B.Tech in Artificial Intelligence and Data Science: Department, Course Details, and Career Opportunities

Department Overview

The B.Tech in Artificial Intelligence (AI) and Data Science is an interdisciplinary program combining computer science, statistics, and domain knowledge to develop intelligent systems and analyze complex data. The department typically focuses on:

• Machine Learning & AI:

  Techniques for building intelligent systems.

• Data Science:

  Methods for data analysis, statistics, and big data technologies.

• Interdisciplinary Applications:

  Applying AI and data science across various fields such as healthcare, finance, and more.

Vision

To enlighten the society by producing leaders in artificial Intelligence and Data Science through teaching and exploration

Mission

• To fortify students to solve societal issues through AI system by inculcating values and ethics.
• To strengthen employability and entrepreneurial skills in the field AI & DS through experiential and self-directed learning
• To foster research genius among the students through project based learning.

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.

PEO

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PO

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PSO

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Course Outcomes

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Regulations & Syllabus

• B.Tech.AIDS syllabus