M.Tech. in DSA

M.Tech. in DSA

M.Tech. in Data Science and Analytics

This is a two year post-graduate engineering programme supported by multidisciplinary faculty. Data is the new “oil”. Read more about this important field here.

This is a unique program 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.

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The program is designed to educate data science leaders and help them earn a professional degree. The program 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 an 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 a M.Tech. in DSA 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 program includes an internship and a capstone project to foster interaction with the data science community and offers opportunities for 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

Semester I

Semester II

Linear Algebra for Machine Learning
Big Data Management and Platforms
Introduction to Prob & Stats
Advanced Statistics
Operations Research
Research and Experimental Design
Information Visualization with Data
Advanced Machine Learning for Data Scientists
Data Structure and Management for Data Science
Software Design for Data Science
Scalable Data systems and Algorithm
Data Mining and Exploring
Contemporary Analytics
Leadership Skill-Teams, Strategies and Communications
R- Programming / Python Programming
Text Visualization, Summarization, Customer Opinion Mining
SQL, NOSQL
Neural Network, k-NN, k-means Clustering, CNN, SVM

Year Two

Semester III

Semester IV

Deep Learning
Business Intelligence
Natural Language Processing
Project Management
Data engineering Platforms for Analytics
Elective Courses (any three)
Machine Learning and Predictive Analytics
Internship
Cloud computing
Capstone Project
Human Centered Data Science
Modeling in Operations Management
Live Project with Data Analytics

Electives

  • Digital Marketing Analytics
  • HR Analytics
  • Media Analytics
  • Real Time Analytics
  • Supply Chain Analytics
  • Financial Analytics
  • Ethical and Legal Considerations in Big Data Analytics
  • Healthcare Analytics

The above list of electives is open and may be offered if sufficient demand exists.