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.
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.
The following table highlights the first-year MSc Data Science syllabus:
Semester 1 | Semester 2 |
---|---|
Advanced Database Management Systems | Calculus and Linear Algebra for Data Scientists |
Applied Probability and Probability | Data Analysis and Visualisation |
Distribution | Distributed Algorithms & Optimisation with Hadoop, Spark |
SQL Programming | Advanced Machine Learning |
Python and R Programming | Deep Learning |
Computational Mathematics | Stochastic Processes |
Statistical Inference |
The following table highlights the second-year MSc Data Science syllabus:
Semester 3 | Semester 4 |
---|---|
Cloud Native Development | Natural Language Processing |
Data Structures and Algorithms | Applied 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 |
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:
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:
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 Retrieval | Business Intelligence |
---|---|
Number Theory and Cryptography | High-Performance Computing (HPC) |
Pattern Recognition | Information Security & Cryptography |
Regression Analysis | Predictive Analytics |
Theory of Computation | Parallel and Distributed Computing |
Time Series Analysis | Soft Computing |
For Data Mining:
Artificial Intelligence | Computer Graphics |
---|---|
Computer Networks | Image Processing |
Clustering Techniques | Network Security |
Graph Theory and Discrete Mathematics | Natural Language Processing |
Text Mining | Signal Processing |
Web Intelligence | Social Network Aggregators |
The following table shows some other elective MSc Data Science subjects:
Deep Learning | System Dynamics Simulation |
---|---|
IoT Spatial Analytics | Spatial User Interface Design and Implementation |
Research Modelling and Implementation | Genomics |
Exploratory Data Analysis | Multivariate Analysis |
Stochastic Process | Programming for Data Science in R |
Hadoop | Image and Video Analytics |
Internet of Things | Identification and Data Collection |
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.
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 Specialist | Market Data Analytics |
Cybersecurity Data Analysis | Deep Learning |
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-
The table below provides the detailed first-year MSc Data Science Syllabus for distance programs-
Semester 1 | Semester 2 |
---|---|
Mathematics for Spatial Sciences | Spatial Big Data and Storage Analytics |
Applied Statistics | Data Mining and Algorithms |
Fundamentals of Data Science | Machine learning |
Python Programming | Advanced Python Programming for Spatial Analytics |
Introduction to Geospatial Technology | Image Analytics |
Programming for Spatial Sciences | Spatial Data Base Management |
Business Communication | Flexi-Credit Course |
Cyber Security | – |
Integrated Disaster Management | – |
Given below is the second-year syllabus of the MSc Data Science Distance program-
3rd Semester | 4th Semester |
---|---|
Spatial Modeling | Industry Project |
Summer Project | Research Work |
Web Analytics | – |
Artificial Intelligence | – |
Flexi-Credit Course | – |
Predictive Analytics and Development | – |
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.
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 Name | Author/s |
---|---|
Practical Statistics for Data Scientists | Peter Bruce and Andrew Bruce |
Introduction to Probability | Joseph K Blitzstein and Jessica Hwang |
Introduction to Machine Learning with Python: A Guide for Data Scientists | Andreas C Müller and Sarah Guido |
Python for Data Analysis | Wes McKinney |
Python Data Science Handbook | Jake VanderPlas |
R for Data Science | Hadley Wickham and Garret Grolemund |
Understanding Machine Learning: From Theory to Algorithms | Shai Shalev-Shwartz and Shai Ben-David |
Deep Learning | Ian Goodfellow, Yoshua Bengio, and Aaron Courville |
Mining of Massive Datasets | Jure Leskovec, Anand Rajaraman, Jeff Ullman |
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:
Candidates will learn the following skills after completing the MSc data science course curriculum:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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:
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:
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.
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.
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.
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.
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.
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.
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.