1 Data Science Training Overview
1.1 Objectives of the Course
1.2 Pre-Requites of the Course
1.3 Course Duration
• 2 Data Science Course Content
2.1 Introduction to Data Science
2.2 Data
2.3 Big Data
2.4 Data Science Deep Dive
2.5 Intro to R Programming
2.6 R Programming Concepts
2.7 Data Manipulation in R
2.8 Data Import Techniques in R
2.9 Exploratory Data Analysis (EDA) using R
2.10 Data Visualization in R
2.11 HADOOP

2.11.1 Big Data and Hadoop Introduction
2.11.2 Understand Hadoop Cluster Architecture
2.11.3 Map Reduce Concepts
2.11.4 Advanced Map Reduce Concepts
2.12 Hadoop 2.0 and YARN
2.13 PIG
2.14 HIVE
2.14.1 Module-9
2.15 HBASE
2.15.1 Module-11
2.16 SQOOP
2.17 Flume and Oozie
2.18 Projects
2.19 Project in Healthcare Domain
2.20 Project in Finance/Banking Domain
2.21 Spark
2.21.1 Apache Spark
2.21.2 Introduction to Scala
2.21.3 Spark Core Architecture
2.21.4 Spark Internals
2.21.5 Spark Streaming
2.22 Statistics + Machine Learning
2.22.1 Statistics
2.22.1.1 What is Statistics?
2.23 Machine Learning
2.23.1 Machine Learning Introduction
2.24 Python
2.24.1 Getting Started with Python
2.24.2 Sequences and File Operations
2.25 Deep Dive – Functions Sorting Errors and Exception Handling
2.26 Regular Expressionist’s Packages and Object – Oriented Programming in Python
2.27 Debugging, Databases and Project Skeletons
2.28 Machine Learning Using Python
2.29 Supervised and Unsupervised learning
2.30 Algorithm
2.31 Application Example
2.32 Scikit and Introduction to Hadoop
2.33 Hadoop and Python

2.34 Python Project Work