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Master of Science (M.Sc.) in Data Science and Analysis
Duration
2 Years
Eligibility Criteria
Bachelor's degree in IT/ CS/ Mathematics/ PCM, BCA, B.Sc. Statistics/Data Science with minimum 55% marks
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This course will give students the required skills in today’s rapidly growing data science industry. The curriculum integrates computer science, statistics, and domain-specific knowledge, & students gain expertise in areas such as data mining, machine learning, big data analytics, data visualisation, etc. With hands-on experience in relevant programming languages, students are prepared for roles in e-commerce, finance, analytics, telecommunications, etc.
Apply NowCurriculum
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Mathematics for Data Science
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Python for Data Science
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Probability and Random Variables
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Linear Algebra
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Fundamentals of MS Office
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Foundation of Professional Development
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Intellectual Property Rights (IPR)
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Python for Data Science Lab
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Fundamentals of MS Office Lab
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Probability Distributions
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Statistical Inference
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Data Base Management System
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Discrete Structure
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Cyber Security and Laws
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Analytical Thinking & Problem Solving Skills
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Data Base Management System Lab
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Time Series Analysis and Forecasting
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Introduction to Artificial Intelligence & Machine Learning
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Big Data Analytics Using Hadoop
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Numerical Methods
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Introduction to Cloud Computing
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Introduction to Artificial Intelligence & Machine Learning Lab
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Big Data Analytics Using Hadoop Lab
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Paper/Book/Chapter/Patent
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Data Visualization
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Data Mining & Data Wrangling
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Optimization Techniques
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Research Project
Program Objectives POs
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POs 1
Domain knowledge: Apply advanced principles of Mathematical Sciences, Economics, and Statistics to solve complex statistical and mathematical problems.
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POs 2
Problem analysis: Identify, formulate, and analyze complex data-related problems, reaching well-supported conclusions using foundational principles of mathematics, natural sciences, and statistical methods.
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POs 3
Design/development of solutions: Develop innovative solutions for complex data science problems, designing system components or processes that meet specified needs while considering public health and safety, as well as cultural, societal, and environmental factors.
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POs 4
Conduct investigations of complex problems: Utilize research-based methods to conduct thorough investigations, including designing experiments, analyzing and interpreting data, and synthesizing information to arrive at logical conclusions.
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POs 5
Modern tool usage: Select and apply appropriate techniques, resources, and modern tools, including predictive modeling and advanced analytics, to address complex mathematical and statistical challenges while understanding their limitations.
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POs 6
Professional and Societal Impact: Apply contextual knowledge to assess societal, health, safety, legal, and cultural issues, understanding the responsibilities relevant to professional data science practice..
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POs 7
Environment and sustainability: Understand the impact of data science solutions in societal and environmental contexts, demonstrating the importance of sustainable development.
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POs 8
Ethics: Adhere to ethical principles, committing to professional ethics, responsibilities, and standards of data science practice.
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POs 9
Individual and team work: Function effectively as an individual independently and as a member or leader in diverse teams, and in multidisciplinary settings
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POs 10
Communication: Communicate effectively on complex data science activities with both the scientific community and society at large, ensuring clear exchange of instructions and information.
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POs 11
Project management and finance: Exhibit knowledge and understanding of principles in statistics, data science, AI/ML, and management, applying them to manage projects as a team member or leader in multidisciplinary environments.
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POs 12
Life-long learning: Recognize the necessity for continuous learning and have the preparedness to engage in independent, life-long learning in the context of ongoing technological advancements in data science.
Program Educational Objectives PEOs
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PEOs 1
Mastery of Data Science and Analytical Techniques: They will possess an in-depth understanding of data science principles, advanced statistical methods, and analytical techniques, enabling them to effectively tackle complex, real-world problems across various industries including business, healthcare, engineering, and social sciences.
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PEOs 2
Research Excellence and Innovation They will be equipped with the skills necessary to conduct rigorous independent research, contributing to the advancement of data science and analytics. They will develop innovative solutions and methodologies, driving progress in academic, industrial, and governmental research initiatives.
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PEOs 3
Professional Leadership and Ethical Standards They will demonstrate professional integrity and ethical responsibility in their practice. They will be prepared to assume leadership roles, effectively communicating data-driven insights to diverse audiences, and contributing to strategic decision-making processes within their organizations.