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With the exponential growth in data, Artificial Intelligence and Data Science (AI & DS) has emerged as one of the most, dynamic, and productive careers in technology. It deals with scientific methodologies, processes, and techniques drawn from different domains like statistics, cognitive science, computing and information science to extract knowledge from structured and unstructured data. The Artificial Intelligence and Data Science program is designed to prepare students to gain practical knowledge of data analytics methods and apply it in interdisciplinary domain. It is designed to inculcate computational thinking to solve practical problems. The program covers variety of topics related to Statistics, Data Mining, Machine Learning, and Artificial Intelligence to equip the students with knowledge of data analysis techniques and data-centric computation to address problems that require large data.


The offered program intends to develop

  • Understanding on collecting, categorizing, strategizing, analysing and interpretation of data in making various intelligent decisions in business applications.
  • Development of data driven solutions, data visualization tools and techniques to analyse big data.
  • Solving various computational and real-world problems using the concepts of machine learning and deep learning model.

Future Prospects in AI & DS

On successful completion, AI & DS graduate students can choose specialization to pursue higher studies in domains like Expert Systems, Natural Language Processing, Neural Networks, Robotics, Fuzzy Logic Systems, etc. Many universities at Global, National and Local level offer specialized courses in M.S., M.Tech, M.E., MBA, and PG Diploma for AI & DS undergraduates.

Availability of enormous data and need to solve existing problems, in domains such as, Healthcare, Agriculture, Smart city, Smart Vehicles to name a few, there are ample opportunities for entrepreneurship. We, at SIESGST provide support to promote entrepreneurship by conducting awareness sessions and mentoring students.

Students with Research aptitude can also pursue it in interdisciplinary domain. Graduate students having an adequate amount of research experience along with an exceptionally great academic background can pursue a PhD.


To provide quality Education in the field of Artificial Intelligence and Data Science to provide sustainable solution for the society


  1. To impart quality Education to meet the challenges in the field of Artificial Intelligence and Data Science.
  2. To inculcate modern technology usage to fulfil the needs of the society.
  3. To nurture lifelong learning skills to develop professional growth and meet industry expectations. 

Program Educational Objectives (PEO)

  1. Apply Artificial Intelligence and Data Science skill to solve challenging and multi-disciplinary domain.
  2. Imbibe students for life long learning skill and research to adapt rapidly evolving skill in Artificial Intelligence and Data Science. 
  3. Inculcate entrepreneurship and leadership skills for professional growth.

Artificial Intelligence and Data Science : PSO, PO’s & CO’s

Program Specific Outcomes (PSO)

  1. To analyse and apply logical and computational skills to solve Artificial Intelligence and Data Science problems.
  2. Implement Artificial Intelligence and Data Science techniques such as data analysis, machine learning, data mining, neural networks and design efficient algorithms using emerging technologies in the field.

Program Outcomes (POs)

  1. Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialisation to the solution of complex engineering problems.
  2. Problem analysis: Identify, formulate, research literature, and analyse 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, process to meet the specifications with 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 modelling 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 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. Some of them are, 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 ateam, to manage projects and in multidisciplinary environments.
  12. Lifelong Learnings: Recognise the need for, and have the preparation and ability to engage in independent and lifelong learning in the broadest context of technological change.

Course Outcome (CO)

  • Course Outcome 2021-22

Artificial Intelligence and Data Science : Faculty

Name Date of Joining Designation Qualification Status of Appointment
Ms. Rohini Gaikwad 15.02.2022 Assistant Professor B.E, M.E (CE) Full Time
Ms.Sneha Tapre 08.07.2022 Assistant Professor B.E, M.E (CE) Full Time
Ms.Pragati Akre 01.12.2022 Assistant Professor B.E (CE), M.E (IT) Full Time
Ms.Kajal Salekar 01.02.2023 Assistant Professor B.E (IT), M.E (IT) Full Time
Ms.Monali Deshmukh 15.02.2023 Assistant Professor B.E (CSE), M.E (CE) Full Time
Ms.Priyanka Dhavale 24.08.2023 Assistant Professor B.E (EXTC), M.E (Digital Systems) Full Time
Ms.Sushma Rathi 01.01.2024 Assistant Professor B.E (CE), M.Tech (CSE) Full Time
Ms.Smrithy C. S. 01.01.2024 Assistant Professor B.E (ECE), M.E (Comm.Systems) Full Time
Tejali Mhatre 02.01.2024 Assistant Professor B.E, M.E (IT) Full Time
Pranita Pingale 02.01.2024 Assistant Professor B.E, M.E (IT) Full Time
Manosha Shasikaran 08.01.2024 Assistant Professor B.E (ECE), M.E (EXTC) Full Time
Ms.Deepali Ganesh Jagtap 16.01.2024 Assistant Professor B.E (Electronics), M.E (Digital Systems) Full Time
Ms.Shubhangi Amit Deshmukh 16.01.2024 Assistant Professor B.E (EXTC), M.E (Digital Systems) Full Time
Ms.Supriya Ingale 19.01.2024 Assistant Professor B.E (Instrumentation Engg), M.E (Instrumentation & Control Engineering) Full Time
Mr.Hrishikesh Vichore 29.01.2024 Assistant Professor B.E (IT), M.S (Data Science) Full Time

Artificial Intelligence & Data Science : Technical Staff

Name Designation
Mr.Amol Patil Lab Attendant

Value Added Courses

To make students industry ready, at SIESGST we offer interdisciplinary value added courses which helps students in gaining practical knowledge in data analytics and their field of interest. The student development programs in Machine Learning with Python, Data Science with R, Text Mining, Cyber Security, Blockchain Technology, etc. are designed to empower them to employ computational thinking and data science tools to solve practical business problems.

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