Associate Content Manager
M.Sc in Data Science is a two-year postgraduate course that deals with the major disciplines, techniques, and theories of Calculus, Descriptive Statistics, and C-Programming. The minimum Master in Data Science eligibility is for the students to have pursued a UG in Bachelor's degree of mathematics, statistics, or computer science with a minimum aggregate of 50% from a recognized institute. Students are supposed to pass the national level exams or other entrance exams conducted by colleges for admission.
See Also: Data Science Courses
The MSc Data Science selection process in India is primarily based on the student's entrance exam performance. Admission to the M.Sc Data Science is only offered to students who have completed the minimum level of the respective university and meet the entry requirements.
Some of the main subjects are Statistics, Coding, Business Intelligence, Data Structures, Mathematics, and Machine Learning. Top companies hiring for M.Sc Data Science Graduates are Bridgei2i Analytics, Tiger Analytics, LatentView, Absolutdata, Innovaccer, and TEG Analytics.
Table of Contents
- MSc Data Science Entrance Exam Syllabus
- MSc Data Science Syllabus in Chennai Mathematical Institute
- MSc Data Science Syllabus Sri Sathya Sai Institute
- MSc Data Science Syllabus in Sharda University
- MSc Data Science Syllabus in Kalyani University
- MSc Data Science Top Colleges
- MSc Data Science Books
- MSc Data Science Syllabus: FAQs
MSc Data Science Course Details
| Course Name | MSc Data Science |
| Course Level | Postgraduate |
| Duration | 2 Years |
| Admission Process | Merit + Entrance Exam |
| Top Entrance Exam | CUCET, NIMSEE, IIT JAM, CUET, JNUEE |
| Eligibility | 50% aggregate marks from any recognized college/university |
| Top Colleges | Loyola College, Chennai; Vellore Institute of Technology (VIT), Vellore; Fergusson College, Pune; and Annamalai University, Chidambaram |
| Average Fees | INR 2 Lakhs to INR 4 Lakhs |
MSc Data Science Syllabus
| Semester I | Semester II |
|---|---|
| Mathematical Foundation For Data Science | Mathematical Foundation For Data Science – II |
| Probability And Distribution Theory | Design and Analysis of Algorithms |
| Introduction to Geospatial Technology | Advanced Python Programming for Spatial Analytics |
| Principles of Data Science | Regression Analysis |
| Fundamentals of Data Science | Machine learning |
| Python Programming | Image Analytics |
| Semester III | Semester IV |
| Spatial Modeling | Industry Project |
| Summer Project | Research Work |
| Genomics | Research Publication |
| Natural Language Processing | Exploratory Data Analysis |
MSc Data Science Subjects
MSc Data Science syllabus must be looked at before choosing the course. The detailed syllabus is mentioned below:
MSc Data Science First Year Subjects
- Mathematical Foundations For Data Science: It covers, in particular, the basics of signal and image processing, imaging sciences and machine learning
- Probability And Distribution Theory: A probability distribution gives the possible outcomes of random events. It is also defined as the set of possible outcomes of a random experiment based on the underlying sampling space.
- Design and Analysis of Algorithms: Algorithm analysis is a part of computational complexity theory, which provides theoretical estimation for the required resources of an algorithm to solve a specific computational problem.
- Introduction to Geospatial Technology: Geospatial Technology includes Geographic Information Systems, Remote Sensing, and Global Positioning Systems. It enables us to acquire data and use it for analysis, modeling, simulations, and visualization.
- Advanced Python Programming for Spatial Analytics: This is a Python module that implements various iterator building blocks that together form an "iterator algebra" that allows you to efficiently build tools in the Python language.
MSc Data Science Second Year Subjects
- Spatial Modeling: Spatial modeling is an important tool for performing geospatial analysis to understand the world and guide decision-making. In GIS, a spatial model is a formal language for expressing the mechanics of geographic processes and designing analytical workflows.
- Genomics: Genomics is an interdisciplinary branch of biology focused on genome structure, function, evolution, mapping, and editing.
- Research Publication: Publications make scientific information publicly available and enable the rest of the academic audience to assess the quality of research.
- Natural Language Processing: Natural language processing is a subfield of linguistics, computer science, and artificial intelligence that deals with the interaction between computers and human language.
