MSc Data Science Syllabus & Subjects 2024

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Sep 24, 2024 21:14PM IST

The MSc Data Science syllabus is divided into four semesters and spans two years, where the commonly covered subjects deal with various ways of managing large sets of real-world data. MSc Data subjects are designed to train students to work as data architects, data scientists, data analysts, etc.

MSc Data Science Syllabus & Subjects Overview

The MSc Data Science syllabus covers the major subjects, tools, and theories of Calculus, Descriptive Statistics, and C-programming to understand different occurrences with a large amount of actual data. As you may know, MSc Data Science is a two-year full-time postgraduate degree that covers the major disciplines, techniques, and theories of Calculus, Descriptive Statistics, and C-Programming to understand numerous phenomena concerning a large set of real-world data. Furthermore, statistics, mathematics, coding, machine learning, and other topics are commonly covered in MSc Data Science subjects.

The purpose of the MSc Data Science course is to produce competent and analytical scientists and researchers who can unravel even the most challenging data to push the world's technological frontiers. It makes the learner aware of the conditions and hardships they must become acclimated to survive in this harsh and competitive world while still pursuing something that would lead them to their goal. Students may pursue jobs as data analysts, data architects, business analysts, data scientists, research data scientists, statistical programmers, operations managers, and operations analysts after completing this course. Students can obtain a detailed understanding of the MSc Data Science syllabus and subjects by scrolling down this page till the end.

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MSc Data Science Year Wise Syllabus

The MSc Data Science is a technically challenging degree that requires students to have a foundational understanding of the field, as has already been stated. To be considered for an MSc in Data Science, a candidate has to be knowledgeable in the required programming languages, mathematics, calculus, and statistics. Other abilities that an MSc Data Science candidate will develop include data analysis, machine learning and deep learning, data visualisation, big data, and so on.

The description of the  MSc Data Science syllabus is provided below. Based on information gathered from several colleges, this is a standard syllabus. As a result, the syllabus at each college may differ slightly.

MSc Data Science First Year Syllabus

The following table highlights the first-year MSc Data Science syllabus:

Semester 1Semester 2 
Advanced Database Management Systems Calculus and Linear Algebra for Data Scientists 
Applied Probability and Probability Data Analysis and Visualisation 
DistributionDistributed Algorithms & Optimisation with Hadoop, Spark  
SQL ProgrammingAdvanced Machine Learning 
Python and R Programming Deep Learning 
Computational Mathematics  Stochastic Processes 
Statistical Inference

MSc Data Science Second Year Syllabus

The following table highlights the second-year MSc Data Science syllabus:

Semester 3Semester 4
Cloud Native Development Natural Language Processing 
Data Structures and AlgorithmsApplied Business Analytics 
Java Programming Data Engineering 
Optimisation Data Mining and Warehousing 
Web Technologies Programming in SAS for Analytics 
Bayesian Statistical Modelling Research Methodology
Longitudinal Data Analysis  Major Project
Minor Project
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MSc Data Science Subjects

The MSc Data Science subjects are designed to give students a thorough understanding of the fundamentals. For a deeper knowledge of complex application-related issues, the MSc Data Science syllabus incorporates both theoretical classroom-based teaching and practical visit sessions. For greater flexibility throughout the two years, the curriculum includes both core and elective subjects. The following is the list of the subjects for MSc Data Science:

  • Statistics
  • Coding
  • Business Intelligence
  • Data Structures
  • Mathematics
  • Machine Learning
  • Algorithms
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MSc Data Science Common Subjects for All Semester

The list of MSc Data Science subjects include Calculus, descriptive statistics, C programming, and the use of several technologies, including ML, DL, Python, and Sparkn. Furthermore, these topics are covered in all MSc data science courses, including full-time, online, and classroom programmes. The following pointers list common MSc Data Science subjects for all semesters:

  • Advanced Python Programming for Spatial Analytics
  • Applied Statistics
  • Data Mining and Algorithms
  • Fundamentals of Data Science
  • Image Analytics
  • Introduction to Geospatial Technology
  • Machine Learning
  • Mathematics for Spatial Science
  • Python Programming
  • Spatial Big Data and Storage Analytics
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MSc Data Science Optional Subjects

Apart from the core subjects, candidates will have to select optional or elective MSc Data Science subjects to fulfil their academic credits. These subjects, however, differ from one college or university to another as each institute provides a list of optional subjects they offer. Among these, a student should select the ones that interest them the most or the ones most beneficial to their career objectives. Listed below are optional MSc Data Science subjects that students can use for reference.

