+91 8100836863   |  admission@mckvie.edu.in
+91 8100836863   |  admission@mckvie.edu.in
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Course Overview
MCKV Institute of Engineering offers a future-ready B.Tech in Computer Science & Engineering with Specialization in Data Science, designed to meet the growing demands of the digital era. The programme blends strong computer science fundamentals with advanced technologies such as Machine Learning, Big Data, Cloud Computing, Cyber Security, Data Analytics, Data Visualization, and Business Intelligence. With experienced faculty, modern laboratories, industry collaborations, internships, and hands-on projects, students gain practical exposure and real-world analytical skills. The curriculum focuses on innovation, research, and data-driven problem solving, preparing students for rewarding careers as Data Scientists, Data Analysts, Business Analysts, Software Engineers, and Data Engineering Professionals in leading global industries.
Intake Capacity
60
Affiliated to
MAKAUT
Approved by
AICTE
Accredited by
NAAC
"A" grade Autonomous Institute under UGC Act. 1956
Course Enquiry
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Eligibility Criteria for Admission
  • Candidates must have valid WBJEE / JEE (Main) Score.
  • Candidates must have passed the qualifying examination (10+2) with Physics and Mathematics along with Chemistry / Biotechnology / Biology / Computer Science / Computer Applications / Technical Vocational subjects with individual pass marks (in both theory and practical, wherever applicable) in all three subjects as stated above in regular class mode.
  • Candidates must have obtained at least 45% marks (40% in case of SC, ST, OBC, PwD) in all the three subjects taken together. As mentioned in Table 8.4 in AICTE Approval Process Handbook-2024, and as annexed (Appendix-7).
  • Candidates must have passed English in “10+2” examination with at least 30% marks.
  • The board of qualifying examination must be recognized either by the Central or State Government.
  • Both B.Sc. Candidates and Diploma holders in Engineering & Technology will also be eligible for admission to the 1st year Engineering Degree Courses, subject to vacancies in the 1st year classes in case the vacancies as lateral entry at second year are exhausted. However, the admission shall be based strictly on the eligibility criteria as mentioned above.
Our Vision
To be a well-recognized department by providing students with an excellent environment for academics and research in the realm of Computer Science.
Our Mission
  • To provide a conducive atmosphere for the teaching learning process.
  • To make the students understand the role of Computer Science in social and global needs.
  • To develop theoretical knowledge of the students and make them skilled professionals by involving them in application-oriented activities, project works and arranging workshops, seminars and industry-oriented programs.
  • To facilitate the students in adapting to the changing technologies and to be successful in their professional and research works.
Programme Educational Objective
  • PEO-I: Graduates of the program will be successful computing professionals especially will have the ability to work as Data Engineers.
  • PEO-II: Graduates will demonstrate leadership quality in their workspace with consideration for society and the environment.
  • PEO-III: Graduates will demonstrate lifelong learning in their profession and in pursuing higher studies.
Programme Outcomes
POs describe what students are expected to know or will be able to do by the time they complete graduation from the program. The Program Outcomes of UG in Computer Science & Engineering are as follows:

  • PO-I: Engineering Knowledge: Apply knowledge of mathematics, natural science, computing, engineering fundamentals and an engineering specialization as specified in WK1 to WK4 respectively to develop to the solution of complex engineering problems.
  • PO-II: Problem Analysis: Identify, formulate, review research literature and analyze complex engineering problems reaching substantiated conclusions with consideration for sustainable development. (WK1 to WK4)
  • PO-III: Design/Development of Solutions: Design creative solutions for complex engineering problems and design/develop systems/components/processes to meet identified needs with consideration for the public health and safety, whole-life cost, net zero carbon, culture, society and environment as required. (WK5)
  • PO-IV: Conduct Investigations of Complex Problems: Conduct investigations of complex engineering problems using research-based knowledge including design of experiments, modelling, analysis & interpretation of data to provide valid conclusions. (WK8).
