B.Tech. in Computer Science and Engineering on Data Science and Artificial Intelligence

(In Collaboration with IBM)

4 Years Degree Programme

Our Apporach

Global Education, Global Acceptance

  • Industry-Aligned Curriculum
  • Software Foundation and Programming
  • Rigorous training in cutting-edge technologies shaping the future

Industry-Academia Collaboration

Specialization

Programme Specialization

  • Data Science and Artificial Intelligence

Programme Details

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4 Years Programme

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Upto 100% Scholarship

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100% Placement Assistance

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Eligibility

Embark on an enriching journey with our B.Tech program in Computer Science and Engineering, specializing in Data Science and Artificial Intelligence. This program seamlessly integrates industry expertise, with a strategic alliance with IBM, and a robust traditional academic scheme. Students delve into a comprehensive curriculum covering Software Foundation, Programming (with C++, Python, and Java), Cloud Fundamentals, RDBMS, Data Visualization, Predictive Analytics, Data Science, and Artificial Intelligence.

  • Mastery of software development, including languages like C++.
  • Proficiency in understanding and utilizing cloud computing principles.
  • Application of predictive analytics, data science, and AI in solving complex problems.
  • Bridging the gap between academia and industry through a comprehensive curriculum.

  • Problem Analysis and Solution Formulation: Identify and solve complex problems, emphasizing adept analysis.
  • Solution Design and Evaluation: Design, evaluate solutions, aligning with IBM technologies for societal needs.
  • Computational Proficiency: Apply computing, IBM technologies, and mathematics for real-world problem-solving.
  • Research-based Investigations: Use research for investigations, ensuring valid conclusions in complex computing.
  • Environmental and Societal Impact: Understand IBM's impact, emphasizing sustainability in computing solutions.

  • Graduates will excel in utilizing a diverse range of IBM tools for software development, data management, system integration, as well as specialized tools for AI and deep learning applications.
  • Students will adeptly design, develop, and deploy applications, with a particular emphasis on harnessing IBM technologies, including AI and deep learning frameworks.
  • The program prioritizes equipping students with advanced proficiency in database management, emphasizing expertise in IBM database solutions, particularly those integral to AI and deep learning applications.

Curriculum Details

Year wise Course Details

Odd Semester

Courses for this semester

Course Overview

This course introduces topics like differential and integral calculus, matrices, vectors, and systems of linear equations. Emphasis on applications in engineering contexts, preparing students for advanced coursework and practical problem-solving in various engineering disciplines

Course Outcomes

  • Enabling solving skills of definite and improper integrals.
  • Understand the concept of calculus and linear algebra.
  • Understand the application of differential and integral calculus.
  • Understand to solve systems of linear equations and application problems requiring them.
  • Understand the concepts of matrices to solve systems of linear equations and application problems requiring them.

Course Overview

This course is to teach students the basic of pure programming and problem solving. This course provides students with a comprehensive study of the C programming language. The course emphasizes problem- solving and empirical skills through the process of designing, implementing, and executing C programs.

Course Outcomes

  • Summarize the concept of computer system, analyze a given problem, develop an algorithm, fundamental programming constructs, identify data representation formats, and describe operators and their precedence, associativity
  • Generalize branching and loop statements.
  • Summarize the concept of homogeneous derives data types, strings and functions
  • Generalize pointers and heterogeneous data types.
  • Summarize the concept of file system.

Course Overview

This course covers fundamental concepts in mechanics, electrostatics, magnetostatics, Faraday’s law, magnetics integrating physics principles into engineering applications. Emphasis on problem-solving skills, critical thinking, and real-world applications through lectures, labs, and projects. Prepares students for further studies in engineering disciplines, fostering analytical thinking and practical problem-solving abilities.

Course Outcomes

  • To compute the vectors and scalar representation of forces and nature of forces.
  • To discuss conservative and non-conservative forces, angular momentum and energy equations.
  • To compute basics of non-inertial frames, harmonic oscillator and forced oscillations.
  • Explain the usage of common electrical measuring instruments.
  • Generalize the basic characteristics of transformers and electrical machines.

Course Overview

This course provides the basic working knowledge of the production and properties of different materials used in the industry. It also explains the use of different tools, equipments, machinery and techniques of manufacturing, which ultimately facilitate shaping of these materials into various usable forms. Ingeneral, various mechanical workshops know by long training how to use workshop tools, machine tools and equipment. Trained and competent persons should be admitted to this type of mechanical works and permitted to operate equipment.

Course Outcomes

  • Develop the knowledge of carpentry, fitting, welding, drilling with respect to its application.
  • Apply the concepts of machining in practical field.
  • Observe the technical aspects involved in workmanship of various plumbing tasks along with their safety measures.
  • Provide and fix the false ceiling, aluminum –glass works.
  • Apply the concept of different finishing works.

Course Overview

Software Foundation with C++ provides a foundational understanding of programming principles using the C++ language. The course covers basic to intermediate concepts, including data types, control structures, functions, classes, and object-oriented programming (OOP) principles. Through practical exercises and projects, students will gain hands-on experience in software development, debugging, and problem-solving using C++.

Course Outcomes

  • To understand Object Oriented Programming concepts.
  • An ability to create an simple C++ Programming.
  • Implement the concept of classes and objects.
  • An ability to develop a program using any type of Inheritance.
  • To understand and develop a program using file operations.

Course Overview

This course offers flexible, accessible learning experiences covering diverse subjects. Students engage with multimedia content, lectures, quizzes, and forums. Courses often feature self-paced learning, allowing participants worldwide to acquire knowledge and skills in various fields, facilitated by leading educators and institutions.

Course Outcomes

Course Overview

This course focusses on enhancing personal and professional skills to foster holistic growth. Topics include communication skills, emotional intelligence, time management, and leadership development. Through workshops, exercises, and mentorship, participants cultivate self-awareness, confidence, and interpersonal skills essential for success in academic, professional, and personal spheres.

Course Outcomes

  • Learn to identify common errors in writing.
  • Acquire skill of report writing.
  • Develop the ability as critical readers and writers.
  • Learn to improve speaking ability in English both in terms of fluency and comprehensibility.
  • Learn to increase awareness of the correct usage of English grammar in writing and speaking.

Course Overview

This course in engineering provide opportunities for students to explore their interests outside the classroom. Activities may include engineering clubs, competitions, hackathons, and community projects. Participants gain hands-on experience, teamwork skills, and networking opportunities, enhancing their academic journey and preparing them for future career endeavors

Course Outcomes

  • The students will be engaged in different activities headed under different clubs namely dance, music, photography, drama, literacy, etc
  • The students will participate in regular club activities like workshops, competitions as per their interest and hobbies.
  • The students will be trained to represent ADTU in various inter university, state and national level competitions.
  • The students will be given a platform to earn from invited experts in their respective fields.
  • The students will get an exposure of 360 degree learning methodology considering the overall growth along with the academics.

