Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

If you already have some (minimal) python experience under your belt and want to have a detailed walk-through of a Data Science project, this is the book for you. Steve goes in depth on the what, why and how when it comes to exploring, cleaning, modeling and presenting a project in a business setting. He does a very good job of explaining what exactly is happening in the code used throughout the book. This really made the book great for a beginner/intermediate data scientist like myself. These explanations really helped me get a better understanding of pandas and scikit-learn. The best part about the book is that he goes into detail on the Machine Learning process and explains how the models work without getting too "mathy." There is some math at some parts, but hey, its ML and this was a great intro to the math and intuition of some important ML algorithms. One final reason to buy the book: I had trouble with some of the code in the book due to unknowingly having outdated versions of the necessary packages, so I emailed Steve directly, half expecting to never get a response. However, Steve responded with a very detailed explanation and code to help me get my code running. It was great. Buy this book if you want to learn how to work through Data Science Projects in Python.

This book teaches you the best practices of data science and machine learning based on real world case studies. I found this highly valuable because you are able to actually work on real data sets. This is also a quick way to learn industry recognized tools and mathematical concepts that are actually being used by data scientist. Another advantage of this book in my opinion is the author's approach for coding. Author writes and explains each code and outcome separately rather than giving you several paragraphs of code and explain them all at once. I strongly recommend this book if you want to learn data science and machine learning on a practical level applying code and assessing the outcome

The book was pretty good in general, I liked the detailed work done in all the process from exploratory data analysis to the creation and evaluation of the different models created in the book. Also, I believe the explanation on how the different model work with scikit-learn was pretty good. I would like it to include unsupervised methods as k-means as well, but in general a solid book.

I liked this book better because it broke through some other books' lectures and abstracts and dove into the kind of data and scenarios that I am more likely to actually encounter in my job, rather than just memorize them. Plus I didn't have to fix or workaround outdated or outversioned python code as I have had to do with some online teaching forums. This book will move your career or business forward.

Data science is not a simple subject, but this book did not clarify or illuminate anything further for me. It may work for individuals with extensive background in statistics, but other works seem to be more clearly written. If you must use this, supplement heavily with online tutorials.

The book is very well written and author did a good job explaining every line of codes and concepts. Worth every penny! Thank you!

Written in a very understandable fashion. Takes the reader through real life data problems in useful ways.

This book is great because it explains concepts well and uses real-world examples. However, the only machine learning free part was the first chapter.I would recommend this book to someone who would like learn about machine learning, not data analysis or visualization.

This could have been a good book but it relies heavily on examples that are unengaging and uses references to colour keyed datasets in graphs that are printed in black and white. Sloppy editing and a generally disappointing book, would not recommend.

Livre bien fait.