Getting Started with Data Science: Making Sense of Data with Analytics (IBM Press) Paperback – 13 December 2015

It turned out to be much more than a book on Stastics. It gives a clear direction on how to conduct formal research - something which came as an eye opener to me.

Terrific book to get started. Author teaches with a storytelling approach !

good book, but toooooo much space is taken up by the definition of data. It makes the basics clear, I want more advanced treatment. Starts about story telling which disappears.

The author, Murtaza Haider, has done a commendable job in writing a very good book as a gentle introduction to the realm of data analytics. He starts out by presenting the basic methods and descriptive statistics methods for drawing inference from underlying data. The bulk of the methods covered can be found in a good statistics textbook and the book has plenty of illustrations and lucid explanations on using these techniques. The major highlight of the book is the “story telling” the author has adopted. This is sorely missing in many of the texts on data science and predictive models. Another highlight is a chapter on spatial data analytics with GIS-related data.However, if the reader is already familiar with basic statistics and is more interested in predictive modeling using machine learning techniques -and this is what most people who see the term “Data Science” in the title expect, then they will be disappointed. The author barely touches on the predictive aspects of data science and glosses over several of the techniques. For instance, the decision tree models are barely covered in 1 or 2 pages, although the term is referenced in many others. There’s no chapter dealing with basic predictive methods such as: apriori algorithms, random forests, or ensembles. All these methods are mentioned in passing in the last chapter titled “Data Mining for Gold”.All in all, a good stats textbook but prepare to be disappointed if you’re looking for a good text on predictive aspects of data science.

I really love this book. Easy to read and understand.

I have to say I do respect the words of Professor Haider, and I really wish he would continue on with his writing and write more literature. His book is one of the more Human Data Science books you can use everyday and in a corriculum. Your students will keep and cherish the book forever and could potentially use it in the office/on site. Real world issues, real job solutions backed by data science methods. Absolutely loved the book and I cannot wait to share with others who are developing a love for data.

He uses code in R, Stata, SPSS without any explanation, just showing the code. It talks about general topics and then all of a sudden he jumps to the statistical equations of the models without any explanation. I was attracted by the introduction which was nice but the rest of book is simply not useful and mostly confusing. Regretted buying it.

Ich wurde durch einen Beitrag bei R-Blogger auf das Buch aufmerksam.Wer jetzt erwartet, in dem Buch über den üblichen Big-Data-Hadoop-R-IoT-usw-Hype zu lesen, der wird enttäuscht.Das Buch beleuchtet, wie man aus Daten Informationen herleitet und damit überzeugende Geschichten erzählt, um schliesslich Entscheidungen herbei zu führen.Es ist in meinen Augen ein hervorragender Überblick über "Data Science" und dessen praktische Anwendung. Das Buch liest sich sehr gut, und man merkt sehr schnell, dass der Autor viel Erfahrung mit dem Extrahieren von Informationen aus Daten hat.Absolute Empfehlung!

I found the book very useful not only for an IBM course I am taking but also as a good general guide to the real-life application of data science.