For Data Analytics:

Information RetrievalBusiness Intelligence
Number Theory and CryptographyHigh-Performance Computing (HPC)
Pattern RecognitionInformation Security & Cryptography
Regression AnalysisPredictive Analytics
Theory of ComputationParallel and Distributed Computing
Time Series AnalysisSoft Computing

For Data Mining:

Artificial IntelligenceComputer Graphics
Computer NetworksImage Processing
Clustering TechniquesNetwork Security
Graph Theory and Discrete MathematicsNatural Language Processing
Text MiningSignal Processing
Web IntelligenceSocial Network Aggregators

The following table shows some other elective MSc Data Science subjects:

Deep LearningSystem Dynamics Simulation
IoT Spatial AnalyticsSpatial User Interface Design and Implementation
Research Modelling and Implementation Genomics
Exploratory Data AnalysisMultivariate Analysis
Stochastic Process Programming for Data Science in R
HadoopImage and Video Analytics
Internet of ThingsIdentification and Data Collection
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MSc Data Science Lab Subjects

The main practical papers listed below are some of the MSc Data Science subjects found in the syllabus. The list of lab subjects for MSc Data Science may vary depending on the academic institution.

  • Database Management System Lab
  • Fundamentals of Programming Lab
  • Big Data Lab
  • Data Visualisation Lab
  • Business Analytics Lab
  • Data Mining and Data Warehousing Lab
  • Machine Learning Lab
  • Deep Learning Lab
  • Internet of Things Lab
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Specialisations Offered in MSc Data Science

Within the broad field of data science, there are several sub-domains. As a result, data science has many specialisations. You can develop solid foundations in each of these specialisations with the help of the MSc Data Science syllabus. This will also be highly beneficial if you want to specialise in a field at a higher level to get more in-depth information about that particular field. The specialisations for MSc Data Science subjects are listed below.

Data Mining & Statistical Analysis Business Intelligence & Strategy Making
Data Engineering & Data Warehousing Data Visualization 
Database Management & Data Architecture Operations-related Data Analysis
Machine Learning & Cognitive SpecialistMarket Data Analytics 
Cybersecurity Data Analysis Deep Learning 
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Syllabus for MSc Data Science Distance Programs

MSc Data Science is a two-year PG program that is being provided via online learning by numerous institutions in India as of 2022. With the help of this MSc Data Science Distance syllabus, it is expected that students will be better able to detect and understand concepts in data science in the future by developing their abstract thinking and design skills. The MSc Data Science Distance Syllabus is provided below-

First-Year MSc Data Science Syllabus for Distance Programs 

The table below provides the detailed first-year MSc Data Science Syllabus for distance programs-

Semester 1Semester 2
Mathematics for Spatial SciencesSpatial Big Data and Storage Analytics
Applied StatisticsData Mining and Algorithms
Fundamentals of Data ScienceMachine learning
Python ProgrammingAdvanced Python Programming for Spatial Analytics
Introduction to Geospatial TechnologyImage Analytics
Programming for Spatial SciencesSpatial Data Base Management
Business CommunicationFlexi-Credit Course
Cyber Security
Integrated Disaster Management

Second-Year MSc Data Science Syllabus for Distance Programs

Given below is the second-year syllabus of the MSc Data Science Distance program-

3rd Semester4th Semester
Spatial ModelingIndustry Project
Summer ProjectResearch Work
Web Analytics
Artificial Intelligence
Flexi-Credit Course
Predictive Analytics and Development
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MSc Data Science Entrance Exam Syllabus

The majority of Indian colleges and universities source admission to their MSc Data Science programs based on candidates' performance on an entrance exam. To get admission, an applicant should attain the required minimum percentage of marks, or the "cut-off." The cutoff varies depending on the college.

Multiple Choice Questions (MCQs) and Numerical Question Answers (NQAs) are usually used in MSc Data Science entrance exams. The following is a discussion about the entrance exam syllabus for MSc Data Science:

IIT JAM: The syllabus for IIT JAM includes topics such as Biochemistry, Molecular Biology, Plant Biology, Biotechnology, Bonding in molecules, Chemistry of organic compounds, Probability, Chemical Thermodynamics, Chemical Kinetics, Electrochemistry, and more.

CUET PG: The syllabus for CUET PG includes topics such as Agronomy, Genetics & Plant Breeding, Soil Science & Agricultural Chemistry, Entomology, Agricultural Economics, Mycology & Plant Pathology, Agricultural Engineering & Statistics, Agricultural Extension Education, Plant Physiology, Horticulture, Mathematics, etc.

BITSAT: The syllabus for BITSAT includes topics such as Bernoulli’s theorem, Collisions, Conservation of momentum, Ecology and Environment, Electric dipole, Gauss’ law and its applications, Genetics and Evolution, Hydrogen, Integral calculus, Momentum of a system of particles, Motion with constant acceleration, Newton’s law of gravitation, Power, Probability, Projectile motion, Redox reactions, Statistics, Stereochemistry, Uniform circular motion, Verbal reasoning, Viscosity and Surface Tension, Vocabulary, etc.

