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IT-702 (A) · Data Science/Official Syllabus

Data Science (IT-702 (A)) - Official Syllabus

1
Unit 1

Data Science and Big Data Overview

  • Types of data
  • Sources of data
  • Data collection
  • Data storage and management
  • Big Data Overview
  • Characterization of Big data
  • Drivers of Big Data
  • Challenges
  • Big Data Use Cases
  • Defining Big Data Analytics and examples of its use cases
  • Data Analytics Lifecycle: Discovery, Data Preparation, Model Planning, Model Building, Communicate Results, Operationalize
2
Unit 2

Advanced Analytical Theory and Methods

  • Clustering
  • K-means
  • Additional Clustering Algorithms
  • Association Rules
  • Apriori Algorithm
  • Applications of Association Rules
  • Regression
  • Linear Regression
  • Logistic Regression
  • Classification, Decision Trees, Naive Bayes
  • Text Analysis, Text Analysis Steps, Determining Sentiments
3
Unit 3

Advanced Analytics-Technology and Tools

  • Analytics for Unstructured Data Use Cases
  • MapReduce
  • Apache Hadoop
  • Traditional database vs Hadoop
  • Hadoop Core Components
  • HDFS, Design of HDFS, HDFS Components, HDFS Architecture
  • Hadoop 2.0 Architecture, Hadoop-2.0 Resource Management, YARN
4
Unit 4

The Hadoop Ecosystem

  • Introduction to Hive, Hbase
  • Hive Use Cases: Facebook, Healthcare
  • Hive Architecture, Hive Components
  • Integrating Data Sources
  • Dealing with Real-Time Data Streams and Complex Event Processing
  • Overview of Pig, Difference between Hive and Pig, Use Cases of Pig, Pig program structure, Pig Components, Pig Execution, Pig data models
  • Overview of Mahout, Mahout working
5
Unit 5

Introduction to R

  • Introduction to R
  • Basic Data Analytics Methods Using R
  • Communicating and Operationalizing an Analytics Project, Creating the Final Deliverables
  • Data Visualization Basics

Reference Books

  • Data Science and Big Data Analytics
  • Big Data for Dummies
  • Big Data Analytics with R and Hadoop
  • Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
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