- Exploratory Data Analysis: Exploratory data analysis refers to the essential process of conducting an initial investigation of data to discover patterns, detect anomalies, test hypotheses, and validate assumptions using summary statistics and graphs.
MSc Data Science Entrance Exam Syllabus
| Sections | Topics |
|---|---|
| Quantitative Aptitude | Logical Reasoning and Verbal Ability |
| Computer Science | Data structures, Programming concepts, Algorithms |
| Mathematics | Sequences and Series of real numbers, Functions of one real variable, Functions of two or three variables, Integral calculus, Differential equations, Linear algebra |
| Statistics | Probability, Random Variables, Standard Distributions, Joint Distributions, Limit Theorems |
MSc Data Science Syllabus in Chennai Mathematical Institute
MSc Data Science Syllabus in Chennai Mathematical Institute
| Semester I | Semester II |
|---|---|
| Mathematical Methods- Analysis | Linear Algebra and its Application |
| Probability and Statistics with R | Data Mining and Machine Learning |
| Progamming and Data Structures with Python | Algorithm |
| Discrete Mathematics | Distributed Computing |
| RDBMS, SQL and Visualization | Big Data with Hadoop |
| Semester III | Semester IV |
| Predictive Analytics- Regression and Classification | Elective 3 |
| Advanced Machine Learning | Elective 4 |
| Elective 1 | Elective 5 |
| Elective 2 | Elective 6 |
MSc Data Science Syllabus Sri Sathya Sai Institute
MSc Data Science Syllabus Sri Sathya Sai Institute is mentioned below
| Semester I | Semester II |
|---|---|
| Computational Linear Algebra | Stochastic Processes |
| Inferential Statistics | Regression Methods |
| Multivariate Analysis | Optimization Techniques |
| Computer Organization and Architecture | Distributed Systems |
| Design and Analysis of Algorithms | Software Engineering |
| Software lab in Python | Software lab in R |
| Awareness Course– I: Education for Life | Awareness Course – II: God, Society and Man |
| Semester III | Semester IV |
| Machine Learning | Elective - I |
| Practicals: Machine Learning | Elective - II |
| Big Data Analytics | Elective - III |
| Practicals: Big Data Analytics | Project* |
| Data Visualization | Comprehensive Viva voce |
| Practicals: Data Visualization | Awareness Course –IV:Wisdom for Life |
| Hadoop Programming | |
| Practicals: Hadoop Programming | |
| Seminar | |
| Project Interim Review* | |
| Awareness Course –III: Guidelines for Morality |
MSc Data Science Syllabus in Sharda University
MSc Data Science Syllabus in Sharda University is mentioned below
| Semester I | Semester II |
|---|---|
| Foundations of Data Science | Numerical Methods with Programming |
| Statistical Methods | Regression Analysis and PredictiveModels |
| Mathematics for Machine Learning | Statistical Data Preparation& Analytics |
| Probability Theory and Distributions | Advanced Big Data and Text Analytics |
| Next Generation Databases | Data Mining & Artificial Intelligence |
| Practicals | Community Connect |
| Semester III | Semester IV |
| Inferential Statistics | Elective-I (Online/Offline Courses) |
| Multivariate Data Analysis | Elective-II (Online/Offline Courses) |
| Soft Computing Techniques | - |
| Exploratory Data Analysis and Visualization | - |
| Open elective (GE) | - |
MSc Data Science Syllabus in Kalyani University
MSc Data Science Syllabus in Kalyani University is mentioned below:
| Semester I | Semester II |
|---|---|
| Mathematics & Statistics - I | CBCS Open Choice Course |
| Algorithms & Data Structure | Mathematics & Statistics - II |
| Database Management Systems | Machine Learning |
| Introduction to Data Science & Artificial Intelligence | Big Data Analytics & Cloud Computing |
| Algorithms & Data Structure Laboratory with C | Machine Learning Laboratory with Python |
| Database Management Systems Laboratory | Information Visualization Laboratory |
| Statistics & Data Analysis Laboratory with R/Excel/SPSS | Big Data Analytics & Cloud Computing Laboratory |
| Communicative English &HR Management– I | Communicative English & HR Management – II |
| Semester III | Semester IV |
| Entrepreneurship & IPR | Dissertation (Final) |
