IT-702 (A) RGPV B.Tech
Data Science (DS)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
3 papers, latest Jun 2025
Syllabus, notes and important questions for IT-702 (A)
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Data Science and Big Data Overview 16 topics
- 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 Analytics Lifecycle: Data Preparation
- Data Analytics Lifecycle: Model Planning
- Data Analytics Lifecycle: Model Building
- Data Analytics Lifecycle: Communicate Results
- Data Analytics Lifecycle: Operationalize
Unit 2 Advanced Analytical Theory and Methods 16 topics
- Clustering
- K-means
- Additional Clustering Algorithms
- Association Rules
- Apriori Algorithm
- Applications of Association Rules
- Regression
- Linear Regression
- Logistic Regression
- Classification
- Decision Trees
- Naive Bayes
- Additional Classification Methods
- Text Analysis
- Text Analysis Steps
- Determining Sentiments
Unit 3 Advanced Analytics-Technology and Tools 12 topics
- 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
Unit 4 The Hadoop Ecosystem 16 topics
- 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
Unit 5 Introduction to R 4 topics
- Basic Data Analytics Methods Using R
- Communicating and Operationalizing an Analytics Project
- Creating the Final Deliverables
- Data Visualization Basics