BITS Pilani | M.Tech. Data Science and Engineering for Working Executives
Fee
INR 78,600 per semester
Experience
2 - 30 Years
Duration
2 Years
Delivery Method
Blended- Online
Programme Introduction
This postgraduate programme takes working professionals through a structured curriculum focusing on both foundational aspects and contemporary toolchains to prepare them for designing and deploying scalable AI-ready data applications and infrastructure for the AI-first world.
While the M.Tech AIML programme focuses more on model lifecycle and AI applications, the M.Tech Data Science and Engineering programme focuses exclusively on data aspects, covering acquisition, organisation, preprocessing, and analytics to create AI-ready data enabled by scalable data infrastructure, software engineering principles, and Agentic AI.
Programme Objectives
- Mathematical and Statistical modeling for problem-solving.
- Data structures, algorithms, software engineering for data, data application architecture
- Computer organization, distributed and cloud computing, and data infrastructure architecture
- Full stack data management, data Pipelines, responsible data engineering
- Advanced skills in big data mining, generative modeling and data synthesis, streaming data
- Agentic AI for productivity enhancement, strategic communication with data
Desired Candidate Profile
- Employed professionals holding MCA / M.Sc. or equivalent with at least 60% aggregate marks with university level mathematics / statistics as mandatory subjects also eligible to apply
- Applicants should possess basic programming knowledge and adequate background in Mathematics
Course Modules
Programme Curriculum
First Semester
- Introduction to Data Science
- Essential Mathematics for Data Science
- Pre-processing for AI-ready Data
- Modern Database Systems
Second Semester
- Elective
- Elective
- Elective
- Elective
Third Semester
- Elective
- Elective
- Elective
- Elective
Fourth Semester
- Dissertation
Core Courses
- Introduction to Data Science
- Essential Mathematics for Data Science
- Pre-processing for AI-ready Data
- Modern Database Systems
Pool of Electives - Specialization in Systems and Infrastructure
- Computer Organization and Software Systems
- Distributed Computing
- Big Data Systems
- Cloud Computing
- Data Infrastructure Architecture
- Data Warehousing
- Data Storage Technologies and Networks
- DataOps
- Distributed Data Systems
- Responsible Data Engineering
- Stream Processing and Analytics
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