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 in order to understand numerous phenomena in relation to a large set of real-world data. 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.
The MSc Data Science syllabus covers the major subjects, tools, and theories of Calculus, Descriptive Statistics, and C-Programming in order to understand different occurrences in relation to a large amount of actual data. Statistics, mathematics, coding, machine learning, and other topics are commonly covered in M.Sc. data science courses.
The M Sc Data Science is a postgraduate program that has a course duration of two years further divided into four semesters. Its concept is to produce competent and analytical scientists and researchers who can unravel even the most challenging data in order to push the world's technological frontiers.It makes the learner aware of the conditions and hardships they must become acclimated to in order to survive in this harsh and competitive world while still pursuing something that would lead them to their goal. Students can obtain a detailed understanding of the field and subject matter by taking the M.Sc. Data Science course.
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 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 visualization, 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 first year syllabus of MSc Data Science is provided below-
|MSc Data Science Syllabus Semester 1||MSc Data Science Syllabus 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|
The second year MSc Data Science syllabus is provided below-
|MSc Data Science Syllabus Semester 3||MSc Data Science Syllabus 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|
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 course of the two years, the curriculum includes both core and elective subjects. The following is the list of MSc Data Science subjects:
Some of the common subjects that a student of MSc Data Science has to study include coding, statistics, data structures, algorithms and related subjects. The list of MSc Data Science common subjects depends on the institute in which students are enrolled.
Apart from these basic courses, you will have to select optional subjects to fulfill your credits. The MSc Data Science Common Subjects for All Semester are-
|MSc Data Science Subjects Semester-3||MSc Data Science Subjects Semester-4|
|Group-A: Data Analytics||Group-A: 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|
|Group B: Data Mining||Group B: 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|
|-||Social Network Aggregators|
Other MSc Data Science Optional Subjects are-
|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 MSc Data Science may vary depending on the academic institution.
The field of data science is fairly broad. Within the broad field of data science, there are several sub-domains. As a result, data science has many specializations. You can develop solid foundations in each of these specializations with the help of the MSc Data Science syllabus. This will also be highly beneficial if you want to specialize in a field at a higher level to get more in-depth information about that particular field.
The MSc Data Science Specializations 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 would 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-
|MSc Data Science Subjects Semester 1||MSc Data Science Subjects 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|
|Integrated Disaster Management||–|
Give below is the second year syllabus of MSc Data Science Distance program-
|3rd Semester||4th Semester|
|Spatial Modeling||Industry Project|
|Summer Project||Research Work|
|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.
There are several distinct entrance exams available nowadays. Entrance exams come in a variety of forms. There are entrance exams for universities, states, and even the national level.
Multiple Choice Questions (MCQs) and Numerical Question Answers (NQAs) are frequently used in MSc Data Science admission exams. General aptitude, logical analysis, and core science disciplines are the subjects.
Some of the significant entrance exams in India are listed here if you want to pursue an MSc Data Science.
The Joint Admission Test (IIT JAM) is a national-level entrance exam conducted by the Indian Institute of Technology. For admission to master's and post-diploma programs, the exam is conducted on a rotational basis.
The Central Universities Entrance Test (CUET) is a national entrance test that offers students from all around the nation a common starting point. To be eligible for admission, you must pass the CUET exam if you want to apply to any central university or any other institution in the nation.
BITS, a premier university of higher education in information technology and sciences in India, conducts the Birla Institute of Technology and Science Admission Test (BITSAT).
Some other M Sc Entrance Exams are-
The MSc Data Science Entrance Exams usually follow a very common pattern to evaluate students for admission into the course. The overview of MSc Data Science Entrance Exams Syllabus is given below-
M.Sc. Data Science books are provided by numerous authors and publishers both online and offline. The M.Sc. Data Science syllabus pdf, which is accessible online, is designed to aid with conceptual understanding. Students should invest in reference books after conducting thorough study. The best M.Sc. data science books include the following:
|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 M.Sc Data Science course structure is intended to contain both core and optional studies. The programme is structured into four semesters over two years and includes the Data Science M.Sc syllabus
In the first year, students are only introduced to fundamental knowledge through basic MSc Data Science subjects. Students are introduced to particular syllabus related to their specialization during the second year. The knowledge of theoretical concepts is also enhanced through practical classes. According to the M.Sc Data Science syllabus, the thesis submission and final assessments must be completed by the conclusion of the fourth semester. The following is the course structure:
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