Programme Overview

The Bachelor of Engineering (B.E.) in Computer Science and Engineering with a specialization in Data Science is designed to prepare students for the growing demands of a data-driven world. The curriculum integrates foundational computer science subjects—such as programming, algorithms, operating systems, and software engineering—with specialized modules in data science including data mining, machine learning, big data technologies, and data visualization. Students gain practical experience through hands-on lab sessions, mini-projects, and a capstone project, working with industry-relevant tools like Python, R, SQL, Hadoop, and Apache Spark. Emphasis is placed on real-world applications, ethical use of data, and the broader societal impact of emerging technologies in data science. Graduates are equipped for careers as Data Scientists, Data Analysts, Big Data Engineers, Business Intelligence Developers, and more. The program also lays a strong foundation for those interested in pursuing advanced studies or research in data science, artificial intelligence, and related fields.

  • 4 Years

    Duration of programme

  • UG

    Level of Study

Key Highlights

High-Performance Computing Lab
IoT & Advanced Research Lab
Student Research & Hackathons
MoUs with Industry & Academia
Central Library & E-Resources Access

How will you benefit

Capstone Projects: Apply your classroom learning to real-world problems through capstone projects, enhancing your critical thinking, innovation, and problem-solving abilities.
Advanced Laboratories: Gain hands-on experience with state-of-the-art computing labs and data science tools, including platforms used in industry and research.
High Employability: Enter a high-growth job market with strong demand for data science professionals, offering abundant career opportunities and attractive salary packages.
Industry Exposure: Stay connected with industry trends through guest lectures, expert sessions, and workshops by professionals from the data science ecosystem.

What will you study

Programming & Data Tools: Learn key programming languages like Python, R, and SQL, along with industry-relevant tools for data analysis, visualization, and reporting.

Mathematics & Statistics: Build a solid foundation in linear algebra, calculus, probability, and statistical methods, essential for data-driven decision making.

Machine Learning & AI: Explore and apply machine learning algorithms and artificial intelligence techniques to develop intelligent, predictive systems.

Big Data & Cloud Technologies: Gain practical skills in big data frameworks like Hadoop and Spark, along with an introduction to cloud platforms used for scalable data processing.

  • PO-1 - Strong Technical Foundation: Demonstrate solid understanding of mathematics, statistics, and core engineering principles to solve complex problems in data science and computing.
  • PO-2 - Analytical and Logical Thinking: Analyze real-world challenges, identify data-driven opportunities, and develop effective solutions using modern analytical techniques.
  • PO-3 - Design and Implementation Skills: Design and build software systems, data models, and machine learning solutions that are efficient, scalable, and user-oriented.
  • PO-4 - Proficiency in Tools & Technologies: Effectively use industry-standard tools and technologies such as Python, R, SQL, Hadoop, Spark, Tableau, and cloud platforms for data processing and visualization.
  • PO-5 - Ethical Data Handling: Understand data privacy laws, responsible AI practices, and ethical implications of data usage in decision-making systems.
  • PO-6 - Project and Process Management: Apply project management principles, software development methodologies, and teamwork practices to successfully deliver technology solutions.
  • PO-7 - Effective Communication: Present ideas, analysis, and technical content clearly through written reports, visualizations, and verbal presentations across multidisciplinary environments.
  • PO-8 - Team Collaboration and Leadership: Work effectively as a team member or leader in diverse and collaborative environments, contributing to shared goals and innovation.
  • PO-9 - Lifelong Learning and Adaptability: Embrace continuous learning to keep pace with evolving technologies, tools, and industry demands in data science.
  • PO-10 - Research and Innovation Aptitude: Apply research methodologies, critical thinking, and creative approaches to explore, experiment, and innovate in the field of data science.
  • PO-11 - Career Readiness: Be equipped for roles in analytics, software, and research through experiential learning, internships, certifications, and industry interaction.
  • PO-12 - Entrepreneurial Thinking: Develop an entrepreneurial mindset and leverage data insights to create innovative solutions, products, or startups.

