The Department of Artificial Intelligence and Data Science was established in the year 2023 and it is offering UG program in Artificial Intelligence and Data Science with an intake of 30. The Department has been maintaining high standards in imparting quality education in the challenging field of Artificial Intelligence. Highly experienced and dedicated faculty members with minimum M.E / M.Tech qualification impart quality training to students, with solid emphasis on understanding the fundamentals and intricacies of the subjects concerned and subsequently apply them to solve problems. The Department has successfully conducted Technical Symposiums and has arranged a number of seminars and several invited lectures by eminent persons both from academia and industry. The Department has well established lab facilities with advanced software suited to the syllabus prescribed by the University.
The Department of Artificial Intelligence and Data Science has primary objective of providing world class education in the field of Artificial Intelligence. The department is combining excellence in education and research with reference to the industry. Right from its inception, the Department has been offering phenomenal infrastructural facilities with a variety of computing platforms to professional students to meet the burgeoning demands of the IT industry.
To provide a holistic education to students and develop them into intelligent, well informed and confident people.
To instill in them a sense of discipline, strong ethical values and groom them as good citizens of India as well as the world.
To provide our students with an education that combines rigorous academic study and the excitement of discovery.
To develop the ability and passion for each student to work wisely, creatively and effectively for the betterment of mankind.
To create an environment of intellectual stimulus, scientific inquiry and moral propriety.
Graduates can
Apply their technical competence in computer science to solve real world problems, with technical and people leadership.
Work in a business environment, exhibiting team skills, work ethics, adaptability and lifelonglearning.
Engineering Knowledge: Apply the knowledge of mathematics, science,engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
Conduct Investigations of complex problems: Use research based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions
Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate. the knowledge of, and need for sustainable development.
Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to ones own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
The Students will be able to
Exhibit design and programming skills to build and automate business solutions using cutting edge technologies.
Strong theoretical foundation leading to excellence and excitement towards research, to provide elegant solutions to complex problems.
Ability to work effectively with various engineering fields as a team to design, build and develop system applications.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: To use appropriate words in a professional context
CO2: To gain understanding of basic grammatical Structures and use them in right context.
CO3: To read and infer the denotative and connotative meanings of technical texts.
CO4: To write definitions, descriptions, narrations and essays on various topics.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Use the matrix algebra methods for solving practical problems.
CO2: Apply differential calculus tools in solving various application problems.
CO3: Able to use differential calculus ideas on several variable functions.
CO4: Apply different methods of integration in solving practical problems.
CO5: Apply multiple integral ideas in solving areas,volumes and other practical problems
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Understand the importance of mechanics.
CO2: Express their knowledge in electromagnetic waves.
CO3: Demonstrate a strong foundational knowledge in oscillations, optics and lasers.
CO4: Understand the importance of quantum physics.
CO5: Comprehend and apply quantum mechanical principles Towards the formation of energy bands.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: To infer the quality of water from quality parameter data and propose suitable treatment methodologies to treat water.
CO2: To identify and apply basic concepts of nanoscience and nanotechnology in designing the synthesis of nonmaterial’s for engineering and technology applications.
CO3: To apply the knowledge of phase rule and composites for material selection requirements.
CO4: To recommend suitable fuels for engineering processes and applications.
CO5: To recognize different forms of energy resources and apply them for suitable applications in energy sectors.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Develop algorithmic solutions to simple computational problems.
CO2: Develop and execute simple Python programs.
CO3: Write simple Python programs using conditionals and loops for solving problems.
CO4: Decompose a Python program into functions.
CO5: Represent compound data using Python lists, tuples, dictionaries etc.
CO6: Represent compound data using Python lists, tuples, dictionaries etc.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: To compare and contrast products and ideas in technical texts.
CO2: To identify and report cause and effects in events, industrial processes through technical texts.
CO3: To analyses problems in order to arrive at feasible solutions and communicate them in the written Format.
