Department of AI & Data Science Engineering

HOD's Desk

It gives me immense pleasure to welcome you to the Department of AI & Data Science Engineering at Dnyanshree Institute. We are living in an era where data is the new oil and artificial intelligence is the engine that drives innovation across every industry. Our department is committed to equipping students with the skills and mindset needed to thrive in this data-driven world.

Our curriculum is carefully designed to blend theoretical foundations with practical applications — spanning machine learning algorithms, data pipelines, neural networks, natural language processing, and business analytics. The department provides a stimulating environment with well-equipped labs, industry collaborations, and dedicated faculty who are passionate about AI research and teaching.

We encourage our students to participate in hackathons, national competitions, research projects, and internships to build a robust professional profile. I invite each student to make the most of the opportunities offered here and step forward as a responsible, ethical, and innovative AI engineer.

Faculty

Supporting Staff

Student Association of AI & Data Science Engineering Department

Sr.No	Name of Student	Designation
1	To Be Updated	President
2	To Be Updated	President
3	To Be Updated	Vice-President
4	To Be Updated	Vice-President
5	To Be Updated	Treasurer
6	To Be Updated	Co-Treasurer

Achievements Faculty

Achievements Student

Laboratories

AI-ML Laboratory (Room No. 123A)

The AI-ML Laboratory is equipped with high-end workstations pre-installed with Python, Jupyter Notebook, TensorFlow, PyTorch, Scikit-learn, and OpenCV. Students use this lab for hands-on experiments in supervised/unsupervised learning, computer vision, and natural language processing.

Data Science Laboratory (Room No. 123B)

The Data Science Laboratory provides tools such as R Studio, MATLAB, Power BI, Tableau, and Apache Spark. This lab supports coursework and projects in data preprocessing, exploratory data analysis, data visualization, and business intelligence.

Infrastructure and Facility

Activities Conducted in the Department

Syllabus

List of Departmental Books

Programme Outcomes (POs)

• Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and AI & Data Science specialization to the solution of complex engineering problems.
• Problem analysis: Identify, formulate, research literature, and analyze complex data-driven engineering problems reaching substantiated conclusions using first principles of mathematics, statistics, and data science.
• Design/development of solutions: Design AI-based solutions for complex engineering problems with appropriate consideration for public health and safety, and cultural, societal, and environmental considerations.
• Conduct investigations of complex problems: Use research-based knowledge and methods including design of experiments, analysis and interpretation of large datasets, and synthesis of information to provide valid conclusions.
• Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern AI/ML tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
• The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal, and cultural issues relevant to professional AI engineering practice.
• Environment and sustainability: Understand the impact of AI engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
• Ethics: Apply ethical principles and commit to professional ethics, responsibilities, and norms of AI engineering practice, including responsible AI and data privacy.
• Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
• Communication: Communicate effectively on complex AI engineering activities with the engineering community and society at large, including writing effective reports and design documentation, making presentations, and giving and receiving clear instructions.
• Project management and finance: Demonstrate knowledge and understanding of engineering and management principles and apply these to one's own work as a member and leader in a team, to manage data science projects in multidisciplinary environments.
• Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and lifelong learning in the rapidly evolving field of AI and Data Science.

Programme Specific Outcomes (PSOs)

Graduates of AI & Data Science Engineering Program will:

• Analyze and develop intelligent systems and data-driven applications using machine learning, deep learning, natural language processing, and computer vision techniques to address real-world problems.
• Demonstrate proficiency in data collection, preprocessing, feature engineering, model development, and deployment using contemporary tools and frameworks such as Python, TensorFlow, PyTorch, Spark, and cloud platforms.
• Provide scalable data solutions and AI-based models for real-time issues following engineering professionalism for the benefit of society.

PEO of Dept.

• To develop graduates who have strong fundamental knowledge in Artificial Intelligence, Machine Learning, Data Science, and related engineering disciplines.
• To develop graduates with contemporary computing, data analytics, and AI deployment skills applicable in diverse industrial sectors.
• To develop graduates with the ability to analyze local/global challenges and propose ethical, data-driven, and socially responsible solutions.

MOU