPI) by Bernard Marr (2012) Delivering Business Analytics: practical guidelines for best practice by Evan Stubbs; James Foster (2013) Google Analytics Breakthrough: From Zero to Business Impact (2016) The Big Book of Dashboards by Steve Wexler; Andy Cotgreave; Jeffrey Shaffer (2017)
Programming and Databases
Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning with Real-World Applications by Joshua Chapmann (2017) Machine learning: the ultimate beginners guide to neural networks, algorithms, random forests and decision trees made simple by Ryan Roberts (2017) Machine learning: the art and science of algorithms that make sense of data by Peter A. Flach (2012)