This book is what I was looking for for my new job as a Credit Risk Data Modeler (basically data science applied to credit problems). It's coverage is broad, but deep and applied enough that I have been able to apply its contents in my day-to-day work. I look forward to a second edition which will hopefully rectify the following:
Earlier in the book it seemed the authors took great pains to explain in layman's terms the various statistical elements of the topic they were covering. They provided very clear and meaningful explanations which made a lot of sense of complex topics. But later in the book it seemed that that approach largely went out the window and they started using more technical boiler plate to describe the various statistical tests and procedures. Rather than perhaps give the technical boilerplate (as you'd see it in a textbook) and then elaborate on it with a more human-centric explanation, they would just leave it at the nearly impervious technical description and then proceed to explain how to conduct the test/procedure/etc in R. But without understanding of what you're trying to accomplish and why, it's hard to write the code to actually do it. Keep in mind that I'm relatively well prepared for this book too, having had as much stats and econometrics as I could fit into my four-year degree. If I found some sections of the book too technical to understand then it seems likely that the book would benefit from some additional explanation and discussion in those later sections.
Also, I have a good deal of "boots on the ground" experience with this book in my attempts to apply it in my daily work. I've found that it is useful, but could be more useful if there was more discussion of various practical problems. For instance, much of my work is focused on producing a predictive model of likelihood of charge-off. I.e., if we approve and fund this application, how likely is it to perform or charge-off. The book shares some high-level approaches to finding problems in data (using plots and summaries), fixing those problems using various techniques, selecting variables, and how to conduct the statistical modeling (logistic in my case). But it fails to really tie those areas together beyond the high-level. For instance, what are the assumptions of a logistic regression? How do you resolve issues in your data to ensure that you meet those assumptions and can perform a valid logistic regression? How do you really select variables when you're faced with at least 20 possibilities (and potentially many many more if you count interaction terms, unfixed variables, and variables which have been fixed in different ways)?
I suppose, for what it was, that it is "mission accomplished." I'd just like to see a lot more. Perhaps there's need for a second volume? Perhaps "Advanced Practical Data Science with R?" Either this book could have a second edition with a lot more content covering finding data problems, resolving those problems intelligently (for instance, resolving missing data is basically left as "either drop the effected records" or "use the mean as a replacement or the missing value," but there are alternative methods which may be more suitable), what data problems will cause issues in OLS regression, logistic regression, and machine learning; And how to practically select variables and a model. I feel like the book gave me some tools to apply (like a small box of tools you might purchase from a hardware store), but left a lot out. So now I'm in deep water trying to figure out why my logistic regression isn't predictive enough and what I can do about it. Is it the data and how I fixed variables? Is it the variables I've selected? Should I have used automated variable selection techniques? Or just manually tried different variables? How does an experienced practitioner approach these problems? I know they iterate: explore data, clean data, select variables, select model, test model, look at data, change data, change variables, etc... but practically speaking what does it look like? In the book they offer a hand-coded basic variable selection script, and mention that one could also use stepwise variable selection. In the real world I'm reasonably sure that this is not actually done--mostly because their selection script does about as well as stepwise at selecting appropriate variables. There are many other better ways of selecting variables, I've discovered, and I wish that they'd discussed some of those ways (pros and cons), and shown how to conduct them in a meaningful fashion. Same thing with building a model. In my case, I have a whole bunch of variables, limited data (about 2000 records, with the desired outcome only occurring in 120 of those), and the automated tools (various R packages I've discovered and applied) either take a long time to run and/or yield poor results. But if not automated tools then what? Manually add variables and ANOVA test the difference between the first and second model?
I'd just like more...more discussion and elaboration and examples of how practical data science is conducted. This book seems like it does a fantastic job as an introduction to the topic, but you'll quickly find that you'll be in deep water without a clue how to swim--as in my case. You'll be left to your own devices, and find yourself wishing, as I do, that there was more in the book (or another book) that I could study after this one which would help take me from beginner data scientist to intermediate.
Overall, I'm very glad I bought and read the book.
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AloeQueen
Great book to learn R programming and Stats
Reviewed in the United Kingdom on May 4, 2020Pedram
This is a great book that artfully bridges the gap of data science ...
Reviewed in Canada on November 13, 2016