Course Overview

This course introduces topics like differential and integral calculus, matrices, vectors, and systems of linear equations. Emphasis on applications in engineering contexts, preparing students for advanced

Course Outcomes

  • Enabling solving skills of definite and improper integrals.
  • Understand the concept of calculus and linear algebra.
  • Understand the application of differential and integral calculus.
  • Understand to solve systems of linear equations and application problems requiring them.
  • Understand the concepts of matrices to solve systems of linear equations and application problems requiring them.
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Even Semester

Courses for this semester

Course Overview

Engineering Chemistry is a foundational course designed to equip students with the fundamental principles of chemistry and their applications in engineering disciplines. The course provides a comprehensive overview of chemical concepts relevant to engineering, with a focus on understanding the behavior of materials, chemical reactions, and their implications in engineering processes and technologies. The course begins with an exploration of atomic structure, chemical bonding, and periodic trends, laying the groundwork for understanding the properties of elements and compounds. Students will examine the structure-property relationships of materials, including metals, polymers, ceramics, and composites, and learn how their chemical composition influences their mechanical, electrical, and thermal properties.

Course Outcomes

  • Analyse microscopic chemistry in terms of atomic and molecular orbitals and intermolecular forces.
  • Rationalise bulk properties and processes using thermodynamic considerations.
  • Distinguish the ranges of the electromagnetic spectrum used for exciting different molecular energy levels in various spectroscopic techniques
  • Rationalise periodic properties such as ionization potential, electronegativity, oxidation states and electronegativity.
  • List major chemical reactions that are used in the synthesis of molecules.

Course Overview

Basic Electrical and Electronics Engineering is an introductory course designed to provide students with a solid foundation in the principles and applications of electrical and electronics engineering. The course covers essential concepts, theories, and practical skills necessary for understanding and working with electrical circuits, devices, and systems. The course begins with an overview of fundamental electrical quantities such as voltage, current, resistance, and power, as well as basic circuit analysis techniques including Ohm's law, Kirchhoff's laws, and nodal and mesh analysis. Students will learn to analyze and solve simple DC and AC circuit problems, gaining proficiency in circuit simplification and analysis methods.

Course Outcomes

  • Analyse and apply basic electric and magnetic circuits.
  • Understand the working principles of electrical machines and power converters.
  • Understand the components of low-voltage electrical installations.
  • Understand the usage of common electrical measuring instruments.
  • Understand the working and basic characteristics of transformers and electrical machines.

Course Overview

This course offers designing and developing applications involving Object Oriented Programming concepts such as inheritance, association, aggregation, composition, polymorphism, abstract classes and interfaces using Java. It also covers designing and building multi-threaded Java Applications etc.

Course Outcomes

  • Understand object-oriented programming concepts and implement in java.
  • Understand and apply building blocks of OOPs language, inheritance, package and interfaces, and analyse real-world problems in terms of these.
  • Unserdtand and apply concepts like multithreading, exception handling etc. in object-oriented programs.
  • Apply exception handling methods in programming.
  • Create interactive as well as GUI-based java applications in project-based learning.

Course Overview

This course will lead students through the fundamental concept of Data science using python where Students will be introduced to the Data science visualization data, feature selection, machine learning algorithms.

Course Outcomes

  • Understand about the history of Programming Languages, Types of programming languages, Basic Syntax of a few programming languages.
  • Understand the fundamentals of Python programming language and its syntax, enabling them to write basic to intermediate level programs.
  • Demonstrate proficiency in using data types and conditional statements to make simple Python programs and manipulate data effectively.
  • Understand about Loops, functions and various packages in Python.
  • Understand Lambda and user defined functions in Python.

Course Overview

This course offers students with a comprehensive overview of fundamental ecological principles, environmental chemistry, biodiversity conservation, and the impacts of human activities on the environment. Through a blend of theoretical knowledge and practical applications, students delve into the complexities of pollution, resource management, and sustainable development. The course emphasizes the interconnectedness of ecological systems, aiming to cultivate an understanding of the delicate balance required for environmental harmony. By studying environmental chemistry, biodiversity, and pollution, students develop the skills needed to critically analyze and propose solutions to contemporary environmental challenges.

Course Outcomes

  • To Develop a comprehensive understanding of the Earth's environmental systems, including the atmosphere, hydrosphere, lithosphere, and biosphere. Explore the interconnectedness of these systems and how they influence ecological processes.
  • To Examine the impact of human activities on the environment, including pollution, deforestation, habitat destruction, and climate change. Understand the scientific principles underlying these environmental issues and their broader implications..
  • To Acquire proficiency in applying scientific methods to assess and address environmental problems. Develop skills in data collection, analysis, and interpretation to make informed decisions related to environmental conservation and management.
  • To Investigate principles of sustainability and explore strategies for sustainable resource management. Analyze the social, economic, and ecological dimensions of sustainable practices and identify ways to promote responsible environmental stewardship.
  • To Foster environmental literacy and awareness among students. Encourage critical thinking about environmental issues and empower students to communicate effectively about environmental challenges..

Course Overview

This course focusses on enhancing personal and professional skills to foster holistic growth. Topics include communication skills, emotional intelligence, time management, and leadership development. Through workshops, exercises, and mentorship, participants cultivate self-awareness, confidence, and interpersonal skills essential for success in academic, professional, and personal spheres.

Course Outcomes

  • Learn to identify common errors in writing.
  • Acquire skill of report writing.
  • Develop the ability as critical readers and writers.
  • Learn to improve speaking ability in English both in terms of fluency and comprehensibility.
  • Learn to increase awareness of the correct usage of English grammar in writing and speaking.

Course Overview

This course offers hands-on experience with essential networking concepts and technologies. Students configure and troubleshoot networks, practice with routers, switches, and network protocols. Emphasis on practical skills development, including network setup, security measures, and troubleshooting techniques, preparing students for entry-level positions in networking roles.

Course Outcomes

  • By the end of this unit, students will be able to identify and describe various network components, transmission mediums, and basic network management principles.
  • At the end of this unit, students will be proficient in explaining data transmission modes, communication protocols, and the concept of packet filtering with different types of filters.
  • After completing this unit, students will be able to understand and apply concepts related to network interface cards, cabling, and modern communication infrastructures.
  • Following this unit, students will be equipped to differentiate between various types of internet connections, understand the role of ISPs, and comprehend internet protocols.
  • Upon finishing this unit, students will have gained knowledge of various internet services, communication tools, and different types of networks including VPNs, intranets, and extranets.

Course Overview

This course is designed to make high-quality learning resources accessible to the students. They continue to evolve, incorporating innovative technologies and pedagogical approaches to enhance the learning experience. MOOCs provide certification options for those who successfully complete the course requirements.

Course Outcomes

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Odd Semester

Courses for this semester

Course Overview

This course serves as an essential foundation in the study of calculus and its application in solving ordinary differential equations (ODEs). Through a comprehensive exploration of both theory and practical problem-solving techniques, students will develop a deep understanding of the principles underlying differentiation and the solution methods for various types of ODEs. The Differential Calculus segment of the course will begin by revisiting the fundamental concepts of limits, continuity, and derivatives. Students will delve into the rules of differentiation, including the power rule, product rule, quotient rule, and chain rule. Emphasis will be placed on understanding the geometric interpretation of derivatives and their applications in various fields such as physics, engineering, and economics.

