A compendium of book’s, tutorials, datasets

On this page I will be posting the links to all resources that I find are pertinent for my research work and for readers to this blog. Book’s 1. Data Mining with R learning with case studies – very interesting a practical book. My rating- 5 stars 2. An Introduction to Statistical Learning with applications in R (available for free online) by James, Witten, Hastie. Springer Publication- Is a classic book that requires no prior R programming experience required. Easy to understand language. My rating- 5 stars 3. Data Mining Practical Machine Learning Tools and techniques with Java Implementations by Witten & Frank. Maurgan Kauffman Publications- From the creators of Weka this book is perhaps the only well established treatise on the application of Weka in Data Mining context. However, a word of caution. Prior statistical concepts are a mandate to understand the algorithms presented in the book. If you are reading this book and need help, go here. My rating- 4 stars Journal’s Leading Data Mining Journals: IEEE Transactions on Pattern Analysis and Machine Learning (TPAMI)

Tutorial’s/MOOC’s
 Statistical Data Mining Tutorials– An exhaustive list of excellent tutorials on DM algorithms
Datasets (Free mostly)
 Open Discovery Space and LinkedUP – will help in collecting, sharing and have an open access to educational data sets also Datasets from DataHub
The Programme for International Student Assessment (PISA) is a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students.
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