Programmes
B.Tech. - Computer Science and Engineering (Data Science) (Lateral Entry)
-
Undergraduate Level
-
3 Years Duration
Download Brochure
Overview
The B.Tech in Computer Science & Engineering (Data Science) at NSHM Institute of Engineering and Technology is a career-oriented undergraduate programme designed to create data-driven engineers and analytical problem solvers for today’s digital economy. The programme blends strong foundations in computer science with advanced training in Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence, and Business Intelligence.
With a continuously updated, industry-aligned curriculum, students gain extensive hands-on experience through data science labs, real-world datasets, live projects, internships, hackathons, and industry certifications. The program emphasizes practical application of data to drive insights, predictions, and intelligent decision-making across domains.
Approved by AICTE and affiliated to MAKAUT, the course prepares graduates for high-demand roles in Data Analytics, Data Engineering, AI & ML, Business Analytics, and Software Development. Supported by expert faculty, advanced infrastructure, and strong industry collaborations, the programme ensures graduates are job-ready, future-focused, and globally competitive in the fast-growing data-driven technology landscape.
Eligibility
10+2 from any recognized board
Candidates appearing for Class XII final can also apply
Programme Educational Objectives
Graduates of this program will:
-
01
Develop a strong foundation in computer science and data science principles to design, develop, and implement efficient data-driven solutions for complex engineering and societal problems.
-
02
Demonstrate analytical thinking, research aptitude, and innovative approaches to extract meaningful insights from data and contribute to the advancement of technology and knowledge.
-
03
Pursue continuous learning and adapt to emerging tools, technologies, and methodologies in data science, artificial intelligence, and related fields.
-
04
Exhibit professionalism, ethical responsibility, and awareness of the societal and environmental impact of data-driven technologies in their professional practice.
-
05
Function effectively as individuals and as members or leaders in multidisciplinary teams, demonstrating communication skills and project management abilities to achieve organizational goals.
Programme Outcomes (PO)
-
01
Engineering Knowledge
Apply core knowledge from mathematics, basic sciences, computer engineering fundamentals, and specialized Artificial Intelligence principles to the analysis and solution of complex data-centric problems.
-
02
Problem Analysis
Identify, precisely formulate, and rigorously analyze complex computational problems, utilizing foundational principles of statistics, computer science, and engineering to reach substantiated conclusions from data.
-
03
Design & Development of Solutions
Design, conceptualize, and develop data-driven systems, processes, components, or programmes that meet specified performance requirements while prioritizing public safety, ethical considerations, and environmental sustainability.
-
04
Conduct Investigations of Complex Problems
Employ research-based methods, including effective data modeling, literature surveys, and experimental design, to analyze, synthesize, and interpret data for generating valid inferences and informed decisions.
-
05
Modern Tool Usage
Select, adapt, and skillfully apply appropriate Artificial Intelligence tools, state-of-the-art techniques, and modern computing platforms (such as Python, AI frameworks, and cloud services) for the rigorous development and evaluation of efficient solutions.
-
06
The Engineer and Society
Apply contextual knowledge to professionally assess the relevant societal, health, safety, legal, and cultural implications of utilizing intelligent and data-driven technologies in professional engineering practice.
-
07
Environment and Sustainability
Understand and evaluate the impact of Artificial Intelligence and computing solutions in both societal and environmental contexts, demonstrating a commitment to principles of resource efficiency and technological sustainability.
-
08
Ethics
Uphold ethical principles and adhere to professional ethics, responsibilities, data integrity, and strict confidentiality rules in all aspects of engineering practice related to data handling and intelligent system deployment.
-
09
Individual and Team Work
Function effectively, both autonomously as an individual and collaboratively as a dedicated member or leader, within diverse and highly multidisciplinary technical teams.
-
10
Communication
Communicate complex technical information effectively with both the engineering community and the public, including the generation of coherent technical reports, delivering formal presentations, and articulating clear, data-driven insights.
-
11
Project Management and Finance
Demonstrate a comprehensive knowledge and understanding of engineering and management principles to lead and manage projects, resources, and finance effectively within multidisciplinary environments.
-
12
Lifelong Learning
Recognize the necessity for, and possess the ability to engage in, continuous and independent lifelong learning, keeping pace with the rapid technological advancements in Artificial Intelligence and allied computational domains.
Programme Specific Outcomes (PSO)
-
01
Data Analytics and Insight Generation
Apply statistical, computational, and analytical techniques to collect, process, and interpret complex datasets, enabling data-driven decision-making and predictive modeling.
-
02
Intelligent System Design
Design and develop intelligent systems and applications using machine learning, artificial intelligence, and big data technologies to solve real-world challenges across various domains.
-
03
Data Engineering and Ethical Practice
Implement efficient data management, storage, and processing solutions using modern tools and frameworks, while adhering to ethical standards, data privacy, and security principles.
Key Facts
- Core Areas Covered: Algorithms, Computer Architecture, Data Structures, Formal Language & Automata Theory, Programming, Software Engineering, Database Systems, Operating Systems, Computer Networks
- Advanced Domains: Artificial Intelligence, Machine Learning, Cloud Computing, Cybersecurity & IoT
- Learning Approach: Theory combined with hands-on labs, projects and industry internships
- Infrastructure: Modern computing labs, high-performance systems and access to development tools
- Industry Exposure: Workshops, expert talks, hackathons and industrial training
- Career Prospects: Software Developer, Data Scientist, AI Engineer, Data Analytics, Cybersecurity Analyst, Cloud Engineer, System Architect, Researcher
- Placement Support: Pre-placement training, technical grooming and strong industry connect
- Higher Studies Pathways: M.Tech, MS, MBA, research programmes and global certifications
Curriculum
Semester-wise syllabus PDF
Semester - 3Scholarship
Eligible students may also avail government and statutory scholarships as applicable. The scholarship framework at NSHM aims to promote inclusive education, academic excellence, and equal access to quality learning.
