Skip to content
Admissions 2026–27 are now open across all programmesAutonomous · NAAC & NBA Accredited · Affiliated to JNTUA80%+ placements · 100+ recruiters every seasonCall the Admissions Office — +91 93905 05457Admissions 2026–27 are now open across all programmesAutonomous · NAAC & NBA Accredited · Affiliated to JNTUA80%+ placements · 100+ recruiters every seasonCall the Admissions Office — +91 93905 05457
A full SVPP computer lab session with students on laptops
Computing · Est. 2022

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

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

  1. 01

    Build strong foundations in AI, statistics and data engineering.

  2. 02

    Emphasise the full data lifecycle — from acquisition to insight and deployment.

  3. 03

    Promote ethical, explainable and human-centred AI.

  4. 04

    Strengthen placement readiness through applied, project-driven learning.

Programmes Offered

  • CSE (Artificial Intelligence & Data Science)60 seats

Total sanctioned intake: 60

Outcomes Framework

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)

PO1

Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

PO2

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.

PO3

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.

PO4

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.

PO5

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.

PO6

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.

PO7

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.

PO8

Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

PO9

Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO10

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.

PO11

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.

PO12

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.

Curriculum

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.

Exam Portal & Syllabus →
People

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

Resources

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.

Infrastructure

Laboratory Facilities

AI & Data Science Lab

60 systems

Workstations with Python data-science stack, scikit-learn, pandas and TensorFlow.

Big Data Analytics Lab

30 systems

Hadoop/Spark cluster tooling for large-scale data analytics.

Data Visualization & BI Lab

30 systems

Visualization and business-intelligence tooling for analytics dashboards.

R & D

Research Facilities

The specialisation encourages applied research across the AI and data-science lifecycle, supported by the R&D Cell.

Research Thrust Areas

Artificial IntelligenceData ScienceBig Data AnalyticsPredictive ModellingExplainable AI
Student Support

Mentor Details

Faculty mentors guide each student under the institutional Mentor Program, emphasising data-portfolio building and applied projects.

Engagement

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
Affiliations

Professional Bodies

CSI Student ChapterIEEEISTE Student Chapter
Knowledge

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.

Where Graduates Go
Data ScientistAI EngineerData AnalystAnalytics EngineerResearch Associate

Join CSE-AIDS

Admissions for 2026–27 are open.

Apply Now →

Career outcomes are supported by the campus-wide Training & Placement Cell — explore placements →

Admissions 2026–27 open

Enquire in under a minute

Apply →