Subjects of B.Tech in Artificial Intelligence and Data Science
The curriculum adopted by B.Tech in Artificial Intelligence 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.
The goal of the artificial intelligence and data science course curriculum is to teach students how to use networks, algorithms, Data analysis and programming techniques to build algorithms that can solve problems like a human. This is accomplished through studying topics related to artificial intelligence and data science such as software engineering, Data structure and algorithms, programming languages, database management, data mining, data warehouse, scripting language, machine learning, big data analytics, etc.
A unique selection of artificial intelligence and data science courses are offered by best colleges for B.Tech in Artificial Intelligence and Data Science. Popular degrees in artificial intelligence and data science in India include B. Tech Artificial Intelligence & Data Science and M. Tech Artificial Intelligence and data science.
Big data analytics, fuzzy technologies, and artificial neural networks are among the other topics that are addressed in this course. It helps students acquire the abilities needed to form conclusions based on data analysis.
Through AI and DS courses offered by KCC Institute of Technology and Management KCC ITM, students will gain skills in areas such as statistics, data science, machine learning, computer science, and logic.
Artificial intelligence is a component of data science. The field of data science uses artificial intelligence in its daily operations. The foundation of data science and artificial intelligence is based on machine learning.
B. Tech Artificial Intelligence and Data Science curriculum comprises 8 semesters of 6 month time period each. Through the study of topics like Data Structures & Algorithms, Software Engineering, Computer Networks, etc., the program focuses on important Artificial Intelligence and Data Science concepts. The advanced AI ideas covered in the B. Tech Artificial Intelligence and data science curriculum are explored through the study of topics like Database Systems, Cloud Computing, Big Data Analytics, Reinforcement Learning, etc.
B.Tech in Artificial Intelligence and Data Science Highlights
Course Level
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Undergraduate
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Duration
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4 years
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Eligibility
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Candidates must have passed the 12th standard in the science stream or its equivalent from a recognized board
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Average Fee
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50,000- 1,50000 yearly
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Average Salary
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4 lakh to 6 lakh annually
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Admission Criteria
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Based on merit and entrance exam
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Eligibility for admission in colleges for B.Tech in Artificial Intelligence and Data Science
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Candidates must have successfully completed the 10+2 exam or an equivalent exam from an accredited university.
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Candidates must have a minimum cumulative score of 55%.
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.
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The number of students taking this course is on the rise. Data science and artificial intelligence has emerged as one of the fastest-growing sectors in recent years.
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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.
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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 in 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
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Semester II
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Multivariable Calculus and Linear Algebra
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Probability and Statistics
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Physics for Computer Science
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Engineering Chemistry
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Introduction to Data Science
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Introduction to Python Programming
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Programming for Problem-Solving
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Basics of Electrical and Electronics Engineering
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Practical /Term Work / Practice Sessions/ MOOCs – Entrepreneurship, IoT and Applications, Computer-Aided Engineering Drawing
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Basics of Civil and Mechanical Engineering
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Practical /Term Work / Practice Sessions/ MOOCs – Biology for Engineers, Design Thinking
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Semester III
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Semester IV
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Analog and Digital Electronics
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Design and Analysis of Algorithms
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Programming in Java
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Unix Operating System
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Data Structures
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Database Management System
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Discrete Mathematics and Graph Theory
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Computer Organization and Architecture
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Agile Software Development and DevOps
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Numerical Techniques and Optimization Methods
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Practical /Term Work / Practice Sessions/ MOOCs – Communication Skills, Indian Constitution and Professional Ethics, Universal Human Values
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Practical /Term Work / Practice Sessions/ MOOCs – Management Science, Environmental Science, Basics of Kannada / Advanced Kannada
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Semester V
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Semester VI
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Artificial Intelligence and Applications
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Theory of Computation
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Neural Networks and Deep Learning
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Big Data Analytics
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Machine Learning
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IoT and Cloud
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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
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Professional Elective I – Cognitive Computing, Business Intelligence, Industrial and Medical IoT, Industrial and Medical IoT, Advanced Computer Architecture, Parallel Computing, and High-Performance Computing
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Open Elective – I Database Management Systems
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Open Elective II – Data Structures
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Practical /Term Work / Practice Sessions/ MOOCs – Predictive Analytics and Data Visualization Tools, Indian Tradition and Culture
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Practical /Term Work / Practice Sessions/ MOOCs – Research-Based Mini Project, Mobile Application Development, Technical Documentation
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Semester VII
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Semester VIII
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Professional Elective V
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Capstone Project Phase 2
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Open Elective III
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Internship/Global Certification
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Capstone Project Phase 1
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MOOC / Competitive Exam
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Internship/Global Certification
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Open Elective IV
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Some important subjects in Artificial Intelligence and Data Science
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Augmented Reality & Virtual Reality
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Cognitive Computing
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Machine Learning Techniques
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Deep Learning
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Robotic Process Automation
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Robotics
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Internet of Things
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Introduction to Data Science
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Data Visualization
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Natural Language Processing
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Geometric Modelling
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Programming for Problem-Solving
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Python Programming
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Object Oriented Programming
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Web Technology
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Computer Communication Networks
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Cryptography and Network Security
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Data Structures and Algorithms
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Database Management Systems
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Distributed Computing
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Business Analyst
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Data Analyst
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Intelligence Analyst
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Data Manager
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Information Security Analyst
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Risk Analyst
Important skills you need
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Good programming skills
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Problem-solving skills
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Knowledge of basic web development
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Critical thinking and analytical skills
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Basics of security, vulnerabilities and cryptography
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Basics of machine learning
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Strong data structure and algorithm skills
Top Colleges and Universities for B.Tech in Artificial Intelligence and Data Science
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IIT Bhilai
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IIT Jodhpur
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IIT Hyderabad
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IIIT Design and Manufacturing Kurnool, Andhra Pradesh
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IIIT Dharwad
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IIIT Lucknow
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IIIT Naya Raipur
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GGSIPU
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JMI
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DTU
Is Coding Mandatory in B.Tech in AI and DS?
Data science does really require code. That’s the reason why most B.Tech Colleges have prioritized coding and programming languages in their curriculum. Large datasets can be extracted, analyzed, and worked with using programming languages like Python, SQL, and R by data scientists. Programming is also used to apply Machine Learning models for data visualization and forecasting. Data Scientists must have a fundamental understanding of programming or coding because it is a core ability in computer science.
Top Books for B.Tech in AI and DS
Book Name
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Author
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Python Data Science Handbook
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Jake VanderPlas
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Practical Statistics for Data Scientists
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Peter Bruce, Andrew Bruce & Peter Gedeck
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Introducing Data Science
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Davy Cielen, Anro DB Meysman, Mohamed Ali
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The Art of Statistics Learning from Data
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David Spiegelhalter
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Data Science from Scratch
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Joel Grus
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R for Data Science
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Hadley Wickham & Garrett Grolemund
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Think Stats
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Allen B Downey
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Introduction to Machine Learning with Python
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Andreas C Muller & Sarah Guido
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Data Science Job: How to Become a Data Scientist
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Przemek Chojecki
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Hands-on Machine Learning with Scikit-Learn and TensorFlow
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Aurelien Geron
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