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Product details
File Size: 7082 KB
Print Length: 226 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 2 edition (October 16, 2014)
Publication Date: October 16, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00OL084UI
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Amazon Best Sellers Rank:
#134,623 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
I love this book. Not only does it illustrate the concepts well, but it's well-written (funny even) and very concise and informative. I bought it to review stats concepts and see the python programming examples, but I think it could serve as a first/ introduction to stats book as well. The author has a wonderful ability to really distill information and teach via examples. This book served me well and I still use it as a reference all the time.
I really liked this book. The author did a really good job. It's a mixture of Python and statistics so some previous background in both will allowing you to benefit entirely from reading this book. Especially prior experience with Python will help you understand the code used by the author, as it is not a simple one. He often uses wrapper functions and class inheritance, so if this doesn't ring a bell, I suggest learning a bit of Python first. Otherwise you can skip the programming parts, but I think you will lose a large part of the book's value.Statistics here is more basic than Python code, for sure. But it does serve well as a introduction to statistical analysis. A software developer wanting to start learning statistics is probably a good candidate for this book. But not the other way around. After reading it I think I still prefer to use R to generate probability density plot, than Python.Anyway, it is almost a must read for anyone on their patch to data scientist career. It not long or super expensive, so if you are interested in stats and Python, just read it.
This book is a great introduction to statistics for any person with basic programming skills. The main issue, when you tackle any math-heavy stuff - is that you are bombarded with a huge amount of formulas, but can't "feel" the practical side of the subject.In this book, you have a simple way to practically and swiftly try each new concept through Jupyter Notebook interactive examples. Transform the data, draw a chart - observe results. The fastest feedback loop you can get learning any math stuff.If you want to get the best results from the book, spend some time and setup Jupyter Notebook, or just run them directly in Google Collab service. Just reading the book without practically running all the examples just a waste of time.
I didn't read the description carefully and didn't realize this was geared for Python programmers, which I'm not. While it seems to have a good discussion of statistical comments, the fact that all examples are in Python limits its usefulness to those not familiar with the language.
What I like about Think Stats is that it is direct and to the point. It includes a case study that runs through the book and works on data available online. It provides a great starting point for exploring once you see how the given examples work. Each chapter has a handful of exercises that can get you started if you aren't sure what to do next. Downey has an easy style of writing and finds the fine line between enough information and too many details. That said, this book might be a bit thin if you don't have any experience with statistics or have access to a mentor.Keeping in mind the that the book is a focused overview, it certainly supports the programmer who is looking for hands-on examples but I believe it also is useful for the non-programmer that needs a quick understanding of the core concepts. They may not be able to do the calculations but they will be able to participate in a conversation.As it's concise and has active examples, the book would be a great supporting text for a course that requires assumes some statistics experience but doesn't need the overhead of a full-blown stats book. As I have mentioned in other reviews, this book is a good addition to the O'Reilly collection of books on data mining - Segaran's Programming Collective Intelligence: Building Smart Web 2.0 Applications, Russell's Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, and Janert's Data Analysis with Open Source Tools.
I was disappointed by this book. It purports to present stats in the Python environment, as a starter for people who are light in stats and heavy in Python. It's a thin book, but that would be fine if it delivered on its premise. The data he uses is not obtainable by a quoted link and the code in the book amounts, over many pages, to under 100 lines of Python. The statistics are bare-bones definitions that are quite well covered, better covered, in other books.I was especially interested in the book because he promised to make some of the arduous formulae more understandable in the prospect of code. No code for those parts though.
This is such an awesome book. The code Downey includes with the book is worth the price of the book.
Very accessible presentation of stats fundamentals and how to apply them. The accompanying thinkstats2 and thinkplot libraries are a common go-to tool for my work.
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