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| Assesment Pattern | |||||||||||||||||||||||||||||||||||||||||
THEORY:
LAB:
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| Examination And Assesments | |||||||||||||||||||||||||||||||||||||||||
For Theory Courses:
Continuous Internal assessment (CIA) forms 50% and the end semester examination forms the other 50% of the marks in theory. CIA marks are awarded based on their performance in assignments, MSE and class assignments (Quiz, presentations, Moodle based tests, problem solving, minor projects, MOOC etc.). The MSE & ESE for each theory paper is of two & three hours respectively. CIA I and CIA III are conducted by respective faculty in the form of different types of assignments. MSE will be held for odd semesters in the month of September and even semesters in the month of February. ESE: The theory as well as practical courses are held at the end of the semesters.
For Lab Courses: CIA I, CIAII, CIA III, CIAIV are conducted in different weeks of the semester. | |||||||||||||||||||||||||||||||||||||||||
| Department Overview: | |||||||||||||||||||||||||||||||||||||||||
The Department of Statistics and Data Science, established in the year 2022, strives to provide a dynamic research environment and effective education, including excellent training in scientific data collection, data management, methods and procedures of data analysis. Our curriculum adheres to worldwide standards to provide the best possible research and educational opportunities. It offers a perfect blend of statistical knowledge with tools and data science techniques required to explode, analyze and interpret the complex data of the modern world. The curriculum and teaching pedagogy foster higher-order thinking and research skills, which equip students for the dynamic and ever-evolving data industry. Well-designed co-curricular activities organized by the department are aimed at the holistic development of students. The skills imparted through various programs offered by the department aim to develop data professionals who would strive to contribute to the development of societyand the achievement of national goals. | |||||||||||||||||||||||||||||||||||||||||
| Mission Statement: | |||||||||||||||||||||||||||||||||||||||||
| Introduction to Program: | |||||||||||||||||||||||||||||||||||||||||
The undergraduate programme of BSc (Data Science, Mathematics) is a novel three-year dual major, interdisciplinary degree programme. The students are given the option to pursue fourth year for the award of Honors degree. It has been specifically designed for the current Information and Knowledge Creation Era. This programme will equip the students to learn about querying, acquiring and understanding the categories of data and its analysis, methods to to extract insights from data and to visualize and report the results. It also helps students to formulate, develop and use quantitative and mathematical models in a logical manner and to understand classical and modern data-analytics techniques, artificial intelligence techniques and statistical machine learning concepts. It effectively blends the theoretical foundations of data science with the mathematical models and concepts. The progressive approach in the design of the curriculum facilitates students to pursue research/career in the areas of Data Science or Mathematics.
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| Program Objective: | |||||||||||||||||||||||||||||||||||||||||
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO1: Acquire a fundamental understanding of the principles and analytical techniques of Data Science to effectively generate useful information from structured and unstructured datasets.PO2: Develop relevant computational and statistical techniques for data science to gain insights from complex and high dimensional data. PO3: Demonstrate understanding of the interdisciplinary nature of data for research and development using machine learning techniques and data science algorithms. PO4: Understand and apply fundamental principles, concepts and methods of mathematics. PO5: Demonstrate problem solving skills using mathematical techniques. PO6: Apply appropriate methods and tools for research and development in the chosen discipline. | |||||||||||||||||||||||||||||||||||||||||