M.sc data science

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Msc In Data Science

Objective: To produce world class data scientists in diverse domains. An avenue for a graduate of any discipline to earn MSc in Computer Science with a special training for making career in the domain of his / her liking by employing innovations and applications that are based on data science and artificial intelligence. This Programme is a quality assured alternative for the employed learners who generally prefer the Distance learning.

Intake: As the Programme shall be offered in blended MOOC model, there shall not be any limit on the intake; To begin with we plan to offer admissions to top 1000 scorers in the admission entrance test

Duration: 2 years, i.e., 4 semester, a specialized Programme including a rigorous training, a capstone project and one semester internships with WIAI or any such AI industry.

Eligibility: Anybody having passed XII or higher standard examination with minimum 50% score can appear for the national level entrance examination and take admission if gets through. The admissions to Master’s degree (MSc in Computer Science, specialization data science) shall be open to the graduates of this University or its equivalents.

The candidates who qualify the entrance test and not having completed their graduation could take admission to micro degrees (Certificates) and are allowed to accumulate credits by qualifying discrete Courses of this Programme by following the pre-requisite structure. The credits earned by such candidates shall be recognized and transferred by this University and be utilized by the candidates wherever applicable.

The candidates who qualify the entrance test and have taken admission in any of the Programmes of This University including a Master’s Degree Programme, can accumulate up to half of the credits of this Programme while completing the other Programme and secure Master’s in Computer Science with specialization in Data Science by earning the remaining credits in one year any time after his / her graduation.

Mode of dissemination of knowledge: Each Course shall be equivalent of 6 credits, the teaching-learning spreads over 16 weeks, ideally each week a student is expected to study 6 videos, each of 15-20 minutes, each ending with an activity that calls for the similar amount of time, attends 2 hours of tutoring and 2 hours of practical offered in a flipped classroom and, contributes to forum discussions on 2 threads by spending half an hour on each thread.

Evaluation model: For each Course a student has to secure minimum 16% of the total marks through a Semester-end examination that would carry 40% weight in the total evaluation; the 60% weight would be for the students’ attendance and submissions in response to the video-based learning and flipped classroom activities, call it continuous internal evaluation (CIE). In order to qualify a Course a student has to earn minimum 40% of the total marks in the same; There shall not be minimum passing requirement for the students score in CIE. However, a failed student can choose to improve CIE score once by paying only examination fees and by submitting all the assignments and term-work as prescribed by the on-going term. The students failed in the second attempt have to pay 30% of the tuition fees in addition to the examination fees, i.e., to avail the Course-ware of the year they will wish to appear for the CIE.

Subject to the availability of the Programme / Course in the University, there shall be no limit on the number of attempts a student takes to qualify the same. A syllabus will be valid only for a year and shall be kept up-to-date by generating a refinement almost every year. The repeaters have to follow the syllabus and assignments that are available at the time they wish to appear for the examination.

The Semester examinations shall be conducted in the months December and May.


Top college for MSc data science

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