Big Data Analytics with R: Utilize R to uncover hidden patterns in your Big Data

Part I. The era of big data
1. The era of big data
2. Big data – the monster re-defined
3. Big data toolbox - dealing with the giant
4. R – the unsung big data hero

Part II. Introduction to R programming language and statistical environment
5. Introduction to R programming language and statistical environment
6. Learning R
7. Revisiting R basics
8. Applied data science with R

Part III. Unleashing the power of R from within
9. Unleashing the Power of R from Within
10. Traditional limitations of R
11. To the memory limits and beyond
12. Parallel R
13. Boosting R performance with the data table package and other tools

Part IV. Hadoop and mapreduce framework for R
14. Hadoop and mapreduce Framework for R
15. Hadoop architecture
16. A single-node Hadoop in Cloud
17. Hdinsight - a multi-node Hadoop cluster on Azure

Part V. R with Relational Database Management Systems (rdbmss)
18. R with relational database management systems (rdbmss)
19. Relational database management systems (rdbmss)
20. Sqlite with R
21. Mariadb with R on a Amazon EC2 instance
22. Postgresql with R on Amazon RDS

Part VI. R with non-relational (nosql) databases
23. R with Non-Relational (nosql) Databases
24. Introduction to nosql databases
25. Mongodb with R
26. Hbase with R

Part VII. Faster than Hadoop - Spark with R
27. Faster than Hadoop - Spark with R
28. Spark for Big Data analytics
29. Spark with R on a multi-node hdinsight cluster

Part VIII. Machine Learning Methods for Big Data in R
30. Machine Learning Methods for Big Data in R
31. What is machine learning?
32. GLM example with Spark and R on the HDInsight cluster
33. Naive Bayes with H2O on Hadoop with R
34. Neural Networks with H2O on Hadoop with R

Part IX. The future of r - big, fast, and smart data
35. The Future of R - Big, Fast, and Smart Data
36. The current state of big Data analytics with R
37. The future of R
38. Where to go next