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