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Under Graduation for Working Professionals
OVERVIEW
Artificial Intelligence and Data Science, as branches of M.Tech, integrate advanced methodologies from statistics, cognitive science, computing, and information science to drive intelligent decision-making through data. These fields focus on extracting actionable insights from vast datasets, employing sophisticated techniques such as machine learning, deep learning, and big data analytics to solve complex computational and real-world challenges.
In the M.Tech program, students are equipped with the expertise to design and develop cutting-edge AI and data science-based solutions, using the latest tools and technologies. The curriculum dives deeper into advanced research areas, enabling students to innovate and create intelligent systems for industries like manufacturing, healthcare, finance, and e-commerce. Graduates acquire specialized knowledge in data visualization, data-driven decision-making, and handling large-scale data, positioning them to contribute significantly to technological advancements and industry-specific applications.
This postgraduate program is ideal for those looking to advance in careers that require high-level problem-solving skills and innovative thinking, preparing students for research, leadership roles, or specialized positions in AI and data science.
Vision and Mission
To be a leading center of excellence in Artificial Intelligence and Data Science, fostering innovation and advanced research. The program aims to produce skilled professionals who can drive the next wave of technological advancements, contributing significantly to societal and industrial transformation through data-driven insights and intelligent systems.
- To provide advanced education and training in AI and Data Science by integrating theoretical knowledge with hands-on experience using the latest tools and technologies.
- To foster innovation and research, encouraging students to address complex real-world problems by developing intelligent, data-driven solutions.
- To prepare graduates for leadership roles in various industries, enabling them to contribute to sectors like healthcare, finance, manufacturing, and e-commerce with cutting-edge AI and data science applications.
- To promote interdisciplinary learning, combining techniques from various fields such as cognitive science, machine learning, and data analytics, preparing students for the future of intelligent automation and decision-making.
PEO's and PSO's
- PEO 1: Work effectively in inter-disciplinary field with the knowledge of Artificial Intelligence and Machine Learning to develop solutions to the real-world problems.
- PEO 2: To communicate and work effectively on team based engineering projects and will practice the ethics of their profession consistent with a sense of social responsibility.
- PEO 3: Excel as socially committed engineers or entrepreneurs with high ethical and moral values.
- PSO-1 Apply fundamental concepts of Data Sciences, Artificial Intelligence and Machine Learning to solve multidisciplinary engineering problems.
- PSO-2 To communicate and work effectively on team based engineering projects and will practice the ethics of their profession.
Faculty Details
S.NO | Faculty Name | Designation | Qualification | UID | Profile |
1 | View Profile |
Courses
I B.Tech I Semester | |
Theory | Practical |
Mathematics-I Question Bank | English Language Communication Skills Lab |
Engineering Chemistry Question Bank | Engineering Workshop |
Engineering Physics-I Question Bank | |
Professional Communication in English Question Bank | |
Engineering Mechanics Question Bank | |
Basic Electrical and Electronics Engineering Question Bank |
I B.Tech I Semester | |
Theory | Practical |
Mathematics-I Question Bank | English Language Communication Skills Lab |
Engineering Chemistry Question Bank | Engineering Workshop |
Engineering Physics-I Question Bank | |
Professional Communication in English Question Bank | |
Engineering Mechanics Question Bank | |
Basic Electrical and Electronics Engineering Question Bank |
II B.Tech I Semester | |
Theory | Practical |
Mathematics-4 Question Bank | OOPS through JAVA |
Mathematical Foundations of Computer Science Question Bank | Data Structures through c++ Lab |
Data Structures through c++ Question Bank | ITWS |
Digital Logic Design Question Bank | |
OOPS through JAVA Question Bank | |
Environmental Science and Technology Question Bank |
II B.Tech II Semester | |
Theory | Practical |
Computer Organisation Question Bank | Computer Organisation Lab |
Database Management Systems Question Bank | Database Management Systems Lab |
Operating Systems Question Bank | Operating Systems Lab |
Business Economics and Financial Analysis Question Bank | Gender Sensatization Lab |
Formal Languages and Automata Theory Question Bank |
III B.Tech I Semester | |
Theory | Practical |
Design and Analysis of Algorithms Question Bank | Design and Analysis Lab |
Data Communications and Computer Networks Question Bank | Software Engineering Lab |
Software Engineering Question Bank | Computer Networks LAb |
Fundamentals of Management Question Bank | |
Open Elective-1 PEC Question Bank | |
Professional Ethics |
III B.Tech II Semester | |
Theory | Practical |
Compiler Design Question Bank | Web Technologies Lab |
Web Technologies Question Bank | Advanced English Communication Skills Lab |
Cryptography and Network Security Question Bank | Cryptography and Network Security Lab |
Open Elective II Question Bank | |
Professional Elective 1 Question Bank |
IV B.Tech II Semester | |
Theory | Practical |
Management Science Question Bank | Industry Oriented Mini Project |
Adhoc and Sensor Networks(Elective – III) Question Bank | Seminar |
Semantic Web and Social Networks (Elective – IV) Question Bank | Project Work |
Comprehensive Viva |
Electives | |
Open Elective Disaster Management Intellectual Property Rights Human Values and Professional Ethics | Elective – I Software Project Management Image Processing and Pattern Recognition Mobile Computing Computer Graphics Operations Research |
Elective – II Machine Learning Soft Computing Information Retrieval Systems Artificial Intelligence Computer Forensics | Elective – III Adhoc and Sensor Networks Storage Area Networks Database Security Embedded Systems |
Elective – IV Web Services Semantic Web and Social Networks Scripting Languages Multimedia and Rich Internet Applications | Professional Elective- I Mobile Computing Design patterns Artificial Intelligence Information security Management(Security Analyst1) Introduction to Analytics(Associate Analytics-1) |
Syllabus
E - Resources
Projects
Events
ACADEMIC CALENDAR 2024-25
Academic Excellence
Academic Year | S.No. | H.T. No. | Name of the Student | Branch | Section | % |
2022-23 | 1 | 22BK1A72A0 | S. Archana | AI&DS | A | 89.68 |
2 | 22BK1A7259 | K. Viswakarma | A | 89.49 | ||
3 | 22BK1A7268 | M.Ignatius Anto Jennifer | A | 89.49 | ||
2021-22 | 1 | 21BK1A7215 | GANDI PRIYANKA | AI&DS | A | 90.56 |
2 | 21BK1A7207 | CHAGANTI SURYANARAYANA MURTHY | A | 87.89 | ||
3 | 21BK1A7244 | POONAM | A | 87 |
Academic Year | S.No. | H.T. No. | Name of the Student | Branch | Section | % |
2022-23 | 1 | 21BK1A7227 | K. S. Hari Kiran Vamshi | AI&DS | A | 87.68 |
2 | 21BK1A7207 | C. Surya Narayana Murthy | A | 86.26 |