Credit: Data Science Central
Bill Schmarzo, also known as the “Dean of Big Data” is CTO at Hitachi Vantara, and former CTO at Dell EMC. He authored a series of articles on analytic applications, and is on the faculty of TDWI teaching a course on “Thinking Like A Data Scientist”. Bill is the author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science.” Bill is also an Executive Fellow at the University of San Francisco School of Management, and Honorary Professor at NUI Galway at NUI Galway J.E. Cairnes School of Business & Economics.
Below are some of hist most popular articles posted on DSC.
- Identifying Variables That Might Be Better Predictors
- What tomorrow’s business leaders need to know about Machine Learning?
- Design Thinking: Future-proof Yourself from AI
- Lessons in Becoming an Effective Data Scientist
- It’s Not Digital Transformation; It’s “Intelligence Transformation” We Seek
- Why Is Data Science Different than Software Development?
- The 3 Phases of Digital Transformation
- 5 Steps to Building a Big Data Business Strategy
- Can Design Thinking Unleash Organizational Innovation?
- Economics, Monetization and the “New Order” Automobile Industry
- Understanding Type I and Type II Errors
- Importance of Metadata in a Big Data World
- Data Lake Business Model Maturity Index
- Using Confusion Matrices to Quantify the Cost of Being Wrong
- Crossing the Big Data / Data Science Analytics Chasm
- Using Data Analytics to Prevent, Not Just Report
- Understanding Behavioral Economics to Change Behaviors with Big Data
- Data Engineers: Nobody Puts Baby in a Corner!
- The Future Is Intelligent Apps
- Golden State Warriors Data Analytics Exercise
- The Danger of Pursuing Customer 360 View
- Blockchain + Analytics: Enabling Smart IOT
- 3 Theorems on the Economic Value of Data
Other DSC Resources
- Book and Resources for DSC Members
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
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