Terms and eligibility criteria apply as per institutional guidelines.
Highlights/ Advantage
-
Interdisciplinary Curriculum
Combines core areas of Computer Science, Statistics, and Mathematics with emerging domains like Artificial Intelligence, Machine Learning, and Big Data Analytics.
-
Industry-Aligned Programme
Designed in collaboration with industry experts and data professionals, ensuring students learn technologies and tools relevant to real-world applications.
-
Hands-on Learning
Emphasis on practical exposure through labs, mini-projects, internships, and capstone projects using real datasets.
-
Cutting-edge Tools & Technologies
Training in Python, R, SQL, TensorFlow, Hadoop, Spark, Tableau, Power BI, and other data science frameworks.
-
Strong Analytical Foundation
Focus on developing quantitative reasoning, problem-solving, and critical thinking for data-driven decision-making.
-
Research & Innovation Opportunities
Encouragement to participate in research projects, hackathons, and data challenges, fostering innovation and creativity.
-
Focus on Ethical AI and Responsible Data Use
Integrates courses on data privacy, ethics, and societal impact to promote responsible and fair use of technology.
-
High demand for skilled professionals in a wide range of industries, both in India and globally.
-
Versatile career paths including software development, data science, cybersecurity, artificial intelligence, cloud engineering, research and product development.
-
Strong earning potential and opportunities for rapid career growth.
-
Flexibility to pursue higher studies, research or entrepreneurship in cutting-edge areas of technology.
-
Ability to build solutions that impact society, improve systems and drive innovation across sectors.
Teaching Pedagogies
-
01
Learner-Centric Pedagogy
The learning environment prioritizes student engagement through active learning strategies and personalized learning pathways, encouraging critical thinking and self-directed growth.
-
02
Outcome-Based Education (OBE)
Programmes are structured around clearly defined outcomes using CO–PO mapping and rubrics-based assessments, ensuring measurable learning achievements aligned with academic and industry standards.
-
03
Experiential & Applied Learning
Students gain hands-on exposure through industry expert lectures, lab- and studio-based work, and application-oriented assignments that bridge theory with practice.
-
04
Technology-Enabled Teaching
Teaching is enhanced with smart classrooms, Learning Management Systems (LMS), and advanced digital tools that promote interactive, blended, and flexible learning.
-
05
Research-Based Teaching
Students are encouraged to explore inquiry-driven learning through research projects, literature review work, and exposure to contemporary research methodologies.
-
06
Problem-Based Learning (PBL)
Real-world relevance is built through case studies and project-based learning, enabling students to solve practical problems collaboratively and creatively.
-
07
Collaborative Learning
Learning is strengthened through group tasks, teamwork, and peer assessment, fostering communication skills, leadership, and mutual learning.
-
08
Skill & Competency-Based Teaching
Alongside academics, students are trained through professional certifications, soft-skill development, and employability-focused modules to enhance career readiness.
-
09
Inclusive Teaching Practices
NSHM promotes equitable learning with bridge courses, academic support systems, and structured student mentoring to cater to diverse learner needs.
-
10
Continuous Assessment & Feedback
Student progress is monitored through assignments, quizzes, presentations, and practical evaluations, supported by timely and constructive feedback.
-
11
Community & Social Learning
Learning extends beyond the classroom through outreach programmes, community engagement, and extension activities that instil social responsibility.
-
12
Faculty Development & Pedagogy Training
Faculty members regularly participate in Faculty Development Programmes (FDPs), pedagogy training, and research workshops to ensure teaching excellence and innovation.
Scope & Career Opportunities
The field of Data Science integrates computer science, statistics, and artificial intelligence, enabling the conversion of vast amounts of raw data into valuable insights that foster innovation and informed decision-making. With the rapid expansion of digital data across diverse sectors such as business, healthcare, finance, education, and governance, the global demand for skilled data science professionals is growing at an unprecedented pace.
The programme also provides a solid foundation for pursuing advanced studies and research in specialized areas such as Artificial Intelligence, Machine Learning, Big Data Analytics, Cloud Computing, and Business Intelligence, opening a wide spectrum of academic and professional opportunities.
- Data Scientist Extract insights from structured and unstructured data to support strategic decision-making.
- Machine Learning Engineer Design and deploy intelligent models for automation and predictive analytics.
- Data Analyst / Business Analyst Analyze business data, identify patterns, and provide actionable recommendations.
- Data Engineer Build and maintain data pipelines, databases, and large-scale data systems.
- AI Engineer Develop artificial intelligence-based solutions and applications.
- Big Data Developer Work with large datasets using technologies such as Hadoop, Spark, and Kafka.
- Data Architect Design and manage data infrastructure and ensure optimal data flow across systems.
- Cloud Data Specialist Integrate data solutions with cloud platforms like AWS, Azure, or Google Cloud.
- Business Intelligence (BI) Developer Create data visualization dashboards and analytical reports using tools like Power BI, Tableau, or QlikView.
- IT and Software Services
- Cybersecurity Firms
- Telecom and Networking
- E-commerce
- Government and Public Sector Projects
- Startups working on emerging technologies
- Manufacturing and Automation
- Banking, Finance and FinTech
- Healthcare Technology
- AI and Analytics Companies
Industry Sectors Hiring Data Science Graduates
