I would go as far as to say that the book is brilliant.First, a drop about me from the standpoint of this book. I have been an IT professional for many years specializing in programming, database, and MS Office add-ons. Part of my job entails self enrichment, that is, expand my working knowledge in areas potentially important for my job. I chose Foreman's book to help with this task for a number of reasons: a) Data Science is a hot area and my company does have a Data Science group, b) I have lots of data experience under my belt - I felt that it would be nice for once to get some useful information from the data, and c) I have a really good Excel background - so I figured that Foreman's approach would be perfect for me - little did I know that I would seriously add to my Excel bag of tricks.The author makes the assumptions that: a) the reader is somewhat technical, b) he knows nothing about Data Science, and c) he is relatively comfortable working in Excel.Reading the book is a joy because Foreman has a cozy, chummy style. He definitely doesn't throw all the technical stuff at the reader rat-tat-tat machine gun style like many other authors. Instead, Foreman gently introduces his topics and then ramps up technical details carefully. This most definitely helps the learning process.Speaking of learning, by the end of the you will have learned important concepts in "machine learning" and I believe that you will be ready for the next step. I sure was. I found the topics interesting and I wanted to learn more. This is where the book's only problem area comes into play - the next step. Foreman has 3 references - one good, but minor, one terrible, and the other is inappropriate. Let me explain.Foreman recommends a free resource as a follow-on to his Forecasting Chapter. This is a good reference, but I believe that Forecasting is a minor topic in Data Science, unless, of course, Forecasting becomes your thing.Foreman's main reference is: "Data Mining with R" by Luis Torgo. Foreman recommends this as the next step after his book.I tried to read this several times, but couldn't. It certainly wasn't my next step.The other reference, "The Elements of Statistical Learning" by Trevor Hastie, et. al, is totally inappropriate for Data Science newbies. You can checkout the Amazon reviews for this book and you'll see that you need a pretty serious background in statistics to get anything out of that reference. In fact, the author Hastie says as much in his next book "An Introduction to Statistical Learning- with Applications in R". This is the appropriate next step, but I'll get to that in a moment.Here are my recommendations:A. Read Foreman's book and follow along with him in working through the Excel spreadsheets. This is a first step in getting comfortable with Machine Learning.B. Take the Coursera courses: 1) Machine Learning Foundations: A Case Study Approach, and 2) Machine Learning: Regression. The courses are free unless you want completion certificates, in which case there is nominal cost.C. Now you are ready for:Â An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)Â This book is also available for free by the authors - check online.