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Top (max 10) reviews: Data Science from Scratch: First Principles with Python.

4.0 out of 5.0    96 total reviews.

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5.0 out of 5.0 -

by TxF on July 7, 2015

This is a great book-- well written, easy to digest and informative. I've been in Data Mining and Statistical Analysis for a little over a decade now; I was looking for a book to share with my team to ensure we were all up-to-speed on some foundational concepts: this book is it. EDIT: I also forgot to mention, it has probably the best get-up-and-running in Python introduction I've seen (see, e.g., Chapter 2, ~20pp.)
It's the right size and correct coverage for the content and the author's sense of humor (indeed, that of a data scientist) resonates with the audience.
Solid introduction, even better review or brief explanation of commonly encountered topics.
One of the best O'Reilly books I've read in a long time-- in fact, a technical book at the level I used to expect from O'Reilly.

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by Rahim on May 4, 2015

The book is well-written and covers a wide range of topics related to data science and machine learning. Things I like about it:
- The range of topics covered is wide, and includes (i) an intro/refresher for Python, (ii) statistics/probability, (iii) several ML techniques, (iv) data manipulation
- For each topic, there is enough explanation of the underlying theory, as well as pointers to further reading
- For each topic, the author builds up the code in simple steps, so it's easy to follow along
- Everything is explained very clearly; there is enough precision, without difficult formal language
- The explanation of eigenvector centrality is awesome

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by Garabato09 on Aug. 21, 2015

Excellent to get an introductory approach to statistical and machine learning using python.

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by Jeff Brown on Aug. 12, 2015

very good book, a lot of details of implementations

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by Rutu Mulkar on Aug. 20, 2015

Good beginners guide to Data Science

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by UN HA KIM on Nov. 16, 2015

Good introduction to data analysis.
The clear syntax grammar of Python helps a lot to clarify the meaning of author.

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by Public name on Feb. 24, 2016

In my view, too many people want to be data scientist and use advance statistical techniques that they don't fully understand. I believe Grus closes this gap with this fun introductory book to some of the basic techniques in data analysis. If you don't come from a statistical background (MS/PhD), then you should start with this book and move on to other texts such as Pattern Recognition and Neural Networks by Brian D. Ripley for a thorough review of classification methods, or the more popular and modern The Elements of Statistical Learning By Hastie, Tibshirani and Friedman.

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by Ira Laefsky on May 13, 2015

This book emphasize excellent pedagogy and understandable Python code. The basis of all programming and mathematical algorithms is given with only an assumption of minimal prior programming and high school mathematics. The basics of Data Science including: 1. A 20 page Clear Introduction to Python 2, 2. An introduction to Linear Algebra (described by Python Functions) 3. A Similar Introduction to Practical Statistics.
Like most scientific programmers who use Python the 2.6/2.7 branch is used throughout given the availability of appropriate libraries (like the Anaconda distribution). Tools for each type of algorithm are prototyped "from Scratch" in the author's own exemplary code with references to the professional libraries in the final chapters. Math is for the most part taught from code rather than mathematical notation.
Highly Recommended

5.0 out of 5.0 -

by bandar on Oct. 11, 2015

Good knowledge but he need to write down the original algorithms in every function the writer use that algorithm. However it is very good and I use most of his ideas in most of my projects

4.0 out of 5.0 -

by Howard on July 30, 2015

Great starting point to further understand basic principles. Rarely rate items as a 5 unless "wowed." This is not a "wow" item hence the 4-star.