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MSc Data Science
& Product Development

2 Year 100% Work- Immersive Learning Program blending Academic, Skill with professional Financial Earnings up to 30,000 per month

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About the Course

The M.Sc. in Data Science and Product Development, offered by the School of Digital Sciences, is a forward-thinking 2-year Work-Immersive Learning Programme designed to bridge the gap between academic learning and real-world application.

This programme goes beyond traditional classroom education by immersing students in a dynamic, project-driven environment where learning is closely aligned with practical implementation. It enables students to develop both strong theoretical foundations and hands-on expertise.

Work-Immersive Learning

The programme follows a Work-Immersive Learning with Professional Earnings model, where students receive monthly financial support throughout the duration of the course, subject to eligibility criteria and performance.

This model not only provides financial assistance but also fosters a strong sense of responsibility, professionalism, and workplace readiness.

Programme Integration Ecosystem

Under this model, students are continuously embedded in live projects coordinated with specialized Centres of Excellence:

CDIPD AIIRL CGA CDA

Academic credits, assessments, and learning outcomes are directly mapped to project tasks, assignments, and deliverables.

Career Pathway

This programme is ideal for students aspiring to build future-ready careers in Data Science, Artificial Intelligence, and Digital Product Innovation—while gaining meaningful industry experience and financial independence during their studies.

Why Choose This Programme?

Program Highlights

The programme is designed to:

  • Develop AI-driven digital product and solution engineers
  • Integrate full-stack and cloud-native development capabilities
  • Embed DevOps and MLOps practices within production environments
  • Promote agile product development methodologies and processes
  • Ensure adherence to governance, industry standards, and security compliance
  • Offer a Professional Learning Track that seamlessly integrates education with employment
  • Provide opportunities to collaborate with industry experts
  • Work within Centres of Excellence aligned with CMMI Level 3 standards
  • Enable pathways to industry-recognized professional certifications

Unique Strength of the Program

  • Education Meets Employment
  • Real-world exposure to the project ecosystem
  • Skill Development oriented pathways
  • Immersive work learning with CMMI Level 3 standards
  • Guided and mentored by Industry Experts
  • High industry relevance with real projects
  • Strong alignment with NEP 2020 through (WIL)
  • Integration with established centres (CDIPD, AIIRL, etc.)
  • Exposure to global standards and process of lifecycles
  • Performance-based assessment for earnings
  • Clear pathways to employability and innovation
  • Digitally Dashboards for Daily Work guidance

Course Outcomes

Learning Outcomes

By the end of the programme, graduates will have:

Program Outcomes (POs)

Upon completion, graduates will be able to:

  • PO 1
    Apply AI/ML techniques to design intelligent systems
  • PO 2
    Architect scalable cloud-native full stack systems
  • PO 3
    Design distributed backend and real-time data systems
  • PO 4
    Implement DevOps, CI/CD and MLOps workflows
  • PO 5
    Translate business requirements into validated products
  • PO 6
    Apply ethical AI, cybersecurity and governance frameworks
  • PO 7
    Conduct experimentation, benchmarking and optimization
  • PO 8
    Demonstrate industry readiness and professional competence

Program Specific Outcomes (PSOs)

  • PSO 1
    Develop AI-powered full stack digital products end-to-end
  • PSO 2
    Engineer microservices-based cloud platforms
  • PSO 3
    Deploy AI models using DevOps and MLOps practices
  • PSO 4
    Deliver compliant, secure and quality-assured systems

Mandatory Core Subjects and Skills

  • CS101: Advanced AI & Machine Learning
  • CS102: Full Stack Architecture & Cloud-Native Dev
  • CS103: Modern Backend Systems & Data Engineering
  • CS104: API Design & Microservices Orchestration
  • CS105: DevOps & Automated Pipelines

Semester Wise Learning Structure

Semester I (20 Credits)

Foundation & Ideation

Core : CS101 (Adv AI & ML)(Full Stack Cloud Dev) + 3 Elective Skill/subject areas

Project 1 : Market Research, tech-stack selection and UI/UX prototyping
Outcome: Validated project idea, architecture blueprint and high-fidelity UI prototype.
Semester II (20 Credits)

Backend & AI / Full stack Engineering

Core: CS103 (Modern Backend Systems & Data Engineering), CS105 (DevOps & Automated Pipelines) + 3 Electives

Project II : Developing the AI model and the backend CRUD operations.
Outcome: A functional backend with a trained AI model or Fullstack based solution accessible via local environment.
Semester III (20 Credits)

Integration & Cloud Orchestration

Core: CS104 (API Design & Microservices Orchestration) + 4 Electives

Project III : Integrating the middle layer, connecting third-party APIs (payment gateways, external data), and deploying to the cloud.
Outcome: A fully integrated, "live" digital product.
Semester - IV (20 Credits)

Solution Evaluation, Report and Viva

  • Duration – 6 months
  • Final project...............
    Final Project Development within DUK CDIPD Lab: (12 credits)
  • Report and Viva (8 credits) - Final Solution/Product Evaluation.
Continuous Assessment of work by Industry Experts:
  • Monitoring of Sprint work plan
  • Activity based monitoring through Project Management Tools
  • Mentored and guided by Industry Experienced project team
  • Monthly progress presentations and mentor evaluation
Mandatory Track/Pool: Students must be placed in a role involving AI or Full Stack development.

Elective Skill Pool

Choose 12 skills out of 17 required for Product/Solution Development.

Students can tailor their skill towards "AI Heavy or Full Stack Development Heavy Path"

Category A

Artificial Intelligence Focus

Category B

Full Stack & Engineering

Category C

Data & Specialized Systems

Course Fee

Academic Fees

₹3,10,000
The total fee for the course (2 year)
  • First Semester
    (Tuition 85k + Caution 5k)     ₹90,000
  • Second Semester
    (Tuition Fee)     ₹75,000
  • Third Semester
    (Tuition Fee)     ₹75,000
  • Fourth Semester
    (Tuition Fee)     ₹75,000

Professional Financial Earnings

Earn While You Learn

Up to ₹30k
Monthly Professional Support
Subject to satisfactory work & appraisal
  • Semester 1 ₹10,000 / mo
  • Semester 2 ₹10,000 / mo
  • Semester 3 ₹20,000 / mo
  • Semester 4 ₹30,000 / mo

Selection Process

Eligibility Criteria

Candidates must possess either a 3-year or a 4-year Bachelor’s degree in Science/Engineering/Mathematics, with Mathematics/Statistics as one of the subjects, and a minimum aggregate of 60% marks (or equivalent).

Strictly Limited to 20 Seats
The candidates will be selected through rigorous selection process

Level 1 : Shortlisting of candidates through CUET (PG) or DUAT

Students can apply through CUET (PG) or DUAT test.
The Applicable CUET(PG) Test Codes are : SCQP09, SCQP27, SCQP19, SCQP24, MTQP04

Level 2 : Skill Assessment Test (Machine based)

The shortlisted candidates from CUET(PG) and UDAT will be invited for a skill Assessment Test.

Level 3 : Interview

Those who qualify the skill Assessment test will be invited for the interview and final selection list will be published. The reservation norms will be followed as applicable.

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