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Subject Syllabus

CSIT-801 - Data Science

CSIT · Semester 8

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

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 and operationalize

Clustering and K-means Additional clustering algorithms Association rules and Apriori algorithm Applications of association rules Regression: linear regression and logistic regression Classification: decision trees and Naive Bayes Additional classification methods Text analysis steps Determining sentiments

Analytics for unstructured data use cases MapReduce Apache Hadoop Traditional database vs Hadoop Hadoop core components HDFS: design, components and architecture Hadoop 2.0 architecture Hadoop 2.0 resource management YARN

Introduction to Hive and HBase Hive use cases: Facebook and healthcare Hive architecture and components Integrating data sources Dealing with real-time data streams Complex event processing Overview of Pig Difference between Hive and Pig Use cases of Pig Pig program structure, components, execution and data models Overview of Mahout and Mahout working

Introduction to R Basic data analytics methods using R Communicating and operationalizing an analytics project Creating the final deliverables Data visualization basics