Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics) Hardcover – 1 Oct. 2014
- Choose from over 13,000 locations across the UK
- Prime members get unlimited deliveries at no additional cost
- Find your preferred location and add it to your address book
- Dispatch to this address when you check out
Frequently bought together
Customers who viewed this item also viewed
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Would you like to tell us about a lower price?
If you are a seller for this product, would you like to suggest updates through seller support?
About the Author
THOMAS W. MILLER is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.
Miller is co-founder and director of product development at ToutBay, a publisher and distributor of data science applications. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets, and has worked with predictive models for over 30 years. Miller’s books include Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.
Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin–Madison.
He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota, and an MBA and master’s degree in economics from the University of Oregon.
Customers who bought this item also bought
There was a problem filtering reviews right now. Please try again later.
Top international reviews
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.
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.
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.
1. All the code works
2. 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.
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.
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.
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.
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.