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M.Tech. Programme

SEAS has launched a two year M.Tech. degree programme in Computer Science and Engineering (CSE) with a specialization in Data Science and Analytics from July, 2014. This hands-on, live case study based programme is designed to meet the exploding need for highly skilled professionals in Data Analytics.

Salient features

  • Intake: 20
  • Duration: Two years, divided into four semesters
  • Eligibility and Selection: Admission through ACPC subject to getting approval from ACPC.
  • The program will offer one year research / industry based dissertation work to the student. To graduate, the student must complete all academic requirements including dissertation in three years after getting admission to the program.
  • Program fees: Rs. 1,35,000 (One lac thirty five thousand) per year

Financial assistance

Interviews will be conducted for the students admitted to MTech program and selected candidates will be provided monthly financial assistance in the form of Teaching Assistantship (TA-ship).


The successful students will be provided degree certificate as "M. Tech. (CSE) with specialization in 'Data Science and Analytics'".

M.Tech. (CSE) with specialization in 'Data Science and Analytics'

Academic programs on 'Data Analytics' are newly developed interdisciplinary programs that combine applied mathematics, statistics, computer science and various business domains or verticals like finance, marketing, supply chain, agriculture, e-commerce etc. In today’s increasingly competitive marketplace, organizations need individuals with the requisite skills to transform the growing amount of industry, product, and customers' behavioral data into actionable information to support strategic and tactical decision making. The focus is on how "big data" can be used to help decision makers improve organizational competitiveness.

The program is designed to meet the rising need for highly skilled professionals who can transform the growing amount of data confronting all organizations into usable information for use by their decision makers. Students will get the opportunity to gain hands-on experience with a variety of analytical tools available for the purpose of structuring large data sets to unearth hidden information to allow their organizations to build and sustain a long-term competitive advantage. Students also gain experience with different software tools used for data analysis and reporting. The students will get exposure to sophisticated software tools and functions such as data mining, predictive modeling, and visual analytics using large data sets. Commercial and open-source tools would be used to conduct analyses and build prototypes using real-world case studies and data sets. Case studies can cover building predictive models in selected industries (e.g., healthcare, medicine, defense, finance, banking, energy, supply-chain etc.).


Computer Programming, Data Structures, File Organization, Database Management System, Computer Algorithms, Computer Organization, Operating System, Computer Networks, Calculus, Linear Algebra, and Statistical Methods

Programme Objectives

Programme objectives are defined as under:

  • To design and implement databases, dimensional models, and data warehousing strategies.
  • To apply advanced methods of data warehousing and data mining in a variety of organizational environments.
  • To transform large data sets into actionable information in an easy-to-understand format to support organizational decision making through the use of advanced analytical tools.
  • To manage the quality, security, and privacy of data relevant to an organization to enhance its value.
  • To manage real life complex data analytics projects to ensure delivery of a successful data analytics initiative throughout its life cycle.

Programme Outcomes

The program will enable students to

  • Assess alternative approaches and infrastructures for implementing big data analytics.
  • Evaluate the appropriate methods and tools for data analysis in specific organizational contexts, including selecting a modeling approach, building a model using appropriate tools, validating the model, and deploying the model for prediction and analysis.
  • Develop experience tackling industry and organization-specific problems and challenges using advanced analytics and computational methods.

Programme Structure

For the semester-wise curriculum of the M. Tech. Program, please click here.

Career Opportunities

The future prospects for successful students are as under:

The student will become

  • Data Scientist
  • Data analytics specialist
  • Software Engineer
    • Companies and research labs involved in data science and analytics
    • Business Verticals/Domains: e-commerce, online banking, financial services, stock brokerage, weather tracking, online retail, agricultural cropping patterns, crop insurance, stream analysis on the incoming data for real-time online analytics, entertainment etc.
  • Business analyst
  • Marketing analyst