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| Introduction to Program: | |||||||||||||||||||||||||||
MSc in Data Analytics is a six trimester inter-disciplinary post-graduate degree programme conducted by Department of Statistics and Data Science. This programme is designed for working professionals and graduates who want to launch their career in the in-demand and lucrative field of data analytics. As organizations are looking ways to exploit the power of big data, technology professionals who are experienced in analytics are in high demand. This programme aims to offer thorough knowledge of the theory and practice of data analytics to become a leading practioner in the field of data analytics. This programme accommodates a wide audience of learners whose specific interests in data analytics may be either technical or business focused. | |||||||||||||||||||||||||||
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO1: Problem Analysis and Design: Ability to identify analyze and design solutions for data analytics problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines.PO2: Modern software tool usage: Acquire the skills in handling data analytics programming tools towards problem solving and solution analysis for domain specific problems. PO3: Societal and Environmental Concern: Utilize the data analytics theories for societal and environmental concerns. PO4: Professional Ethics: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices. PO5: Applications in Multidisciplinary domains: Understand the role of statistical approaches and apply the same to solve the real life problems in the fields of data analytics. PO6: Project Management: Apply the research-based knowledge to analyse and solve advanced problems in data analytics. | |||||||||||||||||||||||||||
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| Examination And Assesments | |||||||||||||||||||||||||||
Out of the maximum marks allotted to the respective course, 70% marks will be considered as CIA and remaining 30% as ESE based on the combinations of the evaluation components (CAC and CAT) as mentioned below,
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