Course Outcomes

  • Understand Boolean algebra, analyze digital logic families, demonstrate IC interfacing.
  • Design logic functions, implement digital circuits, showcase MSI chip expertise.
  • Analyze, design sequential circuits, demonstrate flip-flop and counter proficiency.
  • Evaluate digital-to-analog converters, understand quantization, A/D converter types.
  • Analyze semiconductor memories, demonstrate proficiency in memory technology and PLDs.

Course Overview

The course aims to provide students with a basic foundation in Python programming and problem-solving skills. Students will learn to write efficient, structured, and modular code to solve a few basic real-world computational problems.

Course Outcomes

  • Understand about the history of Programming Languages, Types of programming languages, Basic Syntax of a few programming languages.
  • Understand the fundamentals of Python programming language and its syntax, enabling them to write basic to intermediate level programs.
  • Demonstrate proficiency in using data types and conditional statements to make simple Python programs and manipulate data effectively.
  • Understand about Loops, functions and various packages in Python.
  • Understand Lambda and user defined functions in Python.

Course Overview

Data Structures are the main part of many computer science algorithms as they enable the programmers to handle the data in an efficient way. It plays a vital role in enhancing the performance of a software or a program as the main function of the software is to store and retrieve the user's data as fast as possible.

Course Outcomes

  • Analyze the algorithms to determine the time and computation complexity and justify the correctness
  • Analyze the problem of stacks, queues and linked list to determine the time and computation complexity.
  • Summarize Selection Sort, Bubble Sort, Insertion Sort, Quick Sort, Merge Sort, Heap Sort and compare their performance in term of Space and Time complexity.
  • Implement basic tree-based traversal and search algorithms, learn about applications using tree
  • Graph search and traversal algorithms and determine the time and computation complexity

Course Overview

This course offers designing and developing applications involving Object Oriented Programming concepts such as inheritance, association, aggregation, composition, polymorphism, abstract classes and interfaces using Java. It also covers designing and building multi-threaded Java Applications etc

Course Outcomes

  • Students will be able to understand object-oriented programming concepts and implement in java.
  • Students will be able to comprehend building blocks of OOPs language, inheritance, package and interfaces, and analyse real-world problems in terms of these.
  • Students will be able to identify exception handling methods.
  • Students will be able to develop interactive as well as GUI-based java applications in project-based learning.

Course Overview

The course begins with an introduction to the IBM Cloud platform which covers topics such as data center locations and configuring identity and access management. You will discover the various Infrastructure-as-a-Service (IaaS) options available on IBM Cloud. Next, you will learn about the deployment options on IBM Cloud; this includes topics such as Containers, Kubernetes, and OpenShift. You will also become familiar with IBM Cloud services such as Databases, Artificial Intelligence and Watson, Blockchain, Internet of Things, and many others. In addition to videos, you will also see demos of various IBM Cloud features and services in action, as well as perform hands-on labs to gain practical experience with IBM Cloud at no charge.

Course Outcomes

  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express
  • Student will able to describe Cloud Computing
  • Student will able to explore Web Development with HTML5, CSS3, and JavaScript
  • Student will able to explore Cloud Native, DevOps, Agile, and NoSQL
  • Student will able to Develope Front End Apps with React
  • Student will able to Develope Back-end Application Development with Node.js and Express

Course Overview

Probability and Statistics is a foundational course designed to equip students with essential skills in analyzing data, making predictions, and understanding uncertainty in various real-world scenarios. This course blends theoretical concepts with practical applications to provide students with a comprehensive understanding of probability theory and statistical methods.

Course Outcomes

  • Understand the basic probability concepts and random variables that have numerous applications in computer science.
  • Apply the concept of distribution functions in web data and traffic network modeling in computer science engineering.
  • Analyze statistics and its applications in simulation, data mining and reliability theory.
  • Determine the process constructing linear and non-linear curves through the method of least square and understand its usage in binary mixtures.
  • Identify the concept of statistical quality control in computer science and mechanical engineering.

Course Overview

This course is designed to make high-quality learning resources accessible to the students. They continue to evolve, incorporating innovative technologies and pedagogical approaches to enhance the learning experience. MOOCs provide certification options for those who successfully complete the course requirements.

Course Outcomes

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Even Semester

Courses for this semester

Course Overview

Graph Theory, in discrete mathematics, is the study of the graph. A graph is determined as a mathematical structure that represents a particular function by connecting a set of points. It is used to create a pairwise relationship between objects.

Course Outcomes

  • Apply the Propositional Calculus and Predicate
  • Employ Set Theory and Group Theory concepts
  • Apply Combinatorics and Number Theory
  • Solve different Recurrence Relations
  • Construct Graphs using Graph Theory

Course Overview

A database management system (DBMS) course teaches the core principles and techniques for designing and implementing database systems.

Course Outcomes

  • Understand the basic concepts of database management systems
  • Apply SQL to find solutions to a broad range of queries
  • Understand the relational database design principles
  • Apply normalization techniques to improve database design
  • Analyze a given database application scenario to use ER model for conceptual design of the database

Course Overview

An operating systems course introduces students to the techniques used to implement operating systems and related systems software.

Course Outcomes

  • Understands the different services provided by Operating System at different level.
  • Will be able to control access to a computer and the files that may be shared.
  • Demonstrate the knowledge of the components of computer and their respective roles in computing.
  • Ability to recognize and resolve user problems with standard operating environments.
  • Gain practical knowledge of how programming languages, operating systems, and architectures interact and how to use each effectively.

Course Overview

Software engineering courses teach students how to design, develop, test, and maintain software applications using engineering principles and programming languages.

Course Outcomes

  • How to apply the software engineering lifecycle by demonstrating competence in communication, planning, analysis, design, construction, and deployment
  • An ability to work in one or more significant application domains
  • Work as an individual and as part of a multidisciplinary team to develop and deliver quality software
  • Demonstrate an understanding of and apply current theories, models, and techniques that provide a basis for the software lifecycle
  • Demonstrate an ability to use the techniques and tools necessary for engineering practice

Course Overview

Design thinking is a project-based learning model that uses a creative, systematic approach to problem-solving. Design thinking courses teach students how to use design thinking principles to solve problems in a creative and innovative way.

Course Outcomes

  • Have an awareness of how design thinking can be applied in a wide range of contexts, from the personal to the global
  • Investigate and think creatively about design problems and opportunities
  • Initiate an attitude of playfulness to aid design thinking
  • Develop visual literacy and articulacy to explain design decisions
  • Use computing tools and online environments to aid design thinking

Course Overview

A biology for engineers course aims to teach students basic concepts and techniques in probability and statistics in relation to engineering applications.

Course Outcomes

  • Understand basic biological principles and organizational structure of living systems at molecular level.
  • Comprehend basic biological principles and organizational structure of living systems at cellular level.
  • Know Energy transformations and information processing in biological systems.
  • Appreciate biological process with engineering perspective.
  • Impart knowledge about the common corridors of biology and engineering and biologically inspired technologies.

