- Course Details
- Career Opportunities
- Dept. Best Practices
- ADJUNCT FACULTY
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING (DATA SCIENCE)
The Department of Computer Science & Engineering (Data Science) was established in the academic year 2020-21. The department started with an intake of 60 students in the UG program. The department has highly qualified, experienced and dedicated teaching and supporting staff. The department has fully equipped laboratories with modern and sophisticated equipment. Computer Science and Engineering (Data Science) is a new, exponentially growing field which consists of a set of tools and techniques used to extract useful information from data. The programme encompasses Data Science as an interdisciplinary, problem-solving-oriented subject that learns to apply scientific techniques to practical problems. The course curriculum involves a blend of data inference, algorithm development, and technology to analytically solve complex problems.
About the Course
The core data science subjects focus on Data Analytics, Visualization, Predictive Modelling and Analytics for data-driven decision-making. Knowledge Representation, Machine Learning, Artificial Intelligence, and Deep Learning are also offered to meet the industry requirements. Computer Engineering with Data Science branch not only enriches the students with core functional competencies such as expertise in technologies & tools required for Data Science but also arms students with comprehensive complementary skillsets including but not limited to data-driven approach, critical thinking, problem-solving, creativity, agility, self-driven, multi-disciplinary knowledge and other soft skills. Our elite program prepares students not only for excellence in the core field of Data Science but also prepares students as value creators and meaningful contributors to business functions focusing towards maximizing business success and excellence.
|Course Duration:||4 years (Regular) | 3 years (Lateral Entry)|
|No. of Semesters:||8 (Regular) | 6 (Lateral Entry)|
|No. of Seats:||60|
|Eligibility:||10+2 System of Education. Must have secured a pass in Physics, Chemistry and Mathematics in the qualifying examination with 45% marks in aggregate.|
|Scope for Higher Studies:||M.E. | M. Tech | M.B.A. | M.S.|
- Integrated Liberal education program to gain insights into subjects like Psychology,Design Thinking, Critical Thinking
- Student-centered pedagogy
- Curriculum focused on recent trends
- Blended & Hybrid Learning
- Provides opportunities for hands-on and experiential learning
- Promoting deep learning through project-based learning
- Preparing students for evolving job roles in the chosen area of specialization
- Emphasis on design-oriented thinking, Communication, Collaboration and Creativity
- Offers flexibility in choosing elective courses for the understanding of emerging technologies
- Offers major, minor and specialization as part of the four-year programme
- Startup ecosystem to translate the idea into business models
- Encourage Entrepreneurship
- Targeted towards equipping students for future skill sets
A field in the spotlight, data science offers high salaries and big opportunities. The field that is considered the hottest career option of the 21st century is Data Science. Data science is one of the fastest-growing career opportunities in the country. Our program is designed to meet the increasingly aggressive demand for qualified data scientists in virtually every sector of the economy. Data Scientists are among the most sought-after professionals in the IT sector, with demand for skilled technocrats in that field outpacing other IT jobs by a wide margin. The graduates of the program will have scope in different industries that work on domains such as finance, business, economics, and healthcare. The professionals will be in high demand as Data Managers, Data Scientists, Data analysts, Product analysts, System analysts, Software Quality Assurance analysts, Information Security analysts, Machine Learning Engineers, Machine Learning architects, Artificial Intelligence engineers, Application developers and Software engineers.
Department Best Practices
- Implementation of Mentor-Mentee concept: Enables focus on individual students, increasing the scope of giving guidance, counselling and monitoring for better improvement of every student.
- Enhancement of Programming Language skills: The department organizes various workshops on emerging technologies amongst students for better career prospects and the development of technical skills in the students.
- Peer to Peer Learning: The students participate in mock interviews and group discussions to give them a sense of confidence and groom them towards professionalism.
- Activity-based Learning: The best teaching practice-formative assessment, and assignment design to foster student engagement and ownership.
