Department of

Computer Science & Engineering (Data Science)

Transforming data into intelligent solutions

Future-ready curriculum, outcome-based learning, and partnerships with leading technology companies empower NSRIET students to innovate confidently and lead the next wave of digital transformation.

Explore CurriculumAcademic Calendar
  • Data Driven
  • Strategic Industry Partner with global tech leaders
  • Experiential learning through hackathons & sponsored labs
  • Best Practices & Accomplishments

Program Duration

4 years (8 semesters)

Intake

60 students

Approvals

AICTE And JNTU GV

Program Overview

The Computer Science & Engineering (Data Science) program at NSRIET is designed to develop skilled professionals who can solve real-world data problems. Students gain expertise in data analytics, machine learning, and big data technologies while working on industry-relevant projects and research initiatives.

Program Duration
4 years (8 semesters)
Intake
60 students
Approvals
AICTE And JNTU GV

Vision & Mission

Vision

To establish a distinguished learning hub in Data Science producing skilled professionals capable of transforming data into intelligent solutions.

Mission

  • To provide comprehensive education in data analytics, statistical modelling, and computational techniques.
  • To foster innovation through research, real-world datasets, and industry-oriented projects.
  • To equip students with modern tools, platforms, and practical expertise in emerging data technologies.

Program Educational Objectives (PEOs)

  • PEO1: Technical Excellence & Problem-Solving — Apply strong foundational and advanced knowledge in computer science to solve complex real-world problems using critical thinking, adapting to rapidly changing technologies to meet industry and client needs.
  • PEO2: Professional Success & Leadership — Build successful careers as entrepreneurs, team members, or leaders in IT and related industries, demonstrating professional competence and collaborative skills.
  • PEO3: Lifelong Learning & Career Growth — Engage in continuous self-learning and pursue higher education or certifications to stay current with industry trends and achieve sustained professional excellence.

Program Outcomes (POs) & PSOs

Program Outcomes (POs)

PO1: Apply statistical, mathematical, computational, and engineering knowledge to complex data problems.
PO2: Identify, analyze, and interpret data sets to draw meaningful insights and conclusions.
PO3: Design data-driven systems, visualizations, and scalable models for societal and industrial needs.
PO4: Conduct data investigations through analysis, experimentation, and performance evaluation.
PO5: Utilize advanced data tools, analytics platforms, programming techniques, and models.
PO6: Evaluate the societal, ethical, environmental, and legal implications of data-driven decisions.
PO7: Uphold ethics, privacy, security, and data governance standards.
PO8: Collaborate effectively in multidisciplinary teams and provide strong leadership.
PO9: Communicate data insights effectively using visualizations, reports, and presentations.
PO10: Use project management and business analytics principles to manage DS projects.
PO11: Pursue lifelong learning to adapt to evolving technologies, tools, and methodologies.

Program Specific Outcomes (PSOs)

  • PSO1: Design intelligent software using AI/ML and data engineering practices.
  • PSO2: Build secure, scalable full-stack solutions for cloud and mobile platforms.

Curriculum Highlights & Syllabi

Core Subjects

  • ✓Data Structures and Algorithms
  • ✓Database Management Systems
  • ✓Operating Systems
  • ✓Computer Networks
  • ✓Web Development
  • ✓Software Engineering
  • ✓Mobile Application Development
  • ✓Cloud Computing

Learning Outcomes

  • Proficiency in multiple programming languages and frameworks
  • Understanding of software development lifecycle and best practices
  • Ability to design scalable and secure systems
  • Problem-solving skills for complex computational challenges

Outcome-based curriculum covering core computing, emerging tech electives, and interdisciplinary pathways. Download the detailed syllabus and course outcomes below.

📄 UG Syllabus📄 Elective Booklet

Academic Calendar

The department follows an activity-rich calendar with assessments, workshops, internships, and hackathons mapped semester-wise.

Select Academic Calendar
📄 3rd Year 1st Semester📄 2nd Year 1st Semester

Industry Sponsored Laboratory

iGenuine

Focus: Industry Sponsored Lab

Teckybot

Focus: Industry Sponsored Lab

Memoranda of Understanding (MoUs)

NASSCOMCouncil for Skills and CompetenciesiGenuineTeckybotTeckTeam Solutions

Innovative Teaching Methodologies

Industrial oriented program
LMS
Problem-based learning using hackathons and ideathons

Professional Development Activities

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Faculty Development Program

Annual FDPs on AI/ML, cloud and cybersecurity run with industry mentors, ensuring faculty stay ahead of emerging tech.

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Industry Design Sprints(DiDac,sony,coffeday)

Four-week immersion where student-faculty squads ship prototypes for partner companies using agile methods.

Innovation Showcases

Bi-semester showcases featuring student products, patent pitches, and investor feedback rounds.

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Peer Learning Studios

Community-of-practice circles where learners exchange toolkits, code recipes, and research insights.