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1. Introduction to Data Science

• Introduction to Big Data
• Roles played by a Data Scientist
• Analyzing Big Data using Hadoop and R
• Methodologies used for analysis
• The Architecture and Methodologies used to solve the Big Data problems

2. Basic Data Manipulation using R

• Understanding vectors in R
• Reading Data, Combining Data
• Subsetting data
• Sorting data and some basic data generation functions

3. Machine Learning Techniques Using R Part-1

• Machine Learning Overview
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning
• Techniques, Clustering
• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models

4. Machine Learning Techniques Using R Part-2

• Understanding K-Means Clustering
• Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
• Implementing Association rule mining in R

5. Machine Learning Techniques Using R Part-3

• Understanding Process flow of Supervised
• Learning Techniques
• Decision Tree Classifier
• How to build Decision trees
• Random Forest Classifier
• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Naive Bayes Classifier

6. Introduction to Hadoop Architecture

• Hadoop Architecture
• Common Hadoop commands
• MapReduce and Data loading techniques (Directly in R and in Hadoop using SQOOP, FLUME, and other Data Loading Techniques)
• Removing anomalies from the data

7. Integrating R with Hadoop

• Integrating R with Hadoop using RHadoop and RMR package
• Exploring RHIPE (R Hadoop Integrated Programming Environment)
• Writing MapReduce Jobs in R and executing them on Hadoop

8. Mahout Introduction and Algorithm Implementation

• Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout

9. Additional Mahout Algorithms and Parallel Processing using R

• Implementation of different Mahout algorithms
• Random Forest Classifier with parallel processing Library in R