- Placement Oriented Training: Campus recruitment training is given to the students from II year onwards to cater for the needs of students for placements.
- Organizing Workshops and Guest Lectures:Reputed persons from the software industry and academicians are invited to the campus to give expert talks on emerging technologies.
- Faculty Development Programmes (FDPs): The department organizes FDPs and also deputes the faculty members to other organizations to enhance their knowledge with advanced technologies.
- Research-Oriented Promotions: Research and Development (R&D) cell play a critical role in the process of innovation.
- Incubation Cell: With the support of the Incubation cell, our students will be encouraged to do Live Projects offered by college or industry.
- Internships: Internship is a system of on-the-job training provided for our students in a real-time environment of the IT Industry. Internships provide opportunities for students to gain experience in their field and determine if they have an interest in a particular career.
CSE (Data Science) Organized workshop on Data Visualization
The department of Information Science and Engineering, and Computer Science and Engineering (Data Science) organized a one-day hands-on workshop on "Data Visualization" for CSE (Data Science)
students on December 16, 2022. The resource person for the workshop was Prof. Aswathy M A, from MIT Academy of Engineering, Pune. Around 60 students of third-year CSE (Data Science) participated
in the workshop. The students gained valuable insights into the areas of data visualization and its applications in the current world.
The program was coordinated by Dr. Navaneeth Bhaskar, Associate Professor, ISE/CSE(DS). This workshop was organized to bridge the gap between the curriculum and the industry needs and to equip the students with the necessary knowledge to work in the field of Data Science.
- Navaneeth, B. and Suchetha, M., 2019. PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications. Computers in biology and medicine, 108, pp.85-92. DOI: 10.1016/j.compbiomed.2019.03.017, Elsevier Publications(SCI Journal with Impact Factor: 6.698).
- Navaneeth, B. and Suchetha, M., 2020. A dynamic pooling based convolutional neural network approach to detect chronic kidney disease. Biomedical Signal Processing and Control, 62, p.102068. DOI: 10.1016/j.bspc.2020.102068, Elsevier Publications (SCI Journal with Impact Factor: 5.076).
- Navaneeth, B., Suchetha, M. and Philip, N.Y., (2020). Time series classification based correlational neural network with bidirectional LSTM for automated detection of kidney disease. IEEE Sensors Journal, IEEE Publications (SCI Journal with Impact Factor: 4.325).
- Navaneeth, B. and Suchetha, M., 2020. A computationally efficient correlational neural network for automated prediction of chronic kidney disease. IRBM. (In Press) DOI: 10.1016/j.irbm.2020.07.002, Elsevier Publications (SCI Journal with Impact Factor: 5.5).
- Navaneeth, B. and Suchetha, M., 2020. Analysis of salivary components as non-invasive biomarkers for monitoring chronic kidney disease. International Journal of Medical Engineering and Informatics, 12(2), pp.95-107. DOI: 10.1504/IJMEI.2020.106896, Inderscience Publications (Scopus Indexed).
- Navaneeth, B. and Suchetha, M., 2019. A Deep-Learning-Based System for Automated Sensing of Chronic Kidney Disease. IEEE Sensors Letters, 3(10), pp.1-4. DOI: 10.1109/LSENS.2019.2942145, IEEE Publications (Scopus Indexed).
- Navaneeth, B. and Suchetha, M., 2019, July. An Approach for Analysis and Prediction of CKD using Deep Learning Architecture. In 2019 International Conference on Communication and Electronics Systems (ICCES) (pp. 1660-1664). IEEE. DOI: 10.1109/ICCES45898.2019.9002214 (Scopus Indexed).
|#||Name||Qualification||Designation in External|
|1||Mr. Vipin Kumar N||MS||Assistant Professor|
|2||Mr. Mallikarjuna M Dongre||M.Tech||Assistant Professor|
|3||Mr. Sheikh Moidin K M||M.Tech||Assistant Professor|