Vision & Mission:

Vision:

To empower learners with the knowledge and skills of Artificial Intelligence and Data Science to promote innovations and create transformative solutions in a rapidly evolving computing world.

Mission:

M1: To foster problem-solving abilities of students through the collaboration of a rigorous curriculum and experiential learning while emphasizing responsible practices.
M2: To prepare students with interdisciplinary skill sets for impactful careers and inspire them to shape the future of Artificial intelligence and Data science by bridging the gap between academia and industry.
M3: To strengthen students with the strong academic background, effective communication skills and moral values to thrive in ever challenging world.

Faculty:

1. Faculty List:

2. Faculty Members:

Photo	Faculty name and description
	Name: Dr. A. R. Panhalkar Designation: Assistant professor & HOD Qualification: M.E.(Comp),Ph.D Exp Teaching: 18 yr Exp Industrial : Nil Area of Interest: Data Mining Biodata: Read More
	Name: Ms. A. N. Nawathe Designation: Assistant Professor Qualification: M.E.(CSE),Ph.D - Pursuing Exp Teaching: 24 yr Exp Industrial : Nil Area of Interest: Data Mining Biodata: Read More
	Name: Prof. S.G. Dighe Designation: Assistant Professor Qualification: M.E.(Electronics), Ph.D- Pursuing Exp Teaching: 12 yr Exp Industrial : Nil Area of Interest: Digital System, Internet of Things Biodata: Read More
	Name: Mr. S. P. Shinde Designation: Assistant Professor Qualification: M.E.(Comp), Ph.D- Pursuing Exp Teaching: 10 yr Exp Industrial : Nil Area of Interest: Data Science, Hadoop Biodata: Read More

3. Supporting Staff:

Photo	Faculty name and description
	Name: S. B. Talole Designation : Technical Assistant Qualification : MCS Acad. Exp(yrs) : NIL Exp Industrial (yrs) : NIL
	Name:Mr. G. B. Ugalmugale Designation:Peon Qualification :12 Science Working Exp:1 Exp Industrial (yrs): NIL

Syllabus:

UG:

Sr. No.	2020 Course
1.	FE Common
2.	SE AIDS
3.	TE AIDS
4.	BE AIDS

Activities:

1. AIESA activities

2. Alumni Interactions

4. Value Added Courses:

5. Guest Lectures Organized

Program Educational Objectives(PEO's):

1. PEO1. To demonstrate technical proficiency in AI and Data Science methodologies, algorithms, and tools, applying them creatively to solve complex real-world problems.

2. PEO2. To derive the ethical implications of AI and Data Science, applying responsible practices while driving innovation and developing novel solutions.

3. PEO3. To collaborate with multidisciplinary teams, effectively communicating technical concepts to diverse stakeholders, and working towards shared goals.

4. PEO4. To embrace a mindset of lifelong learning, continuously updating their knowledge and skills to stay current in the rapidly evolving field, and adapting to new technologies and challenges.

5. PEO5. To demonstrate leadership qualities, effectively leading teams, managing projects, and leveraging AI and Data Science to make a positive impact on organizations and society.

Program Specific Outcomes(PSO's)

A graduate of the Artificial Intelligence and Data Science Program will demonstrate-

1. PSO1: Professional Skills-The ability to understand, analyze and develop computer programs in the areas related to algorithms, system software, multimedia, web design, networking, artificial intelligence and data science for efficient design of computer-based systems of varying complexities.

2. PSO2: Problem-Solving Skills- The ability to apply standard practices and strategies in software project development using open-ended programming environments to deliver a quality product for business success.

3. PSO3: Successful Career and Entrepreneurship- The ability to employ modern computer languages, environments and platforms in creating innovative career paths to be an entrepreneur and to have a zest for higher studies.

Program Outcomes(PO's):

Students are expected to know and be able –

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.

Student List: (2024-2025)