Course Overview

Artificial intelligence (AI) courses cover topics such as machine learning, deep learning, natural language processing, computer vision, robotics, and data analytics. They also teach students how to build intelligent systems that mimic human behavior using theories, standards, methods, and innovations from various domains.

Course Outcomes

  • Define and explain the fundamental concepts and subfields of AI.
  • Identify real-world applications of AI across various industries.
  • Analyze the ethical, social, and economic implications of AI.
  • Recognize the potential of AI to drive innovation and transformation in different domains.
  • Gain insights into the ethical, social, and economic implications of AI.

Course Overview

THIS COURSE WILL EQUIP STUDENTS WITH SKILLS TO COLLECT, CLEAN, AND ANALYZE DATA FROM DIVERSE SOURCES, WHILE MASTERING TECHNIQUES TO VISUALLY REPRESENT INSIGHTS AND TRENDS, ENABLING INFORMED DECISION-MAKING IN VARIOUS DOMAINS.

Course Outcomes

  • Know the history of data visualization and its connection with computer graphics
  • Understand the visualization pipeline with its relationship to other data analysis pipelines
  • Now the definition(s) of the visualization and interpretations of the notion
  • Know categories of visualization and application areas
  • Understand the foundations and characteristics of data, which forms the beginning of the visualization pipeline

Course Overview

This course to provide students with the latest Internet technologies knowledge and abilities to design and implement client and server side web programs using modern development environments.

Course Outcomes

  • Student will be able to understand network communication using the layered concept, Open System Interconnect (OSI) and the Internet Model.
  • Student will be able to understand various types of transmission media, network devices; and parameters of evaluation of performance for each media and device.
  • Students will be able to understand the concept of flow control, error control, and LAN protocols; to explain the design of, and algorithms used in, the physical, data link layers.
  • Student will understand the working principles of LAN and the concepts behind physical and logical addressing, subnetting and supernetting.
  • Student shall understand the functions performed by a Network Management System and to analyze connection establishment and congestion control with respect to TCP Protocol.

Course Overview

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Course Outcomes

  • Apply foundational acclimatization principles to adapt effectively in diverse environments and situations.
  • Apply foundational acclimatization principles to adapt effectively in diverse environments and situations.
  • Evaluate personal adaptability through practical exercises, fostering a proactive approach to acclimatization challenges.
  • Synthesize acclimatization strategies for varying contexts, demonstrating creativity and flexibility in response.
  • Demonstrate mastery in acclimatization techniques, utilizing critical thinking to address unforeseen challenges.

Course Overview

This course focusses on enhancing personal and professional skills to foster holistic growth. Topics include communication skills, emotional intelligence, time management, and leadership development. Through workshops, exercises, and mentorship, participants cultivate self-awareness, confidence, and interpersonal skills essential for success in academic, professional, and personal spheres.

Course Outcomes

  • Construct coherent and concise technical reports, demonstrating advanced written communication skills in English.
  • Apply effective verbal communication strategies in professional settings, such as meetings and presentations.
  • Evaluate and analyze complex technical documents, showcasing a high level of English language comprehension.
  • Demonstrate proficiency in using appropriate corporate English vocabulary and language conventions in engineering contexts.
  • Synthesize and communicate engineering concepts clearly and persuasively in English, fostering effective collaboration in a corporate environment.

Course Overview

This course focusses on enhancing personal and professional skills to foster holistic growth. Topics include communication skills, emotional intelligence, time management, and leadership development. Through workshops, exercises, and mentorship, participants cultivate self-awareness, confidence, and interpersonal skills essential for success in academic, professional, and personal spheres.

Course Outcomes

  • Construct coherent and concise technical reports, demonstrating advanced written communication skills in English.
  • Apply effective verbal communication strategies in professional settings, such as meetings and presentations.
  • Evaluate and analyze complex technical documents, showcasing a high level of English language comprehension.
  • Demonstrate proficiency in using appropriate corporate English vocabulary and language conventions in engineering contexts.
  • Synthesize and communicate engineering concepts clearly and persuasively in English, fostering effective collaboration in a corporate environment.
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Odd Semester

Courses for this semester

Course Overview

This course covers the computing devices and the communication protocols. It is the study of how computers can be linked to share data and information

Course Outcomes

  • Understand fundamental underlying principles of computer networking
  • Understand details and functionality of layered network architecture.
  • Apply mathematical foundations to solve computational problems in computer networking
  • Analyze performance of various communication protocols.
  • Compare routing algorithms

Course Overview

This course is to familiarize the students with the basic concept of data and to work with various kinds of data and statistical data. It aims to presents a perfect blend of machine learning, big data analytics, and statistics, the programme helps you gain experience in solving problems with real-world data

Course Outcomes

  • Understanding of Data Science Fundamentals
  • Proficiency in Data Manipulation and Cleaning
  • Ability to Perform Exploratory Data Analysis (EDA)
  • Knowledge of Statistical Modeling Techniques and Proficiency in Machine Learning Algorithms
  • Ability to Evaluate Model Performance

Course Overview

This course offers a comprehensive exploration of the dynamics that shape individual behavior, group dynamics, and organizational effectiveness within various contexts. Through a blend of theory, research, and practical applications, you'll gain valuable insights into understanding human behavior in organizations and developing strategies for effective leadership, teamwork, and organizational change.

Course Outcomes

  • Recognize and discuss the different perspectives of working culture in organizations.
  • Interpret key concepts and theories with regard to individual differences and apply these appropriately to specific situations.
  • Interpret the key concepts and theories with regard to group behaviour and apply these appropriately to specific situations.
  • Understand how organizational performance can be improved through the effective management of human resources

Course Overview

This course provides a comprehensive introduction to the principles, theories, and practices of management in modern organizations. Whether you're a budding entrepreneur, aspiring manager, or seasoned professional looking to enhance your leadership skills, this course will equip you with the knowledge and tools needed to succeed in today's dynamic business environment.

Course Outcomes

  • Demonstrate understanding of the role of managers in an organization
  • Summarize the elementary concepts, principles and theories of management
  • Examine the managerial functions having an impact on the organizational effectiveness
  • Identify the contemporary issues and challenges in management
  • Develop ethical workplace practices

Course Overview

This course provides a comprehensive introduction to neural networks, a fundamental concept in artificial intelligence and machine learning. Neural networks have become increasingly popular due to their ability to learn complex patterns and relationships from data. This course covers the theoretical foundations of neural networks as well as practical applications across various domains.

