IT 278

Big Data and Analytics

TU BIM · Semester 8 · BIM curriculum effective from 2021

Requirement
elective
Credits
3
Syllabus
4 sections

Syllabus

What this course covers and how the teaching time is divided.

course syllabus loaded.

Big Data and Analytics Syllabus

Official TU PDF

Tribhuvan University

Faculty of Management

Office of the Dean

Bachelor of Information Management (BIM)

Course Title: Big Data and Analytics

Course Code: IT 278

Semester: Semester 8

Nature of Course: elective

Full Marks: 100

Pass Marks: 50

Credit Hours: 3 Cr.

Curriculum: BIM curriculum effective from 2021

Course Objective

:

The objective of this course is to provide knowledge of big data and related concepts to students. This course is designed to help students learn big data technologies, NoSQL systems, data lakes , and their applications.

Course Description

:

The course covers different concepts of big data technologies , including an overview and challenges of big data, big data analytics, Hadoop and NoSQL systems, data lakes , and big data applications.

Course Contents:

Lecture hours show the approximate classroom time allocated to each unit.

Unit 1.Overview of Big Data

5 hours
  • Introduction
  • Types of Data
  • Evolution of Big Data
  • Big Data Characteristics
  • Big Data versus Data Warehouse
  • Advantages and Disadvantages of Big Data
  • Obstacles in Utilizing Big Data
  • Impact of Big Data.

Unit 2.Challenges of Big Data

6 hours
  • Introduction
  • Big Data Integration
  • Storing Big Data
  • Maintaining Data Quality
  • Analysis of Big Data
  • Security and Privacy Management
  • Accessing and Sharing.

Unit 3.Big Data Analytics

6 hours
  • Introduction
  • Applications of Big Data Analytics
  • Types of Big Data Analytics
  • Comparison of Data Analytics Stages

Unit 4.Hadoop and NoSQL Databases

14 hours
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Data Storage Component
  • Data Processing Component
  • Data Access Component
  • Data Management Component
  • Apache Spark
  • Introduction to NoSQL Databases
  • Types of NoSQL Databases
  • Key - Value, Column-Oriented, Document Oriented and Graph -Based Databases
  • BASE Model
  • Advantages and Disadvantages of NoSQL Databases.

Unit 5.Data Lakes

7 hours
  • Introduction
  • Data Lake Architecture
  • Usage and Challenges
  • Advantages and Disadvantages
  • Lake House
  • Difference between Data Warehouse, Data Lakes , and Lake Houses
  • Best Practices.

Unit 6.Big Data Applications

10 hours
  • Big Data for Healthcare
  • Big Data Analytics of Fraud Detection
  • Big Data Analytics of Social Media
  • Novel Applications. Laboratory Work The laboratory work includes using Hadoop and any appropriate NoSQL database system to implement big data systems and related concepts.

Suggested Readings:

Husain M.S. Khan M.Z. & Siddiqui T. (2024). Big Data Concepts, Technologies and Applications. First Edition. CRC Press. Hammond-Errey M. (2024). Big Data, Emerging Technologies and Intelligence: National Security Disrupted. Routledge. White T. (2015). Hadoop: The Definitive Guide. Fourth Edition. O’Reilly Media. Fowler A. (2015). NoSQL for Dummies. Wiley.