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Artificial Intelligence and Data Science
Amrutvahini College of Engineering Sangamner



Artificial Intelligence (AI) and Data Science are two interconnected fields that are revolutionizing the way we analyze and utilize data to extract valuable insights and make informed decisions. AI involves the development of intelligent machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Data Science, on the other hand, focuses on extracting knowledge and insights from large and complex datasets using various statistical and computational techniques.
The scope of AI and Data Science is vast and encompasses numerous domains and industries. In today's data-driven world, organizations across sectors such as finance, healthcare, marketing, manufacturing, and transportation are increasingly leveraging AI and Data Science to drive innovation, improve efficiency, and enhance decision-making processes.
AI and Data Science techniques enable businesses to analyze vast amounts of data to uncover patterns, trends, and correlations, enabling them to make data-driven predictions and optimize operations. They also play a crucial role in developing intelligent systems, such as recommendation engines, virtual assistants, and autonomous vehicles, which enhance user experiences and streamline processes.
Moreover, AI and Data Science have significant implications for societal challenges, including healthcare diagnostics, personalized medicine, climate change analysis, fraud detection, and cyber security. These fields have the potential to transform various aspects of our lives, ranging from personalized customer experiences to smart cities and sustainable development.
To thrive in this AI-driven era, individuals need to acquire knowledge and skills in AI and Data Science. Understanding algorithms, statistical methods, machine learning techniques, and programming languages, as well as ethical considerations and responsible AI practices, is becoming increasingly essential for professionals across industries.
By delving into the realm of AI and Data Science, learners can unlock new opportunities, contribute to cutting-edge research, and drive innovation that positively impacts society.

Vision & Mission:


To empower learners with the knowledge and skills to harness the power of Artificial Intelligence and Data Science, driving innovation and transformative solutions in a rapidly evolving computing world


M1: Our mission is to empower students with the knowledge, skills, and ethical awareness needed to excel in the field of Artificial Intelligence and Data Science.
M2: Foster problem-solving abilities through the collaboration of a rigorous curriculum and experiential learning while emphasizing responsible practices.
M3: Prepare students with interdisciplinary skill sets for impactful careers and inspire them to shape the future of AI and data science by bridging the gap between academia and industry.


1. Faculty List:

Sr.No. Academic Year Details
1. 2023-2024 Read More

2. Faculty Members:

Photo Faculty name and description
Name:Dr. R. G. Tambe
Designation : Asst. Prof.
Qualification : M.E(Comp), Ph.D (Comp.)
Exp Teaching (yrs): 12
Exp Industrial (yrs) : NIL
Area of Specialization: Remote Sensing, Satellite Image Processing, Deep learning, Machine Learning
Biodata: Read More
Name: Mr.A.R.Tambe
Designation: Associate Professor
Qualification: M.Sc.Math
Exp. Teaching:36 yrs
Exp. Industrial:Nil
Area of Specialization: Mathematics
Biodata: Read More
Name: S. R. Wakchaure
Designation : Asst. Prof.
Qualification : M.E(Comp)Ph.D Pursuing
Exp Teaching (yrs): 10.5
Exp Industrial (yrs) : NIL
Area of Specialization: Data Mining, Image Retrieval, IoT
Biodata: Read More



Sr. No. 2020 Course
1. FE Common

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.