This book is an excellent primer on data science. It builds up concepts from scratch with code examples in Python. Whilst it uses some well-known libraries for utilities, the code that builds on the core Data Science concepts is all included and explained in the book.
I particularly enjoyed the conversational, often humorous style of the book. He gives a short introduction to NoSQL databases, then concludes: “Tomorrow’s flavour of the day might not even exist now, so I can’t do much more than let you know that NoSQL is a thing. So now you know. It’s a thing”. The author doesn’t get too stuck in jargon either – one example is his definition of a greedy algorithm: “… at each step, it chooses the most immediately best option” – perfect.
Some of the main topics covered are:
- Visualizing Data
- Gradient Descent
- Linear Regression
- Logistic Regression
- Neural Networks
Having covered the theory, the book extends to a few use cases – natural language processing, network analysis and collaborative filtering.
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