Department of Artificial Intelligence and Data Science

About the Department

B. Tech. in Artificial Intelligence and Data Science is a programme offered at Karpagam College of Engineering. The future era of technical education is stepping to Artificial Intelligence (AI) and Machine Learning (ML), Karpagam College of Engineering always takes young engineers to the next edge of future technologies. The program will open various career opportunities involving innovation and problem solving using Artificial Intelligence (AI) and Machine Learning (ML) technologies as well as research careers in AI and ML.

Along with courses that provide specialization in AI, students will also have options to explore some applied domains such as computer vision, natural language processing, robotics, data analytics, and computer security. This enables the B. Tech. in AI graduates to take up careers in R&D positions in Industry, academia, and research laboratories. The 4-year undergraduate program is divided into 8 semesters, with each year containing 2 semesters.

The course content provides the students with proper insight about the domain of Artificial Intelligence with a firm foundation in ‘Mathematics for Artificial Intelligence. The carefully designed syllabus provides the students with deep knowledge in the core areas of Algorithm analysis and design, Modern Computer Architecture, foundations of Artificial Intelligence, Data Science and Machine Learning and Parallel and Distributed Data Management, etc.

Highlights of the Specialisation

AI and Data Science is the current trend ruling the business world and it is a highly paid career now. Artificial Intelligence and data science is a suitable course for those who would like to develop various intelligent business solutions. Big data solutions have changed the way business models are built and run. This study contributes much in manufacturing, e-commerce, banking, finance, transport, and healthcare industry.

The Core courses give them sufficient expertise in the areas of Algorithm Analysis and Design, Modern Computer Architecture, Artificial Intelligence Foundations, Data Science and Machine Learning, Parallel and Distributed Data Management, etc. Elective courses include various application domains of AI such as Robotics, Video/Image Analytics, Medical Signal Processing, Agents Based Systems, Data Mining and Business Analytics, Natural Language Processing, Wireless Sensor Networks, Internet of things, etc.

Once they complete the course, students get opportunities to get fully paid Internships and placement offers at MNCs and IT/ITES companies like Intel, Cerner, Robert Bosch, DELL, etc. Also, they could publish quality research papers of the case studies/dissertations done as part of their B. Tech. program.

Vision

To become an integrative department through creativity and innovation in developing systems with Artificial Intelligence and Data Science to serve society.

Mission

  • Engage collaborative activities with the industry to produce industry-ready and future-ready talent.
  • Foster the spirit of lifelong learning in students through practical and social exposure beyond the classroom.
  • Provide multi-disciplinary research and innovation driven academic environments.
  • Practice and instil the highest standards of professional ethics, transparency and accountability at all levels.

Programme Educational Objectives

  • PEO1: Graduates will be able to demonstrate technical skills, competency in Artificial Intelligence and Data Science, exhibit proficiency and team management capability with proper communication in the job environment.
  • PEO2: Graduates will be able to work effectively in multidisciplinary engineering projects and support the growth of the economy of a nation by becoming entrepreneurs with a lifelong learning practice.
  • PEO3: Graduates will be able to carry out the research in the contemporary areas of Artificial Intelligence, Data Science and address the basic needs of the society.
Programme Outcomes (POs)

PO1: Engineering knowledge

Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

PO2: 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.

PO3: 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.

PO4: 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.

PO5: 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.

PO6: 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.

PO7: 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.

PO8: Ethics

Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

PO9: Individual and team work

Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO10: 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.

PO11: 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.

PO12: 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.

Programme Specific Outcomes
  • PSO1: Demonstrate the knowledge of Artificial Intelligence, Machine Learning and Data Science for designing and developing intelligent systems
  • PSO2: Implement Artificial Intelligence and data science methods for solving a problem in multi-disciplinary areas and design novel algorithms.
HOD Profile

Mr. Manikandan Sundaram
Mr. Manikandan Sundaram currently serves as the Head of Data Science and Analytics Centre and Head of Artificial Intelligence and Data Science at Karpagam College of Engineering (Autonomous), Coimbatore. He has more than 11 years of experience in both industry as well as in the field of education in India. He is well-experienced in many senior positions covering various hierarchies within companies including Data Architect, Head of Data Science and Analytics Centre for 5 years and as a Database Administrator. He did his master’s in Information Technology Engineering from SRM University, Chennai. Many awards have been conferred for his works in which few of them include SIFE-12 by SIFE Organization and Coder 2k06. His areas of expertise include Database Management systems, Data Analytics, Data Science, Machine intelligence and Computer Vision.

Faculty
S.No Name Designation
1 Mr. Manikandan Sundaram Assistant Professor & Head – In charge
2 Mr. Surenther I Assistant Professor
3 Ms. Sathya K Assistant Professor
Domain to Deliver
Core Addition to Core
Data Science
Data Analytics
R Program
Foundation of AI
Machine Learning
Deep Learning
RPA / Robotics
Data Visualization
Computer Vision
NLP
Auto ML
Apache Spark
Apache Kafka
Apache Hadoop
Sqoop
Flume
Data Visualization
Lab Facilities
Job opportunities
  • AI Tech Lab
  • Microsoft lab
  • Data Centre
  • Computer Vision & IOT
  • Artificial Intelligence Engineer
  • Data Scientist
  • Business Intelligence Developer
  • Data Analyst
  • Product Analyst
  • Full stack developer
  • Machine Learning Engineer
  • Machine Learning Architect
Lab Facilities
IN GLOBAL IN INDIA
Amazon
Google
Databricks
DataRobot
Tata Consultancy Services
Accenture
Tiger Analytics
Amazon India
CTS
Programs

B.Tech. – Artificial Intelligence and Data Science

  • Duration: 4 years (Regular) / 3 years (Lateral Entry)
  • No. of Semesters: 8 (Regular) / 6 (Lateral Entry)
  • No. of Seats: Total – 120
  • Eligibility: 10+2 system of Education. Must have secured a pass in Physics, Chemistry and Mathematics in the qualifying examination.
  • Scope for Higher Studies: M.E. / M. Tech. / MBA / M.S. / Researcher