  • PSO-1 - Data Science Proficiency: Apply core concepts of data science, including data preprocessing, statistical analysis, machine learning, and data visualization, to extract insights from complex datasets and support informed decision-making.
  • PSO-2 - Big Data & Cloud Technology Integration: Demonstrate practical skills in handling large-scale data using tools like Hadoop, Spark, and cloud platforms; develop scalable solutions for real-time data processing and analytics.
  • PSO-3 - Intelligent System Development: Design and implement AI-driven applications using techniques from machine learning, deep learning, NLP, and reinforcement learning to solve domain-specific problems.
  • PSO-4 - Industry and Research Readiness: Leverage tools such as Tableau, Power BI, and programming environments (e.g., Python, R, SQL) to build data-driven solutions; exhibit readiness for professional roles, certifications, higher studies, or entrepreneurship in the data science domain.

  • PEO-1 - Successful Professional Careers: Graduates will establish themselves as competent professionals in the fields of data science, machine learning, software development, analytics, and related domains, contributing effectively to the industry and society.
  • PEO-2 - Higher Education and Research: Graduates will pursue advanced studies, research, or professional certifications in data science, artificial intelligence, cloud computing, or interdisciplinary areas to remain competitive and contribute to knowledge creation.
  • PEO-3 - Innovation and Entrepreneurship: Graduates will apply their technical and analytical skills to develop innovative solutions or ventures, promoting entrepreneurship and contributing to the startup ecosystem.
  • PEO-4 - Ethical, Responsible & Lifelong Learners: Graduates will demonstrate professionalism, ethical behavior, effective communication, teamwork, and a commitment to continuous learning in their personal and professional lives.

Curriculum

  • Engineering Chemistry
  • Mathematics-I
  • Engineering Graphics
  • Basic Electrical Engineering
  • Basic Computer Engineering
  • Manufacturing Practices

  • Engineering Physics
  • Mathematics-II
  • Basic Mechanical Engineering
  • Basic Civil & Engg. Mechanics
  • Communication Skills
  • Health, Hygiene & Yoga
  • Rural Outreach

  • Mathematics-III
  • Discrete Structures
  • Introduction to Data Science
  • Object Oriented programming & Methodology
  • Data structures
  • Computer Programming & Tools-I(HTML)
  • Skill Elective-I

  • Mathematics and Statistical foundation
  • Advance Computer Architecture
  • Analysis and Design of Algorithms
  • Python for Data Science
  • Operating System
  • Computer Programming and Tools
  • Skill Elective-II

  • Introduction to Toolkits for Data Science
  • Data Compression
  • Data Base Management System
  • Software Engineering and Agile Methodologies
  • Web Programming Fundamentals
  • Power BI
  • Skill Elective-III

  • Natural Language Processing
  • Deep Learning Fundamentals
  • High-End Programming (Elective)
  • Computer Networking
  • Big Data Analytics
  • Tableau Software
  • Skill Elective-IV

  • Robotics
  • Fundamentals of Cybersecurity
  • Cloud and Distributed Computing
  • Data Engineering
  • Professional Elective-I
  • Minor Project
  • Industrial Training/Entrepreneurship/IPR

  • Soft Computing
  • Reinforcement Learning
  • Professional Elective-II
  • Open Elective-II
  • Major Project
  • Seminar

CAREERS AND EMPLOYABILITY

Data Analyst
Big Data Engineer
Machine Learning Engineer
Business Intelligence Developer

ELIGIBILITY CRITERIA

12th in PCM with 45 %(40% in case of SC/ST)

3 Year diploma in any stream

 

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Rabindranath Tagore University,

Rabindranath Tagore University, Mendua, Post: Bhojpur, Near - Bangrasiya chouraha, Bhopal-Chiklod Road, District: Raisen,(M.P.)

07552700401 | 07552700447 |

info@rntu.ac.in

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