Course Outcomes

  • Learn about basic concept of artificial neuron and human brain
  • Understand the concepts different classes of neural network.
  • Learn the algorithm feed forward network.
  • To become familiar with the concepts of deep learning.
  • Understand the concept of associative memory

Course Overview

The main objective of this course is to present the scientific support in the field of information search and retrieval. This course explores the fundamental relationship between information retrieval, hypermedia architectures, and semantic models, thus deploying and testing several important retrieval models such as vector space, Boolean and query expansion. It discusses implementation and evaluation issues of new algorithms like clustering, pattern searching, and stemming with advanced data/file structures, indirectly facilitating a platform to implement comprehensive catalogue of information search tools while designing an e-commerce web site

Course Outcomes

  • Utilize the information retrieval models
  • Acquire knowledge on pre-processing of web page
  • Organize data from semantic web
  • Operate on various search engine systems
  • Implement different techniques of recommender system

Course Overview

This course gives an overview of Big Data, i.e. storage, retrieval and processing of big data. In addition, it also focuses on the “technologies”, i.e., the tools/algorithms that are available for storage, processing of Big Data. It also helps a student to perform a variety of “analytics” on different data sets and to arrive at positive conclusions.

Course Outcomes

  • Understand Big Data and its analytics in the real world
  • Analyze the Big Data framework like Hadoop and NOSQL to efficiently store and process Big Data to generate analytics
  • Design of Algorithms to solve Data Intensive Problems using Map Reduce Paradigm
  • Design and Implementation of Big Data Analytics using pig and spark to solve data intensive problems and to generate analytics
  • Implement Big Data Activities using Hive

Course Overview

This course is to introduce basic concepts and applications of soft computing tools such as neural networks, fuzzy logic systems, and several optimization techniques like genetic algorithms, evolutionary computation, simulated annealing etc. Also it covers soft computing based solutions for real-world problems.

Course Outcomes

  • Learn about soft computing techniques and their applications
  • Analyze various neural network architectures
  • Understand perceptrons and counter propagation networks
  • Define the fuzzy systems
  • Analyze the genetic algorithms and their applications

Course Overview

This course provides a comprehensive introduction to the principles, algorithms, and applications of machine learning. Students will learn both the theoretical foundations and practical skills necessary to apply machine learning techniques to real-world problems across various domains.

Course Outcomes

  • Understanding of Machine Learning Fundamentals
  • Proficiency in Machine Learning Algorithms
  • Data Preprocessing and Feature Engineering
  • understand how to evaluate the performance of machine learning models using appropriate evaluation metrics and techniques
  • Understand the concepts of Genetic algorithm and Reinforcement Learning.

Course Overview

This course provides an in-depth exploration of the principles, methodologies, and applications of expert systems. Students will learn how to design, develop, and evaluate expert systems using knowledge representation techniques, inference mechanisms, and real-world case studies

Course Outcomes

  • Understanding of Expert Systems Fundamentals and Knowledge Representation Techniques
  • Inference Mechanisms and Reasoning and explore different inference mechanisms used in expert systems, including forward chaining, backward chaining, rule-based reasoning, and uncertainty handling techniques.
  • Designing, developing, and deploying expert systems using expert system development tools, languages, and platforms
  • Evaluation and Validation of Expert Systems
  • Critical thinking skills and decision-making abilities by analyzing and assessing the effectiveness of expert systems, identifying strengths, weaknesses, and areas for improvement, and making informed decisions about their deployment and usage.

Course Overview

This course covers the basic concepts of recommender systems, including personalization algorithms, evaluation tools, and user experiences. This course will discuss how recommender systems and user models are deployed in e-commerce sites, social networks, and many other online systems, with readings from current and past research in the field.

Course Outcomes

  • To understand basic techniques and problems in the field of recommender systems
  • Evaluate Types of recommender systems: non-personalized, content based, collaborative filtering
  • Apply algorithms and techniques to develop Recommender Systems that are widely used in the Internet industry
  • To develop state-of-the-art recommender systems

Course Overview

This course covers data analysis, machine learning, artificial intelligence, and statistical models. The course emphasizes the practical aspects of analyzing the predictive performance of the algorithms.

Course Outcomes

  • Comprehensive understanding of various predictive modeling techniques, including linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and ensemble methods.
  • Learning about the preprocessing of raw data to prepare it for predictive modeling.
  • Hands-on experience with techniques for evaluating the performance of predictive models, such as cross-validation, confusion matrices, ROC curves, precision-recall curves, and metrics like accuracy, precision, recall, F1 score, and AUC-ROC.
  • Knowledge to deploy predictive models into production environments, including considerations such as scalability, performance, monitoring, and maintenance
  • Working on practical projects that involve applying predictive analytics techniques to real-world datasets and problems across various domains, such as healthcare, finance, marketing, e-commerce, and cybersecurity

Course Overview

Introduction to Cyber Security is a foundational course aimed at familiarizing individuals with the fundamental concepts, principles, and practices of safeguarding digital assets and information systems from cyber threats. In this course, participants typically learn about various aspects of cybersecurity

Course Outcomes

  • Apply a solid foundation in digital security and measures taken to protect device from threats.
  • Learning access control mechanism and understand how to protect servers
  • Understand the importance of a network basics and brief introduction on security of network protocols
  • To understand cyber-attacks and learn data privacy issues and preventive measures

Course Overview

Platforms and Systems Security is a course designed to provide students with a deep understanding of security principles, techniques, and best practices for securing various computing platforms and systems. The course covers security aspects related to operating systems, networks, virtualization, cloud platforms, and mobile devices. Students will learn about common vulnerabilities, attack vectors, defense mechanisms, and strategies for mitigating security risks in modern computing environments.

Course Outcomes

  • Understanding of Security Principles, concepts, and best practices related to computer platforms and systems
  • Ability to Identify and Mitigate Security Risks
  • Proficiency in Security Configuration and Management
  • Understanding of Compliance and Regulatory Requirements

Course Overview

Social Network Security is a specialized course focusing on the security challenges, threats, and countermeasures associated with online social networks (OSNs) and social media platforms. The course explores various aspects of security and privacy in social networking environments, including authentication, access control, data privacy, trust management, and threat mitigation strategies. Students will learn about the unique vulnerabilities and risks inherent in social networks and develop skills to protect users, data, and systems from security breaches and attacks.

Course Outcomes

  • demonstrate proficiency and understanding of social networks for business and professional use
  • demonstrate proficiency the use of social network analysis and social network developer tools
  • demonstrate proficiency and understanding of public sector media and privacy
  • demonstrate proficiency in understanding concepts in social networking and utilizing these concepts for solving real-world social network issues.

Course Overview

This course is designed to make high-quality learning resources accessible to the students. They continue to evolve, incorporating innovative technologies and pedagogical approaches to enhance the learning experience. MOOCs provide certification options for those who successfully complete the course requirements.

Course Outcomes

Course Overview

This course is designed to make high-quality learning resources accessible to the students. They continue to evolve, incorporating innovative technologies and pedagogical approaches to enhance the learning experience. MOOCs provide certification options for those who successfully complete the course requirements.

Course Outcomes

Course Overview

This course is designed to make high-quality learning resources accessible to the students. They continue to evolve, incorporating innovative technologies and pedagogical approaches to enhance the learning experience. MOOCs provide certification options for those who successfully complete the course requirements.