  • PO-V: Engineering Tool Usage: Create, select and apply appropriate techniques, resources and modern engineering & IT tools, including prediction and modelling recognizing their limitations to solve complex engineering problems. (WK2 and WK6)
  • PO-VI: The Engineer and The World: Analyze and evaluate societal and environmental aspects while solving complex engineering problems for its impact on sustainability with reference to economy, health, safety, legal framework, culture and environment. (WK1, WK5, and WK7).
  • PO-VII: Ethics: Apply ethical principles and commit to professional ethics, human values, diversity and inclusion; adhere to national & international laws. (WK9)
  • PO-VIII: Individual and Collaborative Team work: Function effectively as an individual, and as a member or leader in diverse/multi-disciplinary teams.
  • PO-IX: Communication: Communicate effectively and inclusively within the engineering community and society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations considering cultural, language, and learning differences.
  • PO-X: Project Management and Finance: Apply knowledge and understanding of engineering management principles and economic decision-making and apply these to one’s own work, as a member and leader in a team, and to manage projects and in multidisciplinary environments.
  • PO-XI: Life-Long Learning: Recognize the need for, and have the preparation and ability for i) independent and life-long learning ii) adaptability to new and emerging technologies and iii) critical thinking in the broadest context of technological change. (WK8)
Programme Specific Outcomes
  • PSO – I: Students at the time of graduation will be able to design and develop Computer-based systems in the areas related to IoT, Machine learning, Cloud computing, and data analytics of varying complexity.
  • PSO – II: Students at the time of graduation will be able to apply domain knowledge and expertise for enhancing research capability to transform innovative ideas into reality.
Faculty Members
Mr. Subir Bhadra
Assistant Professor and Teacher In-charge
Computer Science and Engineering
M.Sc., M. Tech, Ph.D (Pursuing)
Teaching : 14.7 Yrs, Research : 2 Yrs, Industry : 2 yr
Dr. Diptadip Maiti
Associate professor
Computer Science and Engineering
B. Tech, M. Tech, PhD
Teaching : 20.3 Yrs, Research : 4.3 Yrs,
Mr. Nilay Kumar Nag
Assistant Professor
Computer Science and Engineering
BSc (Hons), MS(Software Systems), PhD (Pursuing)
Teaching : 25.3 Yrs, Research : 1.3 Yrs, Industry : 6 yr
Mr. Partha Pratim Saha
Assistant Professor
Computer Science and Engineering
B.Sc. (Hons), M.Sc.(CS), M. Tech, PhD (Pursuing)
Teaching : 20.9 Yrs, Research : 3 Yrs, Industry : 0.5 yr
Dr. Shrabanti Kundu
Assistant Professor
Computer Science and Engineering
MCA, M. Tech (Gold Medalist), PhD (Engg)
Teaching : 4.5 Yrs, Research : 4.5 Yrs,
Mr. Sudipta Hazra
Assistant Professor
Computer Science and Engineering
B. E., M. Tech, PhD (Pursuing)
Teaching : 15.3 Yrs, Research : 1.5 Yrs, Industry : 1.8 yr
Student Achievements
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Faculty Contributions
Academic Year 2021-2022: Journal Publications
  • B. S. Phadikar, A. Phadikar, and S. S. Thakur, "A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms," Multimedia Tools and Applications, vol. 80, no. 10, pp. 15619–15646, 2021, doi: 10.1007/s11042-021-10573-0.
  • S. Bandyopadhyay, S. S. Thakur, and J. K. Mandal, "Emotion detection for online recommender system using deep learning: a proposed method," Innovations in Systems and Software Engineering, vol. 20, pp. 719–726, 2024, doi: 10.1007/s11334-022-00437-7.
  • S. Bandyopadhyay, S. S. Thakur, and J. K. Mandal, "Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society," Innovations in Systems and Software Engineering, vol. 17, no. 1, pp. 45–52, 2021, doi: 10.1007/s11334-020-00372-5.
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