Operating Data Science: Turning Rocket Science into Real Money
summary
How to build an operational model that turns Data Science from “rocket science” into measurable revenue by aligning Data Warehousing, BI, MIS, Advanced Analytics and Big Data efforts. Describes putting a data scientist to work on the right tasks, fixing missing/poor variables, and delivering well-treated data at the right place and time to generate real business value using IBM consultancy approaches.
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chapters (24)
- 0:00Introduction
- 1:23Apply within a Data Science context
- 2:44Implement 200 problems
- 3:23The ungoverned way
- 3:54Is it going to be like this?
- 5:02Where do I start teaching math?
- 5:35Do you have to work well with people to get things right?
- 6:39How to generate value from data?
- 7:58The secret sauce
- 8:38Digest the sites
- 8:58Called a donkey
- 9:09Cultural change
- 9:34Show me the money
- 9:51Ideas
- 10:23Manipulating data
- 11:59Measuring value
- 12:45Fast cycles
- 13:25Truck project
- 15:01Value management
- 15:16Identification
- 15:47Methodology
- 17:00Use case
- 17:28Recommendation
- 19:25Is the data scientist a superhero?
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