Course Outcomes

Course Overview

This course presents how to install, maintain, and upgrade networking systems

Course Outcomes

  • Summarize basic principles of IPv4 and its Addressing mechanisms
  • Understand UDP Services and Applications in Transport Layer
  • Describe the services, and features of TCP
  • Discuss various Flow , Error and Congestion control mechanisms of TCP
  • Understand the Principles of IPv6 Addressing ,IPv6 and ICMPv6 ProtocolS

Course Overview

This course focusses on enhancing personal and professional skills to foster holistic growth. Topics include communication skills, emotional intelligence, time management, and leadership development. Through workshops, exercises, and mentorship, participants cultivate self-awareness, confidence, and interpersonal skills essential for success in academic, professional, and personal spheres.

Course Outcomes

  • Construct coherent and concise technical reports, demonstrating advanced written communication skills in English.
  • Apply effective verbal communication strategies in professional settings, such as meetings and presentations.
  • Evaluate and analyze complex technical documents, showcasing a high level of English language comprehension.
  • Demonstrate proficiency in using appropriate corporate English vocabulary and language conventions in engineering contexts.
  • Synthesize and communicate engineering concepts clearly and persuasively in English, fostering effective collaboration in a corporate environment.

Course Overview

It is to develop the social and soft skills and to promote a holistic development of the learners.

Course Outcomes

  • The students will be engaged in different activities headed under different clubs namely dance, music, photography, drama, literacy, etc
  • The students will participate in regular club activities like workshops, competitions as per their interest and hobbies.
  • The students will be trained to represent ADTU in various inter university, state and national level competitions.
  • The students will be given a platform to earn from invited experts in their respective fields.
  • The students will get an exposure of 360 degree learning methodology considering the overall growth along with the academics.

Course Overview

This course aims to attract students interested in advancing their skills in cloud computing with AWS and obtaining a globally recognized certification.

Course Outcomes

  • Understand economic aspects, cost-benefit, and financial implications in IT outsourcing to the cloud.
  • Evaluate hypervisors in CPU resource allocation, showcasing analytical virtualization skills.
  • Interpret the Web's client-server architecture, showcasing understanding of web structures.
  • Analyze Quality of Service (QoS) technologies, demonstrating evaluative skills in networking
  • Assess cloud security, justify security mechanisms, exhibit critical analysis in cybersecurity.
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Even Semester

Courses for this semester

Course Overview

Web technologies course cover the basics of building and maintaining websites, including HTML, CSS, and JavaScript. They help students gain the skills needed to create engaging web experiences and pursue a career in web development.

Course Outcomes

  • Analyze a web page and identify its elements and attributes.
  • Create web pages using XHTML and Cascading Style Sheets.
  • Build dynamic web pages using JavaScript (Client side programming).
  • Understand the working of Web clients and Web servers.
  • Apply Web Server side technologies and be able to do Web hosting.

Course Overview

This course presents several basic ideas in automata theory and formal languages, such as grammar, finite automaton, regular expression, formal language, pushdown automaton, and Turing machine. This course not only contains the fundamental models of computation but also the building blocks of numerous subfields of computer science, such as concurrent systems, software engineering, compilers, etc.

Course Outcomes

  • Understand the fundamental characteristics of formal languages and formal grammars, showcasing foundational linguistic knowledge.
  • Recognize the similarity between deterministic and non-deterministic finite automata, demonstrating comparative analysis skills.
  • Evaluate the minimization processes of both deterministic and non-deterministic finite automata, applying critical thinking skills.
  • Analyze the resemblance between non-deterministic push-down automata and context-free grammars, demonstrating advanced understanding and synthesis of concepts.
  • Examine the fundamental characteristics of Turing machines and their computational applications, showcasing analytical skills in computing theory.

Course Overview

By the end of the course, participants will have a comprehensive understanding of how economic principles intersect with engineering disciplines, enabling them to make strategic decisions that balance technical innovation with economic efficiency and societal welfare.

Course Outcomes

  • Understand the key distinctions between macroeconomics and microeconomics, applying the laws of demand and supply.
  • Analyze economic indexes such as GNP, NNP, GDP, and NDP, along with cost concepts and break-even analysis.
  • Apply statistical measures, probability distributions, and hypothesis testing in analyzing industrial data.
  • Interpret and apply industrial laws related to industrial relations, disputes, health, safety, and compensation management.
  • Evaluate challenges in the Indian economy, including poverty alleviation programs, human capital formation, employment dynamics, and sustainable economic development.

Course Overview

By the end of the course, students will have developed a well-rounded skill set encompassing employability skills, entrepreneurial acumen, and knowledge of intellectual property rights. They will be better prepared to pursue career opportunities in various sectors, whether as employees contributing to organizational success or as entrepreneurs driving innovation and creating value through their ventures while understanding the importance of protecting their intellectual assets

Course Outcomes

  • Understand rapport with the Trainer and the subject, fostering an environment conducive to sharing doubts and queries, and engaging actively in discussions.
  • Differentiate and assess various personality traits, cultivating self-awareness through reflective practices.
  • Organize and implement strategies for maintaining hygiene, fostering grooming practices applicable in diverse environments.
  • Anticipate and manage negativity in challenging situations by implementing stress reduction techniques and cultivating self-motivation.
  • Demonstrate mastery of formal dress ethics, applying them consistently during interviews and meetings.

Course Overview

The course integrates theoretical foundations with hands-on projects, allowing students to apply deep learning techniques to analyze social network data effectively. By the end of the course, participants will be equipped with the skills to tackle diverse challenges, such as identifying fake news, predicting user behavior, and understanding information diffusion in social networks.

Course Outcomes

  • Understand Deep Learning Architectures such as feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants (e.g., LSTMs, GRUs).
  • Implement Deep Learning Models from scratch and using popular libraries like TensorFlow, Keras, and PyTorch.
  • Apply Deep Learning to Real-World Problems in areas such as computer vision (image recognition, object detection), natural language processing (text classification, machine translation), and time series analysis.
  • Optimize and Deploy Deep Learning Models, including hyperparameter tuning, regularization methods, and model compression.
  • Understand Advanced Deep Learning Concepts such as generative adversarial networks (GANs), attention mechanisms, transfer learning, and reinforcement learning.

Course Overview

This interdisciplinary approach empowers students to leverage the power of deep learning for social network analysis, driving innovation in fields like marketing, cybersecurity, and sociological research

Course Outcomes

  • Understand Network Concepts and Measures such as nodes, edges, centrality measures (e.g., degree, closeness, betweenness), and network properties (e.g., density, clustering coefficient).
  • Analyze Social Network Data through collecting, cleaning, and preparing social network data from various sources (e.g., online social networks, organizational networks, citation networks).
  • Apply Network Analysis Techniques and analyze the strengths and limitations of these techniques and their appropriate use cases.
  • Interpret Network Analysis Results in the context of real-world social systems and draw meaningful insights from network patterns, identify key actors or influencers, and understand the implications of network structures on processes like information diffusion or social contagion.
  • Understand Applications of Social Network Analysis in domains such as organizational behavior, marketing, public health, criminology, and online communities.

Course Overview

Throughout the course, practical projects and case studies provide students with opportunities to apply R programming techniques to solve real-world problems in data science and knowledge engineering domains.