KSET: The syllabus for KSET includes topics such as Anthropology, Archaeology, Chemical Sciences, Commerce, Criminology, Earth Sciences, Economics, Education, Electronic Science, English, Environmental Sciences, Folk Literature, Geography, Hindi, History, Home Science, Kannada, Law, Library & Information Science, Life Science, Management, Marathi, Mass Communication & Journalism, Philosophy, Physical Education, Political Science, Psychology, Sanskrit, Social work, Sociology, Tourism and Administration, Urdu, and so on.

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MSc Data Science Important Books

The best books for MSc Data Science are provided by numerous authors and publishers both online and offline. However, a student should invest in reference books after conducting a thorough study based on their chosen specialisation. Furthermore, the MSc Data Science syllabus and subjects pdf, which is freely accessible online, is designed to aid with conceptual understanding. The best MSc Data Science books include the following:

Book NameAuthor/s
Practical Statistics for Data ScientistsPeter Bruce and Andrew Bruce
Introduction to ProbabilityJoseph K Blitzstein and Jessica Hwang
Introduction to Machine Learning with Python: A Guide for Data ScientistsAndreas C Müller and Sarah Guido
Python for Data AnalysisWes McKinney
Python Data Science HandbookJake VanderPlas
R for Data ScienceHadley Wickham and Garret Grolemund
Understanding Machine Learning: From Theory to AlgorithmsShai Shalev-Shwartz and Shai Ben-David
Deep LearningIan Goodfellow, Yoshua Bengio, and Aaron Courville
Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman
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MSc Data Science Course Structure

The course structure is intended to contain both core and optional MSc Data Science subjects. In the first year, students are only introduced to fundamental knowledge through basic subjects. Students are introduced to a particular MSc Data Science syllabus related to their specialisation during the second year. The knowledge of theoretical concepts is also enhanced through practical classes. The following is the MSc Data Science course structure:

  • IV Semesters
  • Core and Main Subjects
  • Elective and Optional Subjects
  • Practical Meet-ups and Visits
  • Research Project/ Thesis Submission
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FAQs about MSc Data Science Syllabus

What skill will I learn after completing the MSc data science course curriculum?

Candidates will learn the following skills after completing the MSc data science course curriculum:

  • Business Acumen
  • Data Cleaning
  • Data Visualisation
  • Machine Learning Knowledge
  • Problem-Solving Ability
  • Programming Skills
  • SQL Expertise
  • Statistical Knowledge

What is the MSc data science syllabus for entrance exams?

The MSc data science syllabus for entrance exams includes topics such as Biotechnology, Bonding in molecules, Chemistry of organic compounds, Folk Literature, Genetics & Plant Breeding, Genetics and Evolution, Geography, Hindi, History, Hydrogen, Integral calculus, Plant Biology, Soil Science & Agricultural Chemistry, and more.

What are the core MSc data science subjects?

The list of the core MSc data science subjects includes Algorithms, Business Intelligence, Coding, Data Structures, Machine Learning, Mathematics, Statistics, and more. Core subjects are compulsory for students to take to pass the semester-wise exams and these remain the same throughout all universities offering this programme.

What are the specialisation subjects for MSc data science?

The specialisation subjects for MSc data science include topics such as Artificial Intelligence, Fundamental Data Analytics, Machine learning, Natural Language Processing, Python, and Regression Modelling. Candidates can choose any specialisation based on their personal interests or career aspirations.

What does the MSc data science syllabus deal with?

The MSc data science syllabus deals with a large set of real-world data by covering the key subjects, methods, and theories of Calculus, Descriptive Statistics, and Programming. The curriculum consists of basic courses on data analysis and understanding the intricacy of the data environment. The elective courses are more heavily weighted towards in-depth data analysis for students to understand.

Which of the MSc data science subjects is the toughest?

Among the MSc data science subjects, the toughest topics are probability, statistics, and linear algebra. If interested in this course, prospective students must have a solid understanding of these mathematical concepts. As a result, applicants will find it less difficult to comprehend the algorithms and statistical methods used in data analysis.

What is the course content of MSc data science?

The course content of MSc data science includes high-level programming languages such as distributed databases, big data management, data analytics, statistical modelling, programming with data, and more. It also covers data management systems for both structured and unstructured data, machine learning algorithms, and the mathematical underpinnings of data science, including probability, linear algebra, and modelling.

Can I do an MSc in Data Science without Maths?

No, students cannot do an MSc in Data Science without Maths. Mathematics is necessary for professions in data science since machine learning algorithms, executing analyses, and formulating hypotheses from data all demand it. Although it is not a mandatory prerequisite, maths is crucial for your educational and professional path in data science.

Are MSc Data Science subjects difficult to understand for an average student?

