About the Program
The Data-Driven Product Engineering program is designed to equip learners with comprehensive knowledge of modern software development methodologies, front-end and back-end technologies, data science, machine learning, and emerging artificial intelligence techniques. This program prepares learners to become proficient in building scalable, robust, and intelligent software products using cutting-edge frameworks and tools.
Courses
    Credits Semester
  • Front End UI/ UX Development 3 III
  • Advanced JavaScript Frontend Framework with Angular 3 IV
  • Advanced JavaScript Backend Frameworks (Node.JS, Express JS) 3 V
  • ML Algorithms Intuition and Predictive Modeling 3 V
  • Big Data Ecosystem with Hadoop and Spark 3 VI
  • NLP Techniques and Generative AI 3 VII
  • Capstone Project 3 VIII
    Credits Semester
  • IT World Essentials: Your Digital Entrypoint 3 I
  • Critical Thinking, Design Thinking, Leadership and Teamwork 3 II
  • Capstone Project 3 VIII
    Credits Semester
  • Critical Thinking, Design Thinking, Leadership and Teamwork 3 II
  • Career Readiness in Digital Era 3 VI
Mode of Delivery
  • Self-paced learning – 12 hours
  • Expert sessions + Project work – 33 hours
  • Face-to-face instructor led sessions / VILT sessions (including project work) – 45 hours
  • Self-paced learning + Expert session – 45 hours
Job Roles
  • Full-Stack Developer
  • ML Engineer
  • DevOps Engineer
  • Data Engineer
  • NLP Engineer
  • Agile Product Manager
Software Tools
  • Angular
  • Node
  • Express
  • TensorFlow
  • NLTK
  • Hugging Face
  • AWS
  • Git/GitHub
  • Docker
  • Kubernetes
Skills
  • Comprehensive Understanding of SDLC with Agile Methodology.
  • Designing intuitive, user-centric interfaces with a strong focus on responsive and accessible web development.
  • Building dynamic, single-page applications (SPAs) with Angular, and leveraging TypeScript
  • Developing scalable server-side applications and RESTful APIs with Node.js and Express.js.
  • Applying supervised and unsupervised machine learning algorithms for predictive analytics and data-driven insights.
  • Practical Knowledge of NLP Techniques and Generative AI
  • Streamlining CI/CD pipelines, infrastructure automation, and application deployment using modern DevOps tools.