Course Outcomes

  • Ability to import, clean, transform, and manipulate data from various sources using R packages like dplyr, tidyr, and readr.
  • Learning how to perform various statistical analyses in R, including hypothesis testing, regression analysis, and analysis of variance (ANOVA).
  • Gain skills in applying machine learning algorithms in R, such as linear and logistic regression, decision trees, random forests, and clustering techniques.
  • Ability to create effective data visualizations using R's powerful plotting capabilities, including static and interactive plots with packages like ggplot2, plotly, and shiny.
  • Understanding the importance of reproducible research and learn how to use R Markdown for creating reproducible reports and presentations.

Course Overview

By the end of the course, students will have gained a comprehensive understanding of knowledge engineering methodologies, empowering them to excel in diverse roles such as data analysts, data scientists, and AI engineers.

Course Outcomes

  • Gain a deep understanding of various techniques for representing knowledge in a structured format, such as semantic networks, frames, ontologies, and logic-based representations like predicate calculus and first-order logic.
  • Learn methods and tools for acquiring knowledge from different sources, including human experts, text corpora, databases, and sensor data.
  • Proficiency in designing and implementing inference mechanisms for reasoning with the acquired knowledge.
  • Gain hands-on experience with tools and software platforms used in knowledge engineering, such as ontology editors, rule engines, knowledge-based systems development environments, and machine learning libraries for knowledge extraction and inference.
  • Learning how to apply knowledge engineering techniques to solve real-world problems in various domains such as healthcare, finance, manufacturing, and natural language processing.

Course Overview

This course covers cryptographic protocols and security mechanisms to protect communication and data transmission in dynamic wireless environments, addressing challenges like authentication, confidentiality, and integrity, crucial for ensuring secure and reliable communication in ad hoc networks.

Course Outcomes

  • Learning the fundamental concepts of wireless ad hoc networks, including their architecture, routing protocols, and unique characteristics.
  • Gain knowledge of various cryptographic techniques, including symmetric and asymmetric cryptography, hash functions, and digital signatures.
  • Ability to design and implement secure routing protocols for wireless ad hoc networks, incorporating mechanisms like authentication, confidentiality, and integrity
  • Developing skills in analyzing and assessing security risks in wireless ad hoc networks, considering factors like network topology, mobility patterns, and resource constraints.
  • Gain insights into the broader challenges and future directions in securing wireless ad hoc networks, such as trust management, privacy preservation, and cross-layer security solutions.

Course Overview

This course teaches students to investigate digital devices for evidence retrieval, analyzing data integrity and recovering information, crucial for solving cybercrimes and ensuring legal compliance in digital investigations.

Course Outcomes

  • Proficiency in acquiring digital evidence from various sources including computers, mobile devices, and cloud services.
  • Develop the ability to analyze and interpret digital evidence effectively.
  • Gain specialized knowledge and skills in the forensic examination of mobile devices such as smartphones and tablets
  • Learn techniques for investigating security incidents and cybercrimes that involve networked systems.
  • Develop the ability to communicate their findings effectively through written reports and oral presentations.

Course Overview

The course will to enable students to build intelligent systems that anticipate user needs and deliver personalized, immersive experiences. Projects and hands-on exercises allow students to apply predictive models to real-world datasets.

Course Outcomes

  • Comprehensive understanding of various predictive modeling techniques, including linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and ensemble methods.
  • Learning about the preprocessing of raw data to prepare it for predictive modeling.
  • Hands-on experience with techniques for evaluating the performance of predictive models, such as cross-validation, confusion matrices, ROC curves, precision-recall curves, and metrics like accuracy, precision, recall, F1 score, and AUC-ROC.
  • Knowledge to deploy predictive models into production environments, including considerations such as scalability, performance, monitoring, and maintenance.
  • Working on practical projects that involve applying predictive analytics techniques to real-world datasets and problems across various domains, such as healthcare, finance, marketing, e-commerce, and cybersecurity .

Course Overview

By the end of the course, students will possess the skills to leverage predictive analytics and immersive technologies to drive innovation and create impactful solutions in diverse domains, from business intelligence to entertainment and beyond.Students will be able to understand AR/VR applications that leverage predictive insights.

Course Outcomes

  • Gain a solid understanding of the fundamental concepts, principles, and technologies underlying augmented reality (AR) and virtual reality (VR) systems.
  • Acquire practical skills in designing and developing AR and VR applications using industry-standard tools, frameworks, and game engines such as Unity, Unreal Engine, or ARCore/ARKit.
  • Learn principles and best practices for designing effective user experiences and intuitive interactions in AR/VR environments.
  • Investigate various applications and use cases of AR and VR technologies across diverse domains, such as gaming, education, healthcare, training, engineering, and entertainment.
  • Opportunity to design, develop, and test AR/VR prototypes for specific use cases or problem domains.

Course Overview

This course will equip students with skills to collect, clean, and analyze data from diverse sources, while mastering techniques to visually represent insights and trends, enabling informed decision-making in various domains.

Course Outcomes

  • Learn techniques for acquiring data from various sources, such as databases, APIs, web scraping, and CSV files.
  • Develop skills in data cleaning, formatting, and preprocessing, including handling missing values, removing duplicates, and transforming data into suitable formats for analysis and visualization.
  • Able to explore and analyze data using statistical methods, including descriptive statistics, hypothesis testing, correlation analysis, and regression analysis
  • Learn various data visualization techniques, such as scatter plots, line charts, bar charts, histograms, box plots, heatmaps, and geographic maps.
  • Explore advanced visualization techniques, including interactive visualizations, dashboards, and storytelling with data.

Course Overview

This course provides students with the tools and techniques to analyze data, extract actionable insights, and make informed decisions to drive organizational success and competitive advantage in today's dynamic business environment.

Course Outcomes

  • Apply various statistical techniques and data analysis methods to extract meaningful insights from business data.
  • Gain knowledge of data mining techniques and machine learning algorithms used for predictive modeling and pattern recognition in business contexts.
  • Acquire skills in effective data visualization techniques to communicate insights from business data analysis.
  • Understand how to apply analytical techniques to analyze and optimize business processes, operations, and supply chains.
  • Learn how to leverage business analytics to support strategic decision-making and gain competitive advantages.

Course Overview

This course educates students on strategies and technologies to securely manage digital identities, control access to resources, and enforce policies, safeguarding sensitive information and mitigating security risks in organizational environments.

Course Outcomes

  • Gain a solid understanding of the fundamental principles, concepts, and frameworks related to identity and access management.
  • Develop skills in implementing and managing various authentication and authorization mechanisms, including single sign-on (SSO), multi-factor authentication (MFA), role-based access control (RBAC), and attribute-based access control (ABAC).
  • Understand the concepts of digital identities and identity lifecycle management, including provisioning, de-provisioning, and synchronization processes.
  • Gain knowledge of identity federation techniques, such as Security Assertion Markup Language (SAML) and OAuth, enabling secure access and information sharing across different domains or organizations.
  • Learn about the governance, risk, and compliance aspects of IAM, including auditing, reporting, and monitoring mechanisms.