No, the MSc Data Science subjects are not difficult to understand for an average student as graduates are already familiar with the topics they studied at the undergraduate level. The degree to which students find the MSc Data Science syllabus easy or difficult to understand depends largely on their interest levels. Overall, students will find the course easy if they are well-versed in the fundamental topics studied during their bachelor's degree.

Which colleges offer the best MSc data science course curriculum?

A few colleges that offer the best MSc data science course curriculum are the University of Kalyani, Techno India University, Manipal University, GITAM University, Fergusson College, Dhirubhai Ambani Institute of Information and Communication Technology, Annamalai University, Amity University, etc.

Is the MSc data science syllabus for distance learning the same as the regular curriculum?

Yes, the MSc data science syllabus for distance learning is the same as the regular curriculum, however, minor changes are expected. The online course includes MSc data science subjects such as Programming with R and Python, Database Management, Bayesian Statistical Modelling, Longitudinal Data Analysis, Stochastic Processes, and more.

What are the best reference books to study MSc data science subjects?

The best reference books to study MSc data science subjects are available offline as well as online which students can freely download in the form of PDF. Some of the best reference books available are listed below.

  • R for Data Science by Hadley Wickham and Garret Grolemund
  • Python for Data Analysis by Wes McKinney
  • Python Data Science Handbook by Jake VanderPlas
  • Introduction to Probability by Joseph K Blitzstein and Jessica Hwang

What are the best MSc data science project topics?

Project activities help students get hands-on experience to understand the MSc data science syllabus and subjects in an advanced way. Listed below are some of the best MSc data science project topics:

  • Road Lane Line Detection
  • Recognition of Speech Emotion
  • Human Action Recognition
  • Climate Change Impacts on the Global Food Supply
  • Gender and Age Detection with Data Science
  • Forest Fire Prediction
  • Fake News Detection
  • Driver Drowsiness Detection in Python

What are the methodologies and techniques used to teach MSc data science subjects?

The methodologies and techniques used to teach MSc data science subjects are adaptive and offer communicative-based learning to graduates. Here are some general teaching strategies utilised:

  • Conceptualised Learning
  • Group and Individual Projects
  • Practical Lab Sessions
  • Semester Abroad Opportunities
  • Seminars about the scope and future
  • Talks from guest speakers experienced in the field
  • Traditional and Unorthodox Classroom-Based Teaching

What is the course structure for MSc Data Science?

The course structure for MSc Data Science includes four semesters and consists of both core and elective subjects. Furthermore, hands-on workshops and seminars improve students' comprehension of complex concepts. They are further exposed to specialised curricula related to their area of specialisation throughout their second year. Here is the course structure for the MSc in data science:

  • Core and Main Subjects
  • Elective and Optional Subjects
  • IV Semesters
  • Practical Meet-ups and Visits
  • Thesis Submission (Research Project)

What are the fourth-semester MSc data science subjects?

The list of fourth-semester MSc data science subjects includes Exploratory Data Analysis, Industry Projects, Research Publications, Research Work, etc. In this semester, students will need to complete projects, research on a given topic to produce a thesis, internships, and more.

What are the third-semester MSc data science subjects?

The list of third-semester MSc data science subjects includes Genomics, Natural Language Processing, Spatial Modelling, Summer Project, and more. In this semester, students will need to choose elective subjects based on their interests or career objectives to gain additional knowledge related to the field.

What are the second-semester MSc data science subjects?

The list of second-semester MSc data science subjects includes Advanced Python Programming for Spatial Analytics, Design and Analysis of Algorithms, Image Analytics, Machine learning, Mathematical Foundation for Data Science – II, Regression Analysis, and so on.

What are the first-semester MSc data science subjects?

The list of first-semester MSc data science subjects includes Fundamentals of Data Science, Introduction to Geospatial Technology, Mathematical Foundation for Data Science, Principles of Data Science, Probability And Distribution Theory, Python Programming, etc.

What is the purpose of the MSc data science syllabus?

The purpose of the MSc data science syllabus is to develop skilled, analytical scientists and researchers who can push the boundaries of global technology and tackle the most difficult data challenges. Its goal is to help prospective aspirants gain a deep comprehension of the topics covered and the overall field of study.

How many semesters are there in MSc data science?

There are four total semesters in MSc data science given that it is a two-year full-time postgraduate degree programme. Both core and elective subjects are offered in the course curriculum to provide candidates with different possibilities during their two years of study. The semester-based courses are designed to give students a deep understanding of the ideas and specifics.

What are the subjects in MSc data science?

The subjects in MSc data science are Design and Analysis of Algorithms, Exploratory Data Analysis, Fundamentals of Data Science, Genomics, Machine learning, Natural Language Processing, Principles of Data Science, Probability and Distribution Theory, Regression Analysis, Research Publication, and more.

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