Big Data and Analytics

Big Data and Analytics refers to the strategy of analyzing extremely large, complex data sets to reveal patterns, trends, and associations. In the modern "Digital Economy," it provides the "technical clarity" needed to move from raw data to actionable "Business Strategy".

Big Data and Analytics

Course Title: Big Data and Analytics

Course No: IT278

Nature of the Course: Theory + Lab

Semester: 8

Credit Hours: 3

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 Objectives

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 Contents

1. Overview of Big Data

5 hrs

1.1. Introduction

1.2. Types of Data

1.3. Evolution of Big Data

1.4. Big Data Characteristics

1.5. Big Data versus Data Warehouse

1.6. Advantages and Disadvantages of Big Data

1.7. Obstacles in Utilizing Big Data

1.8. Impact of Big Data

2. Challenges of Big Data

6 hrs

2.1. Introduction

2.2. Big Data Integration

2.3. Storing Big Data

2.4. Maintaining Data Quality

2.5. Analysis of Big Data

2.6. Security and Privacy Management

2.7. Accessing and Sharing

3. Big Data Analytics

6 hrs

3.1. Introduction

3.2. Applications of Big Data Analytics

3.3. Types of Big Data Analytics

3.4. Comparison of Data Analytics Stages

4. Hadoop and NoSQL Databases

14 hrs

4.1. Introduction to Hadoop

4.2. Components of Hadoop Ecosystem

4.3. Data Storage Component

4.4. Data Processing Component

4.5. Data Access Component

4.6. Data Management Component

4.7. Apache Spark

4.8. Introduction to NoSQL Databases

4.9. Types of NoSQL Databases

Key-Value

Column-Oriented

Document Oriented

Graph-Based Databases

4.10. BASE Model

4.11. Advantages and Disadvantages of NoSQL Databases

5. Data Lakes

7 hrs

5.1. Introduction

5.2. Data Lake Architecture

5.3. Usage and Challenges

5.4. Advantages and Disadvantages

5.5. Lake House

5.6. Difference between Data Warehouse, Data Lakes, and Lake Houses

5.7. Best Practices

6. Big Data Applications

10 hrs

6.1. Big Data for Healthcare

6.2. Big Data Analytics of Fraud Detection

6.3. Big Data Analytics of Social Media

6.4. Novel Applications

Laboratory Works

The laboratory work includes using Hadoop and any appropriate NoSQL database system to implement big data systems and related concepts.

1.Big Data Systems Lab

Text Books

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

Notes:

Elective course. Lecture hours: 48. This syllabus follows the official BIM curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://www.fomecd.edu.np/syllabuses

Big Data and Analytics

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