In today’s rapidly advancing technological landscape, the fields of Artificial Intelligence (AI) and Data Science are at the forefront of innovation. Pursuing a Bachelor of Technology (B.Tech) in Artificial Intelligence and Data Science equips students with the skills and knowledge necessary to thrive in these dynamic sectors. This article will delve into the curriculum of BTech in AI and Data Science, shedding light on the core subjects, skills acquired, and the engineering admission process for aspiring students.
Understanding B.Tech in Artificial Intelligence and Data Science
B.Tech in Artificial Intelligence and Data Science is a specialized undergraduate program designed to combine principles of computer science, mathematics, and data analysis. This interdisciplinary approach ensures that students not only learn about AI technologies but also understand how to process and interpret vast amounts of data effectively.
Subjects of B.Tech in Artificial Intelligence and Data Science
The curriculum adopted by Btech artificial intelligence and data science, colleges are generally recognized by AICTE and UGC or through an affiliating University. KCC Institute of Technology and Management is affiliated to AKTU and approved by AICTE offers a curriculum that is at part with global standards and includes topics and subjects that are highly relevant to give students a competitive edge.
B.Tech in Artificial Intelligence and Data Science Highlights
Duration
4 years
Eligibility
Candidates must have passed the 12th standard in the science stream or its equivalent from a recognized board
Average Fee
50,000- 1,50000 yearly
Average Salary
4 lakh to 6 lakh annually
Admission Criteria
Based on merit and entrance exam
Eligibility Criteria for B.Tech Admission in Artificial Intelligence and Data Science
To be eligible for admission in BTech programs in Artificial Intelligence and Data Science, candidates must meet the following criteria:
Educational Qualification: Candidates should have successfully completed their 10+2 examination or an equivalent examination from a recognized educational institution.
Minimum Percentage: A minimum aggregate score of 55% is required in the qualifying examination.
B.Tech AI and Data Science Curriculum
The curriculum for B.Tech in Artificial Intelligence and Data Science is meticulously crafted to align with the latest technologies and trends in the computer science industry. The primary objective of AI-focused subjects is to equip students with the skills necessary to develop machines that can think like humans, act rationally, and effectively process information.
The syllabus for B.Tech in Artificial Intelligence and Data Science is comprehensive and intricate, offering a wealth of knowledge and skills that are crucial for a successful career in this dynamic field. As the demand for AI skills continues to rise, students are increasingly motivated to enhance their proficiency in artificial intelligence, recognizing its significant role in improving placement prospects.
Why Choose a B.Tech in Artificial Intelligence and Data Science?
Graduates with a degree from colleges offering B.Tech in Artificial Intelligence and Data science can find jobs with lucrative salaries in the fields soon after graduating.
The number of students taking this course is on the rise. Data science and artificial intelligence in Btech has emerged as one of the fastest-growing sectors in recent years.
With so many great options for jobs available today, it is one of the most challenging specializations to learn in the B.Tech CSE program.
Students may choose this course because it will help them become data scientists and analysts by preparing them for the workplace.
Syllabus for B.Tech in Artificial Intelligence and Data Science
The syllabus for B. Tech artificial intelligence and data science is listed in the table below. The same curriculum is followed by many public and private colleges. There may be variations in the electives available.
Semester I
Multivariable Calculus and Linear Algebra
Physics for Computer Science
Introduction to Data Science
Programming for Problem-Solving
Practical /Term Work / Practice Sessions/ MOOCs – Entrepreneurship, IoT and Applications, Computer-Aided Engineering Drawing
Semester II
Probability and Statistics
Engineering Chemistry
Introduction to Python Programming
Basics of Electrical and Electronics Engineering
Basics of Civil and Mechanical Engineering
Practical /Term Work / Practice Sessions/ MOOCs – Biology for Engineers, Design Thinking
Semester III
Analog and Digital Electronics
Programming in Java
Data Structures
Discrete Mathematics and Graph Theory
Agile Software Development and DevOps
Practical /Term Work / Practice Sessions/ MOOCs – Communication Skills, Indian Constitution and Professional Ethics, Universal Human Values
Semester IV
Design and Analysis of Algorithms
Unix Operating System
Database Management System
Computer Organization and Architecture
Numerical Techniques and Optimization Methods
Practical /Term Work / Practice Sessions/ MOOCs – Management Science, Environmental Science, Basics of Kannada / Advanced Kannada
Semester V
Artificial Intelligence and Applications
Neural Networks and Deep Learning
Machine Learning
Professional Elective-I – Web and Text Mining, Pattern Recognition, Security in IoT, Advanced IoT Programming, Object Oriented Concepts with C++/Java, UI/UX Design, and Data Visualization
Open Elective – I Database Management Systems
Practical /Term Work / Practice Sessions/ MOOCs – Predictive Analytics and Data Visualization Tools, Indian Tradition and Culture
Semester VI
Theory of Computation
Big Data Analytics
IoT and Cloud
Professional Elective I – Cognitive Computing, Business Intelligence, Industrial and Medical IoT, Industrial and Medical IoT, Advanced Computer Architecture, Parallel Computing, and High-Performance Computing
Open Elective II – Data Structures
Practical /Term Work / Practice Sessions/ MOOCs – Research-Based Mini Project, Mobile Application Development, Technical Documentation
Semester VII
Professional Elective V
Open Elective III
Capstone Project Phase 1
Internship/Global Certification
Semester VIII
Capstone Project Phase 2
Internship/Global Certification
MOOC / Competitive Exam
Open Elective IV
Top Books for B.Tech in AI and Data Science
Here is the list of Top books for B Tech in Artificial Intelligence and Data Science.
Python Data Science Handbook
Jake VanderPlas
Practical Statistics for Data Scientists
Peter Bruce, Andrew Bruce & Peter Gedeck
Introducing Data Science
Davy Cielen, Anro DB Meysman, Mohamed Ali
The Art of Statistics Learning from Data
David Spiegelhalter
Data Science from Scratch
Joel Grus
R for Data Science
Hadley Wickham & Garrett Grolemund
Think Stats
Allen B Downey
Introduction to Machine Learning with Python
Andreas C Muller & Sarah Guido
Data Science Job: How to Become a Data Scientist
Przemek Chojecki
Hands-on Machine Learning with Scikit-Learn and TensorFlow
Aurelien Geron
Career Opportunities After B.Tech in AI and Data Science
Graduates of this program are in high demand across various sectors, including technology, healthcare, finance, and more. Potential career paths include:
Data Scientist
Machine Learning Engineer
AI Research Scientist
Data Analyst
Business Intelligence Developer
Conclusion Pursuing a B.Tech in Artificial Intelligence and Data Science is a strategic move for students looking to enter one of the most promising fields of the future. With a curriculum that combines theoretical knowledge with practical skills, graduates are well-equipped to tackle real-world challenges. Aspiring students should understand the engineering admission process and be prepared to meet the eligibility criteria to embark on this exciting journey. The skills acquired during the program will not only enhance career prospects but also contribute to the advancement of technology in various domains.
