M.Sc Big Data Analytics Syllabus and Subjects 2024

Updated on - Jan 4, 2023

The M.Sc Big Data Analytics syllabus is a two-year postgraduate course in data science. The M.Sc Big Data Analytics syllabus and subjects are divided into four semesters. The syllabus includes Introduction to Computer Hardware, Statistics for Data Science, Technical Writing & Paper Presentation, Big Data and Internet of Things, System Software Concepts, Data Warehousing, Projects, Internships, and many more.

Semester Wise M.Sc Big Data Analytics Syllabus

The M.Sc Big Data Analytics course aims to ensure that the students have all the exposure in covering everything from data science to the use of advanced analytic techniques. This M.Sc Big Data Analytics course covers very large heterogeneous data sets, which can contain structured, semi-structured, and unstructured data, as well as data from many sources and sizes ranging from terabytes to zettabytes. Semester-wise M.Sc Big Data Analytics subjects list is given in the table below:

M.Sc Big Data Analytics First Year Syllabus
Semester I	Semester II
Statistical Methods for Data Analysis	Predictive Analytics
Java Programming	Data Mining
Optimization and Heuristics	Text Analytics
Topics in Analytics in R	Topics in Analytics with Python
Databases and Information Retrieval	Social Network Analysis
Programming Lab Courses/ Project Courses - I	Programming Lab Courses/ Project Courses - II

M.Sc Big Data Analytics Second Year Syllabus
Semester III	Semester IV
Analytics for Big Data	Elective I, II, III
Data Visualization	Internship program in Data Analytics.
Data Warehousing	-

M.Sc Big Data Analytics Subjects

The M.Sc Big Data Analytics course offers both theoretical and practical aspects of the study. M.Sc Big Data Analytics subjects like Computer Hardware, Statistics for Data Science, Technical Writing & Paper Presentation, Big Data and Internet of Things, System Software Concepts, Data Warehousing, Projects, Internships, and many more. The course curriculum includes theoretical and practical subjects. The compulsory subjects include:

Introduction to Computer Hardware
Statistics for Data Science
Technical Writing & Paper Presentation
Big Data and Internet of Things
System Software Concepts

M.Sc Big Data Analytics Course Structure

M.Sc Big Data Analytics course structure includes both core and elective subjects which are the aspects of the study. The course structure is made in such a way that both classroom training and practicals are included in the course curriculum. The course structure is given below:

IV Semesters
Core & Electives Subjects
Practicals
Projects
Internships

M.Sc Big Data Analytics Teaching Methodology and Techniques

The course curriculum takes into account different teaching methods. Classroom learning includes practical sessions for students. Students who are passionate about studying deep into computer programming with data analytics can be a boon for global technology development. Listed below are the teaching methodology and strategies in general:

Practical & Live sessions
Guest Lectures, Seminars, and Conferences
Group Assignment and Discussion
Project Submission
Internship

M.Sc Big Data Analytics Projects

Projects are given to students to understand the concepts and help students in getting hands-on experience. Research projects are to be completed by the end of the final year. Some popular M.Sc Big Data Analytics projects topics are:

Web Scraping
Data Cleaning
Sentiment Analysis
Exploratory Data Analysis (EDA)
10 Free Public Datasets For EDA

M.Sc Big Data Analytics Reference Books

M.Sc Big Data Analytics books are available both online and offline by many authors and publications. Reference books are meant for gaining an in-depth understanding of big data analyst concepts. Books on M.Sc Big Data Analytics for academic purposes differ according to subjects. Some of the reference books for the course in M.Sc Big Data Analytics are:

M.Sc Big Data Analytics Books
Books	Authors
Big Data Analytics	Parag Kulkarni
Lean Analytics: Use Data to Build a Better Startup Faster	A.Croll and B.Yoskovitz
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die	E. Siegel
Data Analytics Made Accessibly	A. Maheshwari