| Research Methodology | Seminar |
| Elective – I | Grand Viva |
| Elective – II | - |
| Elective-III (Student’s choice) | |
| Review on Frontiers in Data Science | |
| Project/Training/Seminar |
MSc Data Science Top Colleges
| Name of the College | Fees |
|---|---|
| Loyola College, Chennai | - |
| Vellore Institute of Technology (VIT), Vellore | INR 1.61 Lakhs |
| Fergusson College, Pune | INR 17,955 |
| Annamalai University, Chidambaram | INR 27,495 |
| Manipal University, Manipal | INR 3 Lakhs |
| GITAM University, Visakhapatnam | INR 2 Lakhs |
| Amity University, Gurgaon | INR 2.09 Lakhs |
| Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar | INR 7.3 Lakhs |
| Techno India University, Kolkata | - |
| University of Kalyani, Kalyani | INR 1.65 Lakhs |
MSc Data Science Books
| Name of the Book | Fees |
|---|---|
| Practical Statistics for Data Scientists | Peter Bruce and Andrew Bruce |
| Introduction to Probability | Joseph K. Blitzstein and Jessica Hwang |
| Python for Data Analysis | Wes McKinney |
| Introduction to Machine Learning with Python: A Guide for Data Scientists | Andreas C. Müller and Sarah Guido |
| Python Data Science Handbook | Jake VanderPlas |
| R for Data Science | Hadley Wickham and Garret Grolemund |
| Deep Learning | Ian Goodfellow, Yoshua Bengio, and Aaron Courville |
| Mining of Massive Datasets | Jure Leskovec, Anand Rajaraman, Jeff Ullman |
| Understanding Machine Learning: From Theory to Algorithms | Shai Shalev-Shwartz and Shai Ben-David |
MSc Data Science Syllabus: FAQs
Ques. What is MSc Data Science?
Ans. M.Sc in Data Science is a two-year postgraduate course that deals with Calculus, Descriptive Statistics, and C-Programming in order to understand the big set of real-world data.
Ques. Who can do MSc Data Science?
Ans. Candidates with Bachelor's degree of mathematics, statistics, or computer science with a minimum aggregate of 50% from a recognized institute can pursue MSc Data Science.
Ques. What are the top colleges for MSc Data Science?
Ans. Loyola College, Chennai;Vellore Institute of Technology (VIT), Vellore;Fergusson College, Pune; and Annamalai University, Chidambaram are the top colleges for MSc Data Science.
Ques. What are the electives in MSc Data Science?
Ans. The electives in MSc Data Science:
- Computational Linguistics – Advanced Python
- Data Structures, Objects, and Algorithms in Python
- Time Series
- Optimization
- Advanced Analytics and Applied Math for Streaming and High Dimension Data and Applications.
Ques. What are the core subjects of MSc Data Science?
Ans. Statistics, Mathematics, Computer Science, and Business are the core subjects of MSc Data Science.
Ques. What can be done after MSc Data Science?
Ans. Courses that can be done after MSc Data Science:
- PhD
- MBA
- M.Phil
Ques. What are the project topics of MSc Data Science?
Ans. The project topics of MSc Data Science:
- Climate Change Impacts on the Global Food Supply.
- Fake News Detection.
- Human Action Recognition.
- Forest Fire Prediction.
- Road Lane Line Detection.
Ques. What are the job options after MSc Data Science?
Ans. The job options after MSc Data Science:
- Data Analytics.
- Business Analyst.
- Data Analytics Manager.
- Data Architect.
- Data Administrator.
- Business Intelligence Manager.
Ques. What is the average salaryof MSc Data Science graduate?
Ans. Data Scientist salary in India with less than 1 to 8 years ranges from INR 4.5 Lakhs to INR 26 Lakhs with an average annual salary of INR 10.5 Lakhs.
Ques. Is it worth doing MSc Data Science?
Ans. Yes, it is worth doing MSc Data Science because there is scope for huge data-related operations such as data scientists, data analytics, big data managers, and data architects.
![Chandigarh University - [CU]](https://staticstage.collegedunia.com/public/college_data/images/appImage/1614949268bg.png?tr=h-100,w-200,c-force)
![Banaras Hindu University - [BHU]](https://staticstage.collegedunia.com/public/college_data/images/appImage/25924_BHU_APP.jpg?tr=h-100,w-200,c-force)
![Vellore Institute of Technology - [VIT University]](https://staticstage.collegedunia.com/public/college_data/images/appImage/1614064870cover.jpg?tr=h-100,w-200,c-force)