Master of Science in Data Science and Analytics

This is a two year programme offered by the Xavier University Bhubaneswar. It will be taught by Professors from different schools of the Xavier University such as the Xavier School of Computer Science and Engineering, Xavier School of Economics, Xavier Institute of Management (XIMB).

This is a unique programme for those who are interested in shaping and creating a future world where AI and Machine Learning, Natural Language processing, and business intelligence are providing opportunities and competitive advantages. It will empower the students with Data Science skills and competencies. The students will acquire the following skills: Research Design, Data Cleansing, Data Engineering, Data Mining and Exploring, Data Visualization, Information Analytics, Ethics and Privacy, Statistical Analysis, Machine Learning, Communicating Results, and many others.

The programme is designed to educate data science leaders and help them earn a professional degree. The programme features a multidisciplinary curriculum in computer science, statistics, management and law.

Eligibility

BTech/BE, or MCA/MSc in CS/IT/CE/MATH/STAT streams, or MBA (or equiv.), with at least 55% aggregate marks or equivalent CGPA. (B.Sc. degree with exceptional record and/or work experience can be considered). Candidates will be shortlisted based on academic credentials and Statement of Purpose (SoP). Shortlisted candidates will be invited for Personal Interview leading to the final unified selection. Candidates with a sound background in Mathematics and/or Statistics with at least basic coding knowledge are preferred

You may contact the Dean's office for the program related questions (contact@xcomp.edu.in or by phone). All admission-related queries may be directed to: admission@xub.edu.in
Phone: 0674 – 2377 806

Course Duration and Structure

In order to earn Master of Data Science and Analytics degree, the student must complete a minimum of 90 credits from 27 courses of 4 credits each and 18 credits in practical projects, internship, and capstone project, which is mandatory.
The Curriculum is a blend of machine learning and programming and business oriented subjects. The programme includes an internship and a capstone project to foster interaction with the data science community and offers opportunities of applying data science knowledge.
Opportunities for various workshops such as SAS , Linux, Hadoop, Python, R will be available.

Syllabus

The course has 4 semesters spread over 2 years. Core courses and electives offered are as follows.

Year One 1/2

Semester I
1. Linear Algebra for Machine Learning
2. Introduction to Prob & Stats
3. Operations Research
4. Information Visualization with Data
5. Data Structure and Management for Data Science
6. Scalable Data systems and Algorithm
7. Contemporary Analytics
8. R- Programming / Python Programming
9. SQL, NOSQL

Semester II
1. Big Data Management and Platforms
2. Advanced Statistics
3. Research and Experimental Design
4. Advanced Machine Learning for Data Scientists
5. Software Design for Data Science
6. Data Mining and Exploring
7. Leadership Skill-Teams, Strategies and Communications
8. Text Visualization, Summarization, Customer Opinion Mining
9. Neural Network, k-NN, k-means Clustering, CNN, SVM

Year Two 2/2

Semester III
1. Deep Learning
2. Natural Language Processing
3. Data engineering Platforms for Analytics
4. Machine Learning and Predictive Analytics
5. Cloud computing
6. Human Centered Data Science
7. Modeling in Operations Management
8. Elective Courses (any one)
9. Live Project with Data Analytics

Semester VI
1. Business Intelligence
2. Project Management
3. Elective Courses (any three)
4. Internship
5. Capstone Project

Electives 1-2

1. Digital Marketing Analytics
2. HR Analytics
3. Media Analytics
4. Real Time Analytics
5. Supply Chain Analytics
6. Financial Analytics 7. Ethical and Legal Considerations in Analytics 8. Healthcare Analytics

The above list of elective is open and may be offered as per the availability of Resource Person.

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Mentors

XCOMP is currently mentored Professors of international reputation. The mentors guide in the formulation of syllabi, proper running of the course and connect us with industry. They also hold meetings with the students and faculty at regular intervals and visit them annually.

Dr Sanghamitra Mohanty

Mohanty researches topics in the fields of artificial intelligence, speech processing, image processing, natural language processing, fractal geometry, weather prediction, and high energy physics. Mohanty has thirteen Intellectual Property Rights (IPRs) on Indian Language Technology Solutions. More than 160 of her research papers have been published in international journals and conference proceedings.She has visited a number of institutions in India and abroad for academic collaboration and research.

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Dr Sanghamitra Mohanty

The mentors include;
Prof. Chitta Baral of Arizona State University, USA.
Prof. Prasant Mohapatra of the University of California, Davis, USA
Dr. Ashutosh Dutta, Senior Research Scientist at Johns Hopkins University Applied Physics Lab, USA.


FAQs

My graduation results are not yet out or I haven't received the certificates. Can I still take admission?

Yes, you can. You will be given provisional admission subjected to submission of certificates at the ealiest but not later after the commencement of the first semester examinations.

The University has a centre for Career Advisory Services who will guide you in this. XCOMP stresses a lot of importance on the curriculum content and learning process which will enble the students to fetch a good job or pursue higher studies in any university in the world.

For successful completion of M Tech in AI, good knowledge of Mathematics and basic computer engineering concepts are very important. Deep desire to learn and a passion for knowledge will result in excellence.

Depending on the availability of rooms in the hostel, one can apply for hostel rooms. They are sigle rooms with double sharing. Out station students are encouraged to stay in the campus.

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