Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics)

I purchased this book before I had a chance to read any sample chapter and was disappointed after I went through the book.Every chapter is dedicated to an application of a particular model of predictive analytics, where a (more or less) real problem is described and discussed, name of a model to use is mentioned, chart outputs are shown and used for a conclusion. In very much the same format and content of an article that you would see in for example Bloomberg business magazine. There is no substantial discussion of any of the models, and without a good understanding of such models you cannot conduct predictive Analytics.The content of this book could be used in the first 2-3 weeks of an introductory course in Analytics discussing what is Analytics and what are some example applications. I ended up keeping the book mostly due to hassle of a return, and partly for using it as a list of major models to read elsewhere and learn.

This is a good book on using R for predictive modeling.The books website contains all the code that is used in the book.I tried all of the downloadable R files and they all worked as advertised.I admit not trying the text processing though (Chapter 7) only because I don't like R for text processing.Rather use perl or Rapidminer.Pros:1. All the code works2. A good sample space of topics, so you get a feel of predictive modeling in different situations.3. You really don't need an extensive math background, since there is virtually no math described at all.Cons:1. If there was one thing I wish was better done is the analysis of the results. Some of the results, unless you are already familiar with the statistical technique used, might seem foreign and will require you to do some additional research.Summary:Overall a good book, minus the 1-Con above.Hint: If you do download the R programs, go through each one a piece at a time, to see what's going on. I found it's better than just "running the code". You'll have a better understanding of what's going on.

I had high hopes for the book, but it didn't enrich me in any way. The book has been outdated and outsmarted by better books in the market, and at much reasonable prices.Also, its misleading in many ways . The book says `Python Edition` - and then the author uses Python wrapper scripts to call R. That's not what I thought it would mean by `Python edition`. I regret this purchase.

Great overview of real examples in business with nice visualizations capabilities shown. The author lays out the background of the examples and the analytic questions to be solved. Plan to spend some time reviewing the code in the files if you are not as familiar with R at the level the book speaks to

Easy to understand and useful

Very informative. Models were easy to understand and follow. Being new to R it was very helpful for the projects I wanted to do.

I got the Kindle edition so that I could lift code from Kindle for PC and try it. But I started reading it in my old (circa 2009) Kindle II and noticed the figures are misaligned and the links don't work. (I still like extended reading on the Kindle because it is easier on my eyes indoors-- and the best thing next to hard copy outdoors.)I decided to write to Kindle folks about the misalignment to try and exchange the digital version for a hardcopy. I wanted to show what I was seeing in my Kindle so I opened the book in my Kindle for PC Windows 7. In the PC version, the figures align perfectly and we have the added bonus of full color reproduction. All the links work as expected. Then I check my iPad mini and the figures are aligned. (Although I hate the weird reverse text and columns-- have to figure out how to adjust that.)So, if you want the digital version it's fine on current technology. Reading this book on a PC is the best of the three digital formats.

I've read a few books now and been taking classes on data science but I've had trouble linking theory and the practice. This book has been a huge help in seeding ideas, and giving practical examples on how to execute those ideas. I appreciate the lack of downloadable source code as it has forced me to write the source by hand. Great book.

Analytics has become the new buzzword in marketing and the description tells a much different story to the one actually contained in the many examples provided.The book attempts to cover too many subject areas without the necessary detail that is critical when developing any model to be applied in real world solutions.This book really is an executive summary of many different areas that leads to more "discovery" type learning contained in better books already in print.The one saving grace is the R programming solution that is included at the end of end chapter for the topic being discussed. This minimally provides a clue on how to model the problem being presented in the chapter.Cookbooks such as this have long been used successfully but with much greater detail and scope that is simply lacking in this edition of the book.As with any new buzzword it takes time for the rebranded tools to be formally described into a new science of study. Analytics really is nothing more than the search for underlying factors that are contained within data and databases.Formalizing a new science such as analytics will take further time to develop and it is suspected the subsequent editions of this volume will equally evolve giving the reader more value and insights than is the case for this first edition.

Zwar ist der Katalog der dargestellten Methoden sehr umfangreich, die Behandlung im Einzelnen dann doch nur streiflichthaft. Ist eine passable Bahnlektüre, um sich einen Überblick zu verschaffen. Um die Dinge dann anzuwenden, ist bei den meisten nicht trivialen Verfahren dann weitere spezialisierte Lektüre erforderlich.

This book is not exhaustive, i dont suggest this book for both advanced as well as beginners. Waste of money.

Great book. The code snippets in Python and R are a great resources and easy to adapt, implement to existing projects.

Excellent! Thank you.