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COURSE OVERVIEW

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The M. Tech in Data Science is a cutting-edge postgraduate program designed to equip students with advanced knowledge and skills in the field of data analysis, machine learning, and computational intelligence. Blending theoretical foundations with hands-on training, the program emphasizes real-world applications of data science in diverse industries. With a strong focus on statistics, programming, big data technologies, and domain-specific analytics, students will emerge as proficient data professionals ready to tackle complex problems in a data-driven world.


Syllabus >
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Programme Objectives

  • To develop a strong foundation in data science concepts including statistics, machine learning, and data mining.
  • To provide practical exposure to tools and technologies such as Python, R, SQL, Hadoop, Spark, and TensorFlow.
  • To encourage research and innovation in data-driven problem solving and intelligent systems.
  • To prepare students for interdisciplinary roles in academia, industry, and entrepreneurship involving data analytics and artificial intelligence.
  • To cultivate ethical and socially responsible data science professionals.

PROGRAMME HIGHLIGHTS

Industry-Relevant Curriculum designed in collaboration with leading experts and academic institutions.
Hands-On Learning through labs, case studies, and real-world projects.
Internship Opportunities with reputed organizations for practical exposure.
Workshops & Seminars by data scientists and industry leaders.
Research Opportunities in AI, deep learning, natural language processing, and more.
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Programme Curriculum

  • 3 Week Induction Program

    Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    MTDT6501 Mathematical Foundations of Data Science 2 1 0 3 3
    SOTT6301 Advanced Data Structures and Algorithms 2 1 0 3 3
    Program Elective-I
    MTDT6601a Programming for Data Science 3 0 0 3 3
    MTDT6601b Data Engineering and Databases
    MTDT6601c Machine Learning and AI
    Program Elective-II
    MTDT6602a AI and Decision-Making Models 3 0 0 3 3
    MTDT6602b Data Preparation and Analysis
    MTDT6602c Data Visualization and Communication
    SOTT6374 Research Methodology and IPC 2 0 0 2 2
    SOTT6303 Human Values & Professional Ethics

    (Audit Course – I)

    		 2 0 0 0 2
    MTDS6501/MTDC6501 Technical Seminar/Case Study -1 1 0 0 1 1
    SOTL6301 Advanced Data Structures and Algorithms Lab 0 0 4 2 4
    MTDL6501 Programming for Data Science Lab 0 0 4 2 4
    — Mentoring 0 0 0 0 1
    — Library 0 0 0 0 2
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Co-curricular activity 0 0 0 0 2
    — Self-Learning 0 0 0 0 2
    TOTAL 15 2 8 19 36

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    SOTT6304 Advanced Machine Learning 2 1 0 3 3
    MTDT6504 Big Data Analytics 2 1 0 3 3
    Program Elective-III
    MTDT6603a Ethical AI and Decision Making 3 0 0 3 3
    MTDT6603b Time Series Analysis
    MTDT6603c Text Analytics
    Program Elective-IV
    MTDT6604a Predictive and Perspective Analytics 3 0 0 3 3
    MTDT6604b Edge AI and IoT Analytics
    MTDT6604c Data Privacy and Security
    SOTT6305 English for Research Paper Writing

    (Audit Course-2)

    		 2 0 0 0 2
    MTDP6501 Mini project 0 0 8 4 8
    SOTL6302 Advanced Machine Learning Lab 0 0 4 2 4
    MTDL6502 Big Data Analytics Lab 0 0 4 2 4
    — Mentoring 0 0 0 0 1
    — Co-curricular activity 0 0 0 0 1
    — Self-Learning 0 0 0 0 1
    — Extra-curricular activities 0 0 0 0 2
    — Library 0 0 0 0 1
    TOTAL 12 2 16s 18 36

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    MTDM7501 MOOC-1 3 0 0 3 3
    MTDM7502 MOOC -2 3 0 0 3 3
    MTDP7501 Dissertation I / Industrial Project 0 0 20 10 20
    — Mentoring 0 0 0 0 1
    — Co-curricular activity 0 0 0 0 2
    — Self-Learning 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Soft Skills Training 0 0 0 0 1
    — Certification Course 0 0 0 0 1
    TOTAL 6 0 20 16 36
  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    MTDP7502 Dissertation Phase II 0 0 32 16 32
    TOTAL 0 0 32 16 32

PROGRAMME FEE AND SCHOLARSHIPS

PROGRAMME FEES SCHOLARSHIPS ADMISSION PROCESS
One Time Fee Admission Fee ₹10,000
Year I Tuition Fee + Annual Recurring Fee ₹ 1,50,000 + ₹ 13,000
Year II Tuition Fee + Annual Recurring Fee ₹ 1,50,000 + ₹ 13,000
Total Programme Fee (2 Years) ₹ 3,36,000

Scholarship is available for eligible students

Admissions are conducted in two modes:
1. Convener Quota: As per the Andhra Pradesh Private Universities Act, through the Government of Andhra Pradesh.
2. University Quota: Based on the TAU entrance exam or recognized national-level exams.

Eligibility

Pass with 50% aggregate marks (45% for reserved categories) in B.Tech. (Computer Science and Engineering or Information Technology or Electronics and Communication Engineering or Electrical and Electronics Engineering) or MCA or M.Sc. (Information Technology or Computer Science) or equivalent.

EMPLOYABILITY AREAS

Graduates of the M. Tech in Data Science program can explore careers in:

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  • Data Science Expert & Data Analyst
  • Artificial Intelligence & Machine Learning
  • Business Analyst
  • Big Data Engineering
  • Data Engineering
  • Research & Development
  • Healthcare Analytics
  • Financial Services & Fintech
  • Retail & E-commerce Analytics
  • Government & Smart City Projects