ARTIFICIAL INTELLIGENCE

PROGRAM EDUCATIONAL OBJECTIVES (PEOs)

Graduates can:

• Utilize their proficiencies in the fundamental knowledge of basic sciences, mathematics, Artificial Intelligence, data science and statistics to build systems that require management and analysis of large volumes of data.
• Advance their technical skills to pursue pioneering research in the field of AI and Data Science and create disruptive and sustainable solutions for the welfare of ecosystems.
• Think logically, pursue lifelong learning, and collaborate with an ethical attitude in a multidisciplinary team.
• Design and model AI-based solutions to critical problem domains in the real world.
• Exhibit innovative thoughts and creative ideas for effective contribution towards economy building.

PROGRAM SPECIFIC OUTCOMES (PSOs)

Graduates should be able to:

• Evolve AI-based efficient domain-specific processes for effective decision-making in several domains such as business and governance.
• Arrive at actionable foresight, insight, hindsight from data for solving business and engineering problems.
• Create, select, and apply the theoretical knowledge of AI and Data Analytics along with practical industrial tools and techniques to manage and solve wicked societal problems.
• Develop data analytics and data visualization skills, knowledge acquisition, representation, and engineering skills to coordinate complex projects.
• Carry out fundamental research to cater to the critical needs of the society through cutting-edge technologies of AI.

ROLE OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE ENGINEER

Artificial Intelligence (AI) and Data Science Engineers play crucial roles at the intersection of technology and data-driven decision-making. Key responsibilities include:

• Developing AI Models: Designing and developing machine learning and deep learning models to solve complex problems and improve decision-making processes.
• Data Collection and Preparation: Gathering and preprocessing large datasets to ensure they are clean, relevant, and suitable for analysis.
• Feature Engineering: Identifying and selecting relevant features from data to enhance model performance and accuracy.
• Model Training and Evaluation: Training machine learning models using appropriate algorithms and evaluating their performance using metrics like accuracy, precision, recall, and F1-score.
• Optimization and Tuning: Optimizing model parameters and hyperparameters to improve model performance and efficiency.
• Deployment of Models: Deploying AI models into production environments, ensuring scalability, reliability, and integration with existing systems.
• Monitoring and Maintenance: Monitoring model performance post-deployment, retraining models periodically, and addressing issues such as concept drift.
• Data Visualization and Interpretation: Visualizing data insights and model outputs to communicate findings and recommendations to stakeholders effectively.
• Research and Innovation: Staying updated with the latest advancements in AI and data science, experimenting with new algorithms and techniques, and contributing to research publications and conferences.
• Collaboration and Communication: Collaborating with cross-functional teams to implement solutions and deliver value to the organization.

CAREER PROSPECTS:

Artificial Intelligence (AI) and Data Science Engineers are in high demand across diverse industries. Promising career prospects include:

• Machine Learning Engineer
• Data Scientist
• AI Research Scientist
• AI Consultant
• Business Intelligence Analyst
• Big Data Engineer
• AI Product Manager
• Healthcare Data Scientist
• AI in Robotics Engineer