
Computer Science & Engineering (Artificial Intelligence & Data Science)
Turning data into responsible, real-world intelligence.
CSE-AIDS
Programme
60 seats
Sanctioned Intake
AI + Data Science
Focus
Project-driven
Practice
About the Department
The Artificial Intelligence & Data Science specialisation combines core computer science with statistics, machine learning and large-scale data engineering.
Students learn the complete data lifecycle — acquisition, cleaning, modelling, visualisation and deployment — through applied, project-driven coursework.
The programme prepares graduates for data scientist, AI engineer and analytics roles across industry and research.
Vision
To be a centre of excellence in artificial intelligence and data science that turns data into responsible, real-world impact.
Mission
- 01
Build strong foundations in AI, statistics and data engineering.
- 02
Emphasise the full data lifecycle — from acquisition to insight and deployment.
- 03
Promote ethical, explainable and human-centred AI.
- 04
Strengthen placement readiness through applied, project-driven learning.
Programmes Offered
- CSE (Artificial Intelligence & Data Science)60 seats
Total sanctioned intake: 60
PEO's, PSO's & PO's
Program Educational Objectives (PEOs)
PEO1
Achieve professional competency across the AI and data-science lifecycle from data to decision. (Professional Competency)
PEO2
Build successful careers as data scientists, AI engineers, analysts, entrepreneurs or researchers. (Successful Career Goals)
PEO3
Engage in lifelong learning and contribute to society through innovative, data-driven solutions. (Continuing Education and Contribution to Society)
Program Specific Outcomes (PSOs)
PSO1
Apply AI, statistics and machine-learning techniques to extract insight from complex data.
PSO2
Engineer and deploy scalable, ethical data-driven solutions using contemporary toolchains.
Program Outcomes (POs)
Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one's own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Regulation and Syllabus
The department follows the JNTUA outcome-based curriculum, periodically revised by the Curriculum Development Cell in line with AICTE and NBA guidelines.
Regulation documents and the detailed semester-wise syllabus are available from the department office and the college Exam Portal.
Faculty Profile
Dr. V. Janardhan Babu
Professor & Head (CSE)
Ph.D
Dr. D. Nagaraju
Professor
Ph.D
Dr. B. Ramaganesh
Associate Professor
Ph.D
Dr. N. Srinivas Rao
Associate Professor
Ph.D
Dr. G. B. Hima Bindu
Associate Professor
Ph.D
M. Malli
Assistant Professor
M.Tech
P. Gopi Chand
Assistant Professor
M.Tech
B. Usha Bindu
Assistant Professor
M.Tech
M. Revathi
Assistant Professor
M.Tech
Course Material
Subject-wise lecture notes, lesson plans, question banks, lab manuals and model papers are curated by the faculty and shared through the department's learning portal and class repositories.
Laboratory Facilities
AI & Data Science Lab
60 systemsWorkstations with Python data-science stack, scikit-learn, pandas and TensorFlow.
Big Data Analytics Lab
30 systemsHadoop/Spark cluster tooling for large-scale data analytics.
Data Visualization & BI Lab
30 systemsVisualization and business-intelligence tooling for analytics dashboards.
Research Facilities
The specialisation encourages applied research across the AI and data-science lifecycle, supported by the R&D Cell.
Research Thrust Areas
Mentor Details
Faculty mentors guide each student under the institutional Mentor Program, emphasising data-portfolio building and applied projects.
Departmental Activities
- Guest lectures on applied AI and data science
- Hands-on analytics and visualization workshops
- Hackathons and data-science competitions
- Industry-mentored capstone projects
- Open-source contribution drives
Professional Bodies
Department Library
A dedicated departmental library supplements the central library with titles, reference volumes, previous question papers, project reports and subscriptions to technical journals for ready student and faculty access.
Career outcomes are supported by the campus-wide Training & Placement Cell — explore placements →
