Entrance Exams
Offering Colleges (1)
Masters of Science in Computer Engineering (Specialization: Data Science and Analytics) is an analytics and data science. The Masters of Science in Computer Engineering (Specialization: Data Science and Analytics) is an analytics and data science program that leverages the strengths of the Department of Electronics and Computer Engineering (DOECE) in statistics, operations research, computing, and business by combining the Industry expertise and Faculty Members of DOECE. By blending the strengths of academics and people working in these national IT industries, graduates will learn to integrate skills in a unique and interdisciplinary way that yields deep insights into analytics problems. Analytics is an important, fast-growing field that has quickly become a key facet of business strategy. There is an increasing need for analytics-savvy employees who can think uniquely across disciplines to transform data into relevant insights for making better business decisions.
DOECE’s approach to analytics gives students the opportunity to learn directly from top national IT industries on business intelligence, developers of cutting-edge analytics techniques in machine learning, statistics, and operations research, and world leaders in big data and high-performance computing. Students will use advanced resources across DOECE and national industries such as state-of-the-art high-performance computing infrastructure for massive-scale data analytics, work in cross-disciplinary teams to solve real analytics problems for a range of companies and organizations, and more. It all adds up to a unique ability to generate deeper insights into analytics problems.
With the Masters of Science in Computer Engineering (Specialization: Data Science and Analytics) degree, graduates will enter the workplace with the computing, business, statistics, and operations research skills needed to immediately identify, analyze, and solve analytical problems for better business intelligence and decision support. One of the central objectives of the program will be to produce and place graduates ready to make both immediate and long-term impacts in business, industry, and government. In addition to making contacts with leading analytics organizations during the course of the program, students will be encouraged to attend a major analytics conference, gain valuable exposure, and be supported in their job.
The curriculum will also facilitate internal as well as external connections. To establish a strong professional network within each cohort, students will take several courses together, developing interdisciplinary working relationships and forging connections that can be relied upon throughout their careers.
The objective of the program
The main objectives of the course are to:
- Produce highly competent professionals in the field of Data Science and Analytics
- Enhance the analytical skills and problem-solving capability in handling current issues in Data Science and Analytics
- Impart the theoretical background that students will eventually call upon to strategically manage teams of Data Science and Analytics professionals
- Develop research skills in students to make them capable of carrying out sound research in Data Science and Analytics
Eligibility
The candidate pursuing the admission must hold a Bachelor’s Degree in Computer Engineering, Electronics and communication engineering, Software Engineering, Information Technology Engineering, or it's equivalent from recognized institutions. The candidate shall appear in the admission tests.
Admission Criteria
The MSc Entrance examination will be a "Computer Based Examination" of two hours duration, consisting of two sections. Section A consists of 45 questions for 50 marks. While Section-B consists of a stream specialized course with 50 questions of 1 mark each. Each question will be of objective type with multiple choice answers and the negative marking for each wrong answer is 10%.
Curricular Structure
Year I Part I
| S.N. | Course Code | Course Title | Credit | Assessment Marks | Duration Hours | Marks | Total | Remarks |
| 1 | CT803...... | Foundation of Data Science & Analytics | 4 | 40 | 3 | 60 | 100 | |
| 2 | CT803...... | Distributed & Edge Computing | 4 | 40 | 3 | 60 | 100 | |
| 3 | CT803...... | Machine Learning & Computational Intelligence | 4 | 40 | 3 | 60 | 100 | |
| 4 | CT803...... | Big Data Analytics | 4 | 40 | 3 | 60 | 100 | |
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Total | 16 | 160 | 240 | 400 |
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Year I Part II
| S.N. | Course Code | Course Title | Credit | Assessment Marks | Duration Hours | Marks | Total | Remarks |
| 1 | CT803...... | Optimization Theory & Techniques | 4 | 40 | 3 | 60 | 100 | |
| 2 | CT803...... | Information Visualization | 4 | 40 | 3 | 60 | 100 | |
| 3 | Elective - I | 4 | 40 | 3 | 60 | 100 | ||
| 4 | Elective - II | 4 | 40 | 3 | 60 | 100 | ||
| Total | 16 | 160 | 240 | 400 |
Year II Part I
| S.N. | Course Code | Course Title | Credit | Assessment Marks | Duration Hours | Marks | Total | Remarks |
| 1 | Elective - III | 4 | 40 | 3 | 60 | 100 | ||
| 2 | Elective - IV | 4 | 40 | 3 | 60 | 100 | ||
| 3 | CT903..... | Project | 4 | 40 | 3 | 60 | 100 | |
| Total | 120 | 180 | 300 |
Year II Part II
| S.N. | Course Code | Course Title | Credit | Assessment Marks | Duration Hours | Final Marks | Total | Remarks |
| 1 | CT953..... | Thesis | 16 | 100 | 100 |