Data Science for Business: What you need to know about data mining and data-analytic thinking
by Foster Provost and Tom Fawcett
I started reading Data Science for Business back in May, 2018 and it took me a few months to get through this one. It’s advertised as an introduction to Data Science concepts and techniques. I would say that those who are looking to broaden their knowledge into Data Science would benefit from having some familiarity with data and analysis as it dives straight into techniques and concepts without much foundation. A background in data, or studying data simultaneously would be of great benefit. However, for the keen developer or business associate looking to understand what Data Science can offer, this will be a good stop-gap. The engineer will likely be left understanding a bit more of t’Why’ , but less of the ‘How’.
The book does a really good job of framing it’s knowledge in a business sense, absolutely vital for understanding real world applications of Data Science. After all, the purpose of Data Science is to add value to the business. Each concept will generally have a real-world everyday business example to keep things relevant, while also bridging the gap between business and technology terminology, crucial for those learning.
I felt it was a very valuable deeper dive into the world of Data Science. Particularly the early and latter chapters on the business benefits and analytical thinking mind-set.
Other topics covered include;
- Predictive modeling
- Fitting a model to data
- Avoiding Overfitting
- Similarity, Neighbours and Clusters
- Visualising performance
- Evidence & Probabilities
- Text mining