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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
Computer Science laboratory
Computing · Est. 2022

Computer Science & Engineering (Data Science)

Engineering insight from data at scale.

CSE-DS

Programme

60 seats

Sanctioned Intake

Data Science

Focus

Analytics-driven

Practice

About the Department

About the Department

The Data Science specialisation focuses on extracting actionable insight from data — spanning statistics, data engineering, machine learning and visualisation.

Students work with contemporary analytics toolchains and participate in competitions, applied projects and industry-mentored work.

Graduates are prepared for data scientist, analyst and data-engineering roles.

Vision

To be recognised for excellence in data science education that translates directly into industry and societal impact.

Mission

  1. 01

    Build deep competence in data analysis, statistics and machine learning.

  2. 02

    Emphasise data engineering, visualisation and reproducible analytics.

  3. 03

    Encourage applied projects, competitions and open-source contribution.

  4. 04

    Strengthen placement readiness through portfolio-driven learning.

Programmes Offered

  • CSE (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 data-science lifecycle from data to insight. (Professional Competency)

PEO2

Build successful careers as data scientists, analysts, data engineers, 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 statistics, data engineering and machine learning to extract actionable insight from data.

PSO2

Build and communicate reproducible analytics using contemporary toolchains and visualisation.

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

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

Data Science Lab

60 systems

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

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, analytics-driven research, supported by the R&D Cell.

Research Thrust Areas

Data ScienceBig Data AnalyticsStatistical ModellingPredictive AnalyticsData Visualisation
Student Support

Mentor Details

Faculty mentors guide each student under the institutional Mentor Program, emphasising analytics portfolios and applied projects.

Engagement

Departmental Activities

  • Guest lectures on applied data science
  • Hands-on analytics and visualization workshops
  • Hackathons and data 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 ScientistData AnalystData EngineerBusiness Intelligence AnalystResearch Associate

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