Course Details

The curriculum for B.Tech in AI and Data Science generally includes the following components:

Core Courses

• Programming Fundamentals:

  Basics of programming languages like Python, C++, or Java.

• Data Structures and Algorithms:

  Essential for efficient data handling and problem-solving.

• Database Management Systems:

  Concepts of database design, SQL, and NoSQL databases.

• Statistics and Probability:

  Fundamental statistical methods and probability theory.

• Linear Algebra and Calculus:

  Mathematical foundations for machine learning.

Specialized Courses

• Machine Learning:

  Supervised and unsupervised learning, neural networks, and deep learning.

• Artificial Intelligence:

  Search algorithms, knowledge representation, and AI ethics.

• Big Data Technologies:

  Tools and frameworks for handling large datasets (Hadoop, Spark).

• Data Mining:

  Techniques for extracting patterns from large datasets.

• Natural Language Processing (NLP):

  Processing and analyzing human language data.

Electives and Advanced Topics

• Computer Vision:

  Image processing and analysis using AI techniques.

• Reinforcement Learning:

  Algorithms for training agents based on reward systems.

• Internet of Things (IoT):

  Integration of AI in smart devices and sensor networks.

• Cloud Computing:

  Deploying AI models and data processing on cloud platforms.

Practical Components

• Lab Sessions:

  Hands-on practice in coding, data analysis, and implementing AI models.

• Projects:

  Real-world projects to solve industry-specific problems using AI and data science.

• Internships:

  Opportunities to work with companies to gain industry experience.

Soft Skills

• Communication Skills:

  Effective presentation and technical writing.

• Teamwork and Leadership:

  Working in teams and managing projects.

Career Opportunities

Graduates in AI and Data Science have a wide array of career options across various industries. Key roles include:

Data Scientist

• Analyzing and interpreting complex data to help organizations make informed decisions.
• Developing predictive models and algorithms.

Machine Learning Engineer

• Designing and implementing machine learning models.
• Working with large datasets and tuning model performance.

AI Research Scientist

• Conducting research to advance the field of artificial intelligence.
• Publishing findings and developing new AI algorithms.

Data Analyst

• Extracting insights from data and generating reports.
• Performing statistical analysis and data visualization.

Big Data Engineer

• Managing and processing large-scale data systems.
• Ensuring the efficiency and scalability of data infrastructure.

Business Intelligence Developer

• Developing strategies to help businesses understand their data.
• Creating dashboards and visualizations for decision-makers.

AI Consultant

• Advising companies on how to leverage AI technologies.
• Developing customized AI solutions for business needs.

Product Manager (AI & Data Products)

• Overseeing the development and deployment of AI-driven products.
• Bridging the gap between technical teams and business stakeholders.

Emerging Trends

• AI Ethics and Governance:

  Ensuring ethical use of AI and data, addressing bias and fairness.

• Edge AI:

  Implementing AI at the edge of networks for real-time data processing.

• Autonomous Systems:

  Development of autonomous vehicles, drones, and robotics.

• Healthcare AI:

  Applying AI in medical diagnostics, personalized medicine, and health monitoring.

• AI in Finance:

  Algorithmic trading, fraud detection, and risk management.

A B.Tech in Artificial Intelligence and Data Science equips students with the skills needed to thrive in a data-driven world. With a blend of theoretical knowledge and practical experience, graduates are well-prepared for a diverse range of career opportunities in industries poised to benefit from advancements in AI and data science.

Regulations & Syllabus