- Paperback: 368 pages
- Publisher: Wiley; 1 edition (November 13, 2017)
- Language: English
- ISBN-10: 1119126762
- ISBN-13: 978-1119126768
- Product Dimensions: 5.9 x 0.9 x 8.9 inches
- Shipping Weight: 1.1 pounds (View shipping rates and policies)
- Average Customer Review: 10 customer reviews
- Amazon Best Sellers Rank: #467,571 in Books (See Top 100 in Books)
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Python for R Users: A Data Science Approach 1st Edition
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From the Back Cover
The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python
The first book of its kind, Python® for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R.
Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing.
- Features a quick-learning format with concise tutorials and actionable analytics
- Provides command-by-command translations of R to Python and vice versa
- Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages
- Offers numerous comparative examples and applications in both programming languages
- Designed for use for practitioners and students that know one language and want to learn the other
- Supplies slides useful for teaching and learning either software on a companion website
Python® for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics.
About the Author
A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.
Top customer reviews
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1. Lack of comprehensive command reference.
The author seems to be proud of his highly-accessed slides that compare R and Python side-by-side. I expected that this book would be an extended version of the slides. But this book doesn't have such a one. The index looks incomplete and is not very useful.
2. Poor printing quality.
The book has a lot of screenshot images. Some of the letters are not even readable due to poor printing quality.
3. Poor layout.
The book has hundreds of pages raw computer output instead of self-contained explanation. What is annoying is the pages sometimes rotates by 90 degrees. See the pictures.
4. Shallow and useless explanation of statistics/machine learning.
I understand the author's desire to view himself as a professional data scientist, but who would expect his armature lecture on causality and correlation in this book? It is completely unnecessary. He could have focused more and only on the original goal: talking about the difference between R and Python and giving a useful prescription about that.
5. Full of URLs in the text
Who would find them useful in a paper book? Instead of writing down those URLs, he should have explained the content in a self-contained way.
Overall, this is one of the most terrible books of this kind. The author should understand that this book is hurting his professional reputation.
The book is very useful. If you are interested in Data Science using free tools then I recommend you buy and implement the book now. (More on that below.) You will need to spend some time prizing out the critical information and visiting a number of referenced websites, but in the end you will have a collection of first rate Data Science/Big Data Tools.
I give the book six stars for information and usefulness and subtract two stars for organization and latency.
There is a tremendous amount of useful information in the book ... for the moment. Mostly the book is a collection of web URL references and cites, and that means unless there is an on-line updated version then the half-life of the links is going to cause many of them to lead to 404 errors fairly soon.
A useful way to think of this book is as a bibliography of helpful links.
There are some interesting anecdotes and useful/informative illustrations. That said, the book is not a primer on any particular topic. The first couple of chapters give you some idea of the similarities and differences between "R" and "Python." There are then follow-up links. For most readers you are going to either fall into one of two categories, you know the languages cold, or you will simply stop and go to the other, referenced links to learn the material and then come back to this book.
I like the book, and I am scrambling to download the referenced packages while they are still available and at the described links.
The problems here are 1) organization of material, 2) print design, and 3) failure to provide downloadable code examples.
In an attempt to cover everything, the author stretches too far, includes too many subjects and then provides too little information. To compensate, the author includes occasional links to web resources.
The print design can fairly be described as awful, absolutely awful.
Page after page of coding examples are presented vertically on the page. That is, you have to turn the book sideways to read the code listings. Frankly, the design looks improvised and homemade.
That leads to the third problem: the author has not provided his code examples as downloadable files.
The net result is a book that contains very useful information, but is essentially impossible to read as it stands.
Too bad because the subject could use a good treatment.
I would suggest that author and publisher pull this title from the shelves, give to a competent technical editor, redo the print design and support it with downloadable code listings (at a minimum).
Most recent customer reviews
Not at all.
It's not nearly as useful as I thought it would be.Read more
I read some reviews on some poor print issues and I agree (hence the -1 star).Read more