CO4: To present their ideas and opinions in a planned and logical manner
CO5: To draft effective resumes in the context of job search.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Apply the concept of testing of hypothesis for small and large samples in real life problems.
CO2: Apply the basic concepts of classifications of design of experiments in the field of agriculture.
CO3: Appreciate the numerical techniques of interpolation in various intervals and apply the numerical techniques of differentiation and integration for engineering problems.
CO4: Understand the knowledge of various techniques and methods for solving first and second order ordinary differential equations.
CO5: Solve the partial and ordinary differential equations with initial and boundary conditions by using certain techniques with engineering applications.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Design various combinational digital circuits using logic gates.
CO2: Design sequential circuits and analyze the design procedures.
CO3: State the fundamentals of computer systems and analyze the execution of an instruction.
CO4: Analyze different types of control design and identify hazards.
CO5: Identify the characteristics of various memory systems and I/O communication.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Define the data science process.
CO2: Understand different types of data description for data science process.
CO3: Gain knowledge on relationships between data.
CO4: Use the Python Libraries for Data Wrangling.
CO5: Apply visualization Libraries in Python to interpret and explore data.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Define the data science process.
CO2: Understand different types of data description for data science process.
CO3: Gain knowledge on relationships between data.
CO4: Use the Python Libraries for Data Wrangling.
CO5: Apply visualization Libraries in Python to interpret and explore data.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Define linear and non-linear data structures.
CO2: Implement linear and non–linear data structure operations.
CO3: Use appropriate linear/non–linear data structure operations for solving a given problem.
CO4: Apply appropriate graph algorithms for graph applications.
CO5: Analyze the various searching and sorting algorithms.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Apply the concepts of classes and objects to solve simple problems.
CO2: Develop programs using inheritance, packages and interfaces.
CO3: Make use of exception handling mechanisms and multithreaded model to solve real world problems.
CO4: Build Java applications with I/O packages, string classes, Collections and generics concepts.
CO5: Integrate the concepts of event handling and JavaFX components and controls for developing GUI based applications.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Implement linear data structure algorithms.
CO2: Implement applications using Stacks and Linked list.
CO3: Implement Binary Search tree and AVL tree operations.
CO4: Implement graph algorithms.
CO5: Analyze the various searching and sorting algorithms.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Design and develop java programs using object oriented programming concepts.
CO2: Develop simple applications using object oriented concepts such as package, exceptions.
CO3: Implement multithreading, and generics concepts.
CO4: Create GUIs and event driven programming applications for real world problems.
CO5: Implement and deploy web applications using Java.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Make use of the python libraries for data science.
CO2:Make use of the basic Statistical and Probability measures for data science.
CO3: Perform descriptive analytics on the benchmark data sets.
CO4: Perform correlation and regression analytics on standard data sets.
CO5: Present and interpret data using visualization packages in Python.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Use MS Word to create quality documents, by structuring and organizing content for their day to day technical and academic requirements
CO2:Use MS EXCEL to perform data operations and analytics, record, retrieve data as per requirements and visualize data for ease of understanding.
CO3: Use MS PowerPoint to create high quality academic presentations by including common tables, charts, graphs, interlinking other elements, and using media objects.
CO4: Perform correlation and regression analytics on standard data sets.
CO5: Present and interpret data using visualization packages in Python.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Construct automata theory using Finite Automata.
CO2:Write regular expressions for any pattern.
CO3: Design context free grammar and Pushdown Automata.
CO4: Design Turing machine for computational functions.
CO5: Differentiate between decidable and undecidable problems.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Use appropriate search algorithms for problem solving.
CO2: Apply reasoning under uncertainty.
CO3: Build supervised learning models.
CO4: Build assembling and unsupervised models.
CO5: Build deep learning neural network models.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Construct SQL Queries using relational algebra.
CO2: Design database using ER model and normalize the database.
CO3: Construct queries to handle transaction processing and maintain consistency of the database.
