Here I will be posting informative reviews for books I receive from publishers, and which in my view, students and others practising data science may find useful. Please note that I only post reviews of books that are in my view neutral or good.
My Rating will be:
Modeling techniques in predictive analytics with Python and R: A guide to data science by Thomas W. Miller published by Pearson Education Inc. This book introduces a number of data science predictive modelling techniques focusing on text analytics, sentiment analysis, sports analysis, economic data analysis and spatial data analysis applications which are essentially tasks requiring regression or classification techniques. Each chapter is focused on an application and presents a solution using Python or R. The book’s solutions are using conventional machine learning approaches (such as Support Vector Machines for classification, ARIMA models for time series economic data analysis, and Regression models for Spatial Data) for these regression and classification problems. The book teaches a lot of concepts on how to deal and analyse non-image data. It can serve as a nice textbook on learning basic data analysis concepts and getting a good understanding of how to code in Python or R.
The book presents code in each chapter without explanations of the code in detail. Hence, a suitable reader for this book is someone who is already familiar and has some experience in coding using the Python and/or R programming language. However, the reader may soon resort into looking at alternative approaches to solving these problems using more current data science libraries, and the more recent deep learning approaches.
Review date: 7 June 2019 More information about the book can be found here.