The Department of Computer Science and Engineering at Techno Main Salt Lake provides a dedicated program in Data Science. Students learn to extract insights from complex data sets using statistical methods, machine learning algorithms, and data visualization techniques. The department's state-of-the-art infrastructure and partnerships with leading organizations in the field provide students with hands-on experience and job opportunities in the rapidly growing field of data science.
| Program | Intake | Duration | Entry Level |
|---|---|---|---|
| B.Tech - CSE - Data Science | 60 | 4 Years | After Class 12 |
| B.Tech (L) - CSE - Data Science | 6 | 3 Years | After Diploma/B.Sc |
1. To develop an individual’s insight towards real life problem solving by delivering quality education in the field of Data Science and associated subdomains.
2. To identify and nourish talented and focused individuals to become leaders and/or innovators in the world of Data Science, with the aim to create substantial number of job providers in the long run
3. To encourage cross-disciplinary thinking and broaden competence in cutting-edge tools and technology in order to construct data-intensive systems and acquire professional ethics
4. To enhance employable skills and support self-sustainability by developing interpersonal skills among individuals via intra, inter-departmental, and inter-institutional activities.
5. To emerge as highly sought-after location for Data Science technology development, research, and entrepreneurship on a worldwide scale
To be a leader in the field of Data Science education by providing a holistic learning environment to produce generations of top-class engineers with a proclivity towards research, contributing heavily to the data driven world in terms of business enhancement as well as social engineering.
1. To excel in exhibiting professional data science skills to build robust knowledge models to generate valuable insights, required for data-driven decision making that facilitates in sustainable growth of business and society.
2. To establish expertise in applying innovative analytical and design skills to demonstrate strong research capabilities in the field of Data Science.
3. To embrace lifelong learning and professional development in order to keep oneself relevant and employable despite the rapid advancement of tools and techniques in the field of data science.
4. To exhibit leadership and business acumen in several cross-discipline areas of Data Science and subsequently emerge as employers and contribute towards overall advancement of society.
1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
4. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice
7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions
11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change
1. Ability to apply the principles of Data Science, Data Management, Data Security, and Optimization in order to generate and maintain predictive intelligence.
2. Ability to apply analytical, statistical and visualization techniques to investigate humongous quantity of data, extract hidden patterns, and make intelligent predictions to solve complex real-world problems.
3. Ability to identify and analyze research gaps in the field of Data Science and associated multidisciplinary areas to come up with innovative and optimal solutions for technological and socio-economic problems
| Laboratories | ||
|---|---|---|
| Applied and Adaptive Optics lab | Material Characterization lab | Data Structure Using C lab |
| Atomic and Molecular Physics lab | Statistical Mechanics & Electromagnet. Theory lab | Statistics using MAT Lab |
| Computational Physics lab | Biochemistry and Analytical Techniques lab | Micro Programming & Architecture lab |
| General Physics lab | Microbiology lab | Programming lab |
| Modern Physics lab | Genetic Engineering lab | Business presentation and language lab |
| Optics lab | Immunology lab | Object-Oriented Programming lab |
| Solid State Physics lab | Bioinformatics lab | Unix lab |
| Solid state Technology lab | Bioreactor operations lab | Graphics & Multimedia lab |
| General Chemistry lab | Food and Environmental Biotechnology lab | Basic Computer Application lab |
| Organic Chemistry lab | Molecular Biology lab | Electronic Media: Planning & Production lab |
| Inorganic Chemistry lab | Basic Microscopy & Instrumentation lab | Electronic Media: Writing, Editing & Execution lab |
| Polymer Processing lab | Cytogenetic Techniques lab | Press Photography lab |
| Polymer Technology lab | Tissue Culture Techniques lab | Film & Television: Theory & Practice lab |
| Chemical Engineering lab | Numerical Methods lab | Design & Page Make up lab |