CO4: Compare and contrast various indexing strategies and apply the knowledge to tune the performance of the database.
CO5: Appraise how advanced databases differ from Relational Databases and find a suitable database for the given requirement.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Analyze the efficiency of algorithms using various frameworks.
CO2: Apply graph algorithms to solve problems and analyze their efficiency.
CO3: Make use of algorithm design techniques like divide and conquer, dynamic programming and greedy techniques to solve problems.
CO4: Use the state space tree method for solving problems.
CO5: Solve problems using approximation algorithms and randomized algorithms.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Analyze various scheduling algorithms and process synchronization.
CO2: Explain deadlock prevention and avoidance algorithms.
CO3: Compare and contrast various memory management schemes.
CO4: Explain the functionality of file systems, I/O systems, and Virtualization.
CO5: Compare iOS and Android Operating Systems.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: To recognize and understand the functions of environment, ecosystems and biodiversity and their conservation.
CO2: To identify the causes, effects of environmental pollution and natural disasters and contribute to the preventive measures in the society.
CO3: To identify and apply the understanding of renewable and non-renewable resources and contribute to the sustainable measures to preserve them for future generations.
CO4: To recognize the different goals of sustainable development and apply them for suitable technological advancement and societal development.
CO5: To demonstrate the knowledge of sustainability practices and identify green materials, energy cycles and the role of sustainable urbanization.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Define and implement UNIX Commands.
CO2: Compare the performance of various CPU Scheduling Algorithms.
CO3: Compare and contrast various Memory Allocation Methods.
CO4: Define File Organization and File Allocation Strategies.
CO5: Implement various Disk Scheduling Algorithms.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Create databases with different types of key constraints.
CO2: Construct simple and complex SQL queries using DML and DCL commands.
CO3: Use advanced features such as stored procedures and triggers and incorporate in GUI based application development.
CO4: Create an XML database and validate with meta-data (XML schema).
CO5: Create and manipulate data using NOSQL database.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Explain the basic layers and its functions in computer networks.
CO2: Understand the basics of how data flows from one node to another.
CO3: Analyze routing algorithms.
CO4: Describe protocols for various functions in the network.
CO5: Analyze the working of various application layer protocols.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Understand the techniques in different phases of a compiler.
CO2: Design a lexical analyzer for a sample language and learn to use the LEX tool.
CO3: Apply different parsing algorithms to develop a parser and learn to use YACC tool.
CO4: Understand semantics rules (SDT), intermediate code generation and run-time environment.
CO5: Implement code generation and apply code optimization techniques.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Understand the fundamentals of networks security, security architecture, threats and vulnerabilities.
CO2: Apply the different cryptographic operations of symmetric cryptographic algorithms .
CO3: Apply the different cryptographic operations of public key cryptography.
CO4: Apply the various Authentication schemes to simulate different applications.
CO5: Understand various cyber crimes and cyber security.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Explain the foundations of distributed systems (K2).
CO2: Solve synchronization and state consistency problems (K3).
CO3: Use resource sharing techniques in distributed systems (K3).
CO4: Apply working model of consensus and reliability of distributed Systems (K3).
CO5: Explain the fundamentals of cloud computing (K2).
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: Classify the encryption techniques.
CO2: Illustrate the key management technique and authentication.
CO3: Evaluate the security techniques applied to network and transport Layer.
CO4: Discuss the application layer security standards.
CO5: Apply security practices for real time applications.
COURSE OUTCOMES:
At the end of the course, learners will be able
CO1: To examine and explore the role and importance of digital marketing in today’s rapidly changing business environment.
CO2: To focuses on how digital marketing can be utilized by organizations and how its effectiveness can be measured.
CO3: To know the key elements of a digital marketing strategy.
CO4: To study how the effectiveness of a digital marketing campaign can be measured.
CO5: To demonstrate advanced practical skills in common digital marketing tools such as SEO, SEM, Social media and Blogs.