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M.Tech. in Data Science and Engineering

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Overview

The programme in Data Science and Engineering, firstly, lays an adequate foundation in data science, mathematics and programming. The programme also lets the student acquire specialized knowledge and insights with a balance in coverage of theory and practice to be able to build effective big data applications.

Objective

The objectives of the programme are to train and educate the students on the following:

  • Data life cycle and associated functions
  • Programming for data science along with libraries
  • Mathematical pre-requisites required for data science and engineering
  • Distributed computing concepts and techniques for Big data
  • Mechanisms related to data storage, data access, data transfer, visualization and predictive modelling in a cost-effective manner
  • Key aspects of distributed processing in big data analytics
  • Data Visualization
  • Machine learning

Highlights

  • Elective Specialisation

    Electives are grouped so that students can specialise in a chosen field. Students can also opt for elective courses across multiple streams given affordability and approval from a course leader, HoDs and Deans, and this requires that guidelines are followed

  • Online Elective

    Students can choose elective courses online through MOOCs, SWAYAM, NPTEL and other equivalent platforms, adhering to guidelines prescribed by the University

  • International Competitions

    Students can also earn 3 or 4 credits by participating in international competitions like technical presentations, conferences and publications in journals and winning awards. Students who achieve this can be exempted from one of the elective courses in the program

  • Group Project

    A group of up to 5 students can come together to work on an interdisciplinary project in consultation with faculty. Students are required to develop a report for assessment and demonstrate the working of the product, and the project needs to be approved by a committee before it is executed. The IPR rights of all such work lies with the University only

  • Industry Internship or Project

    A student can opt for an internship in an industry, a business or research organisation. Alternately, students can undertake a mini-project which requires self-directed study that can be pursued with an affiliated faculty member. The internship needs to be approved by the HoD and Dean and students have to submit a report and make a presentation to a panel constituted by the HoD for assessment

  • Dissertation and Publication

    This course includes a dissertation and publication. Each student has to choose a relevant topic for a dissertation and this needs to be approved by a committee constituted by the HoD. The dissertation should then be converted into a technical paper published in reputed conferences or journals

  • Uses application-based learning.
  • Offers research opportunities.
  • Includes internship, training, and placement support with various universities worldwide.
  • Offers training by faculty members holding PhD degrees in Data Science who train students in group projects and dissertation work.
  • Includes expert guest lectures from various institutions and industries.
  • Ensures workshop, industry visits, group discussions and debates.
  • Offers small batches to encourage in-depth learning.

  • We offer accommodation in student hostels on our campus, and even offer third-party off-campus hotels where necessary to ensure students can focus on what matters the most. 
  • Our on-campus facilities include WiFi any time of the day, 24/7.
  • From a host of eateries and laundry facilities, we ensure that students have access to all facilities.
  • We encourage students to participate in co-curricular activities on campus which include sports and cultural events, both at a university level and a faculty level.
  • We help students focus on independent and sponsored research with projects and a dissertation. 

Structure

Study Domains

  • CC

    Core Courses

    A rich set of core courses in Programmeming for Data Science, Data Mining, Data processing and Advanced Data Processing with case studies using tools based on Map Reduce Paradigm

  • BM

    Basic Mathematics

    Students are required to manage pre-requisite courses in Mathematics

  • EC

    Elective Courses

    Includes electives in Artificial Intelligence, Text Mining, Big Data and Healthcare, Big Data and SDN and Natural Language Processing

  • PSI

    Project, Seminar, and Internships

    Active participation in output-based group projects, seminars, and internships in industry. Students will work in a team and exhibit the product developed and also develop a thesis by investigating into a problem in Data Science and Engineering

