Fundamentals of artificial intelligence – Understand AI principles, including intelligent agents, knowledge representation, and problem-solving techniques.
Data science & big data analytics – Study data processing, statistical analysis, and visualization to extract valuable insights from large datasets.
Machine learning models – Learn how to design, train, and optimize machine learning models for prediction, classification, and pattern recognition.
Deep learning – Delve into neural networks and deep learning architectures for applications like image recognition, natural language processing, and predictive analytics.
Natural language processing (NLP) – Understand techniques for processing and analyzing human language, powering applications like chatbots and translation.
Data mining & predictive analytics – Discover how to extract patterns from data and use them to make informed predictions.
Computer vision – Explore the principles of computer vision to analyze and interpret visual data in applications like autonomous vehicles and facial recognition.
Big data frameworks – Gain expertise in big data tools like Hadoop, Spark, and MapReduce for managing and analyzing massive datasets.
Ethics & responsible AI – Study the ethical implications of AI, focusing on issues like privacy, bias, and accountability.
Capstone project – Apply AI and data science knowledge to a real-world project, solving a complex business or social problem through research, analysis, and AI techniques.
Semester 1
- Mathematics I (Linear Algebra and Calculus)
- Introduction to Artificial Intelligence
- Programming Fundamentals (Python)
- Computer Fundamentals and Digital Logic
- English Communication Skills
- Basic Engineering Workshop
Semester 2
- Mathematics II (Discrete Mathematics)
- Data Structures and Algorithms
- Object-Oriented Programming (Java/C++)
- Computer Organization and Architecture
- Environmental Science
- Professional Ethics and Values
Semester 3
- Mathematics III (Probability and Statistics)
- Database Management Systems
- Operating Systems
- Machine Learning Basics
- Software Engineering Fundamentals
- Open Elective I (e.g., Psychology for AI, Economics for Engineers)
Semester 4
- Advanced Machine Learning
- Data Mining and Data Warehousing
- Deep Learning Fundamentals
- Natural Language Processing Basics
- Elective I (e.g., IoT in AI, Reinforcement Learning)
- Technical Communication
Semester 5
- Artificial Neural Networks
- Big Data Analytics
- Computer Vision
- Cloud Computing for AI Applications
- Elective II (e.g., Time Series Analysis, AI in Healthcare)
- Minor Project I
Semester 6
- Advanced Natural Language Processing
- Ethics in AI and Data Science
- Data Visualization Techniques
- Elective III (e.g., Explainable AI, Advanced Robotics)
- Industry Internship I (6-8 weeks)
- Project Management
Semester 7
- Advanced Topics in Artificial Intelligence
- Generative AI and GANs
- Quantum Computing for AI
- Elective IV (e.g., Cognitive Computing, AI for Smart Cities)
- Major Project I
- Seminar/Technical Paper Presentation
Semester 8
- AI and Data Science in Business Applications
- AI Model Deployment and Maintenance
- Elective V (e.g., AI for Cybersecurity, Autonomous Systems)
- Major Project II
- Industry Internship II (8-12 weeks)
Want to build a successful artificial intelligence and data science career in India? Let’s learn about your career opportunities:
- Artificial Intelligence Engineer
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Developer
- NLP Engineer
- Data Analyst
- Predictive Analytics Specialist
The B. Tech in Artificial Intelligence and Data Science is designed to equip students with a strong foundation in the principles and technologies driving the modern AI and data analytics landscape.
Graduates of B. Tech. in AI and Data Science can pursue roles such as:
- Data Scientist
- AI Engineer
- Machine Learning Engineer
- Data Analyst
- Business Intelligence Analyst
- Deep Learning Specialist
- Natural Language Processing Engineer
- Computer Vision Engineer
These roles are in demand across industries like healthcare, finance, e-commerce, manufacturing, and technology companies. Graduates can also explore research and academic opportunities in AI and data science.
Eligibility criteria typically include:
- Successful completion of 10+2 (or equivalent) with a focus on Mathematics, Physics, and Chemistry.
- A minimum percentage in 10+2 exams (varies by institution).
- Qualifying in entrance exams such as JEE Main, state-level engineering exams, or institutional exams.
Some institutions may also consider extracurricular achievements in programming or science.
Specializations in the syllabus may include:
- Natural Language Processing (NLP)
- Computer Vision
- Deep Learning
- Big Data Analytics
- Reinforcement Learning
- Predictive Analytics
- Robotics and Automation
- Cloud-based AI Solutions
- AI Ethics and Governance
These provide students with expertise in specific areas of AI and data science.
Placement support typically includes:
- Dedicated placement cell to connect students with recruiters.
- Resume building and interview preparation workshops.
- Internship opportunities with industry leaders.
- On-campus recruitment drives with top companies like and startups.
- Alumni network access for mentorship and career advice.
Support for international placements or higher studies, depending on individual aspirations.
Core subjects include:
- Fundamentals of Artificial Intelligence
- Machine Learning Algorithms
- Data Structures and Algorithms
- Database Management Systems
- Deep Learning
- Data Visualization and Analytics
- Big Data Technologies
- Natural Language Processing
- Mathematics for AI (Linear Algebra, Probability, and Statistics)
- Cloud and Distributed Computing
Students will gain:
- Proficiency in programming languages like Python, R, and Java.
- Strong analytical and problem-solving skills.
- Expertise in machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn.
- Knowledge of big data tools like Hadoop and Spark.
- Skills in data visualization using tools like Tableau or Power BI.
- Team collaboration and project management abilities.
- Research and critical thinking skills in AI and data science applications.
The program often includes:
- Internships with top-tier companies like Microsoft, Google, and Infosys.
- Capstone projects based on real-world challenges.
- Research opportunities under faculty guidance.
- Participation in hackathons and AI challenges.
- Exposure to industry tools and platforms like AWS, Azure, and Google Cloud AI services.
- These experiences enhance hands-on learning and employability.
Advantages include:
- Access to a booming job market with high demand for AI professionals.
- Opportunities to work in cutting-edge fields like robotics, autonomous vehicles, and personalized healthcare.
- Competitive salaries and career growth.
- The ability to contribute to solving global challenges using AI solutions.
- Comprehensive learning, combining technical expertise with ethical and social considerations in AI development.
Artificial Intelligence and Data Science is a four-year undergraduate course with core technologies.
info@seamedu.com
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