Course Progression

Course Credits Study Domain
Mathematics for Data Science

Theory Tutorials

 4 Basic Mathematics BM
Programmeming for Data Science

Theory Practicals

 4
Data Mining

Theory Tutorials

 4
Data Processing

Theory Tutorials

 4
Artificial Intelligence

Theory Tutorials

 4
Research Methodology & IPR

Theory

 2
Professional Communication

Theory

Key

  • Theory
  • Tutorials
  • Practicals

Course Credits
Artificial Neural Networks

Theory Practicals

 5
Advanced Data Processing

Theory Practicals

 5
Distributed Computing

Theory Tutorials

 4
Natural Language Processing

Theory Tutorials

 4
Text Mining and Visualization

Theory Practicals

 4
Value Education

Key

  • Theory
  • Tutorials
  • Practicals

Course Credits
Internship

Theory Tutorials

 4
Group Project

Theory

 8
Dissertation and Publication Phase 1

Theory

Key

  • Theory
  • Tutorials
  • Practicals

Course Credits
Dissertation and Publication Phase 2

Theory Tutorials

 24

Key

  • Theory
  • Tutorials
  • Practicals

Details

Teaching and Assessment

Student performance in each course is assessed through an assignment (with 50% weightage) and an examination (with 50% weightage). A student is required to score a minimum of 40% in each of the components and 40% overall for the successful completion of a course and for earning the corresponding credit(s)

Key Skill Development

  • Ability to apply concepts of Big Data to solve social problems in healthcare, electricity and education. 
  • Ability to extract knowledge from Big Data to gain insights into a problem domain.
  • Ability to discuss various phases of Big Data life cycles along with the associated functions.
  • Ability to understand security and privacy policies and efficient distributed computing solutions, that are appropriate for data processing. 
  • Ability to discuss techniques for data visualisation and apply computational and statistics concepts to work with real data in various fields.
  • Ability to design solutions for Big Data processing problems and apply suitable techniques for data visualisation. 
  • Ability to infer meaningful insights from data. 
  • Ability to use appropriate methods, tools and frameworks to implement various steps in a typical data life cycle created for a Big Data application 
  • Ability to implement distributed solutions for data processing using tools such as Hadoop. 
  • Ability to test solutions of Big Data problems from both functional and nonfunctional requirement perspectives. 
  • Ability to manage information, develop technical reports and make presentations. 
  • Ability to build, manage and lead a team to successfully complete projects, communicate across teams and organisations to achieve professional objectives. 
  • Ability to work under various constraints to meet project targets.
  • Ability to adapt to the chosen profession by continuously upgrading knowledge and understanding through life-long learning

Careers

  • Researcher

  • Data Scientist

  • Data Engineer

  • Data Analyst

  • Big Data Engineer

Admissions

Eligibility

Candidates must have a

  • BE or B. Tech or equivalent degree in:
    • Electronics and Communication Engineering;
    • Computer Science and Engineering; 
    • Electrical and Electronics Engineering; 
    • Automobile/​Automotive Engineering;
    • Mechanical Engineering
    • Aerospace/​Aeronautics Engineering;
    • Civil Engineering;
    • Bio-Medical Engineering;
    • Mechatronics;
    • Information Science;
    • Telecommunication Engineering
    • Instrumentataion Engineering; or
    • Medical Electronics

Candidates who belong to SC/ST categories are given a different qualifying mark as per government notifications.

Application Process

University Quota

Candidates seeking admission are required to contact the admissions department for more details.

Through Government of Karnataka Quota 

  1. PGCET, Counselling and Selection (PGCET Code T945)
  2. Download the admission form 
  3. Submit completed form and documents at the University Admissions Office
  4. Pay fees online or through DD at the University admission Office. See full instructions.

Fees & Scholarships

Government Seats:

  • As per Government of Karnataka for Indian nationals

University Seats

  • Refer Fee Structure

Eligibility

  • Candidates should have the equivalent qualification approved by Association of Indian Universities.
  • Should have proof of proficiency in English with a minimum TOEFL score of 8

Application Process

University Quota

Candidates seeking admission are required to contact the admissions department for more details.

Through Government of Karnataka Quota

  1. PGCET, Counselling and Selection (PGCET Code T945)
  2. Download the admission form
  3. Submit completed form and documents at the University Admissions Office
  4. Pay fees online or through DD at the University admission Office. See full instructions.

Fees & Scholarships

NRI/​Foreign Students

  • USD 6000 + Other fee Rs.38,550 per annum

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Director - Admissions
Dr. T. Hemanth
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+91 80 4536 6616

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