Course Overview

By the end of the course students will learn to design, develop, and deploy software applications with robust security features, incorporating techniques such as secure coding, threat modeling, and vulnerability assessment to mitigate risks and protect against cyber threats in modern computing environments.

Course Outcomes

  • Explain the fundamental principles of software security, including confidentiality, integrity, availability, and non-repudiation.
  • Learn and implement secure coding techniques and best practices for different programming languages and frameworks.
  • Gain knowledge of security controls and countermeasures for software systems, including input validation, output encoding, cryptographic techniques, authentication and authorization mechanisms, and secure communication protocols.
  • Understand the security considerations and best practices for deploying and operating software systems securely, including secure configuration management, patch management, and monitoring.
  • Gain knowledge of relevant security regulations, standards, and compliance requirements for software systems, such as OWASP, PCI-DSS, GDPR, and industry-specific regulations.

Course Overview

This course teaches students how to leverage computational methods to understand and manipulate human language, enabling the development of applications such as language translation, sentiment analysis, and chatbots for diverse real-world applications.

Course Outcomes

  • Understand the fundamental concepts and theories underlying natural language processing, including syntax, semantics, pragmatics, and discourse analysis.
  • Learn techniques for preprocessing raw text data, including tokenization, stemming, lemmatization, and part-of-speech tagging. Explore different methods for representing text data, such as bag-of-words, TF-IDF (Term Frequency-Inverse Document Frequency), word embeddings (e.g., Word2Vec, GloVe), and contextual embeddings (e.g., BERT, GPT)
  • Study a range of statistical and machine learning models commonly used in NLP tasks, including but not limited to Naive Bayes classifiers, hidden Markov models (HMMs), conditional random fields (CRFs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based architectures.
  • Explore a variety of NLP applications and tasks, such as text classification, sentiment analysis, named entity recognition (NER), part-of-speech tagging, machine translation, question answering, summarization, and dialogue systems.
  • Consideration of the ethical and societal implications of NLP technologies, including issues related to privacy, bias, fairness, and transparency.

Course Overview

This course educates students on techniques to analyze and interpret audio and visual data, enabling the development of applications such as speech recognition, emotion detection, object detection, and video surveillance in various fields including entertainment, security, and healthcare.

Course Outcomes

  • Understand the fundamental principles and techniques underlying speech and video processing, including signal representation, analysis, transformation, and synthesis.
  • Learn techniques for processing speech signals, including speech feature extraction, speech recognition, speaker identification, and speech synthesis.
  • Study techniques for processing video signals, including video compression, motion estimation, object detection, tracking, and recognition.
  • Explore methods for integrating and fusing information from multiple modalities, such as speech and video, to enhance the performance of multimedia processing tasks.
  • Explore a variety of applications and use cases of speech and video processing across different domains, including but not limited to multimedia content analysis, surveillance, human-computer interaction, augmented reality, healthcare, and entertainment.

Course Overview

After completing the course students will be able to analyze temporal data patterns and develop predictive models to forecast future trends, enabling informed decision-making in fields such as finance, economics, and resource planning.

Course Outcomes

  • Develop a thorough understanding of time series data structures, including concepts such as trend, seasonality, cyclic patterns, and irregular variations. Learn to identify different types of time series data and their characteristics.
  • Acquire skills in conducting exploratory data analysis specific to time series data.
  • Gain proficiency in various time series modeling techniques, including but not limited to autoregressive integrated moving average (ARIMA) models, seasonal ARIMA (SARIMA) models, exponential smoothing methods, and state-space models.
  • Gain knowledge of identity federation techniques, such as Security Assertion Markup Language (SAML) and OAuth, enabling secure access and information sharing across different domains or organizations.
  • Learn about the governance, risk, and compliance aspects of IAM, including auditing, reporting, and monitoring mechanisms.

Course Overview

This course will instruct students in the theory and application of algorithms to extract information from visual data, enabling the development of systems for tasks like image recognition, object detection, and autonomous navigation in diverse domains such as healthcare, robotics, and security.

Course Outcomes

  • Core concepts and principles of computer vision, including image formation, digital image representation, and image processing techniques.
  • Learn and implement various image processing techniques, such as filtering, edge detection, and image enhancement.
  • Gain knowledge of machine learning algorithms and deep learning architectures used in computer vision, such as convolutional neural networks (CNNs), region-based CNNs, and transformer-based models.
  • Understand advanced computer vision techniques, such as 3D reconstruction, motion analysis, and video processing.
  • Gain awareness of the ethical considerations and potential biases in computer vision systems, such as privacy concerns, algorithmic bias, and fairness issues.

Course Overview

This course introduces students to the fundamentals of decentralized systems and cryptographic principles, enabling them to understand, develop, and implement secure and transparent distributed ledgers for various applications such as cryptocurrency, supply chain management, and digital identity.

Course Outcomes

  • Gain a solid understanding of the basic concepts and principles underlying blockchain technology, including distributed ledgers, consensus mechanisms, cryptography, and decentralization.
  • Acquire practical skills in developing decentralized applications (DApps) on blockchain platforms, utilizing programming languages such as Solidity (for Ethereum) or Go (for Hyperledger Fabric).
  • Explore real-world use cases and applications of blockchain technology across various industries, such as finance, supply chain management, healthcare, and real estate.
  • Gain knowledge of security considerations in blockchain systems, including cryptographic primitives, consensus algorithms, and potential attack vectors.
  • Understand the regulatory landscape surrounding blockchain technology, including legal frameworks, compliance requirements, and data privacy considerations.

Course Overview

This course equips students with advanced techniques in encryption, digital signatures, and secure communication protocols, enabling them to design and implement robust security solutions for protecting sensitive data and ensuring confidentiality, integrity, and authenticity in digital systems.

Course Outcomes

  • Understand fundamental security threats and services and their identifications.
  • Apply symmetric and asymmetric key algorithms for cryptographic purposes.
  • Formulate a security strategy for a specified application.
  • Evaluate key management techniques and emphasize the significance of number theory.
  • Scrutinize the challenges and framework of Authentication Service and Electronic Mail Security.

Course Overview

This course presents how to handle different types of data, analyze statistics, and use tools and techniques to manage business data.

Course Outcomes

  • Understand the fundamental concepts and techniques of data science, including data acquisition, data preprocessing, exploratory data analysis, and data visualization.
  • Apply statistical and machine learning methods to analyze and model complex data sets, including regression, classification, clustering, and dimensionality reduction techniques.
  • Develop proficiency in programming languages and tools commonly used in data science, such as Python, R, SQL, and data manipulation libraries like NumPy, Pandas, and Matplotlib.
  • Gain practical experience in solving real-world data science problems by working on projects and case studies from various domains, such as finance, healthcare, marketing, and social media.
  • Develop ethical and professional practices in data science, including data privacy, security, and responsible use of data, as well as effective communication and collaboration skills for presenting insights and recommendations to stakeholders.

Course Overview

This course presents how to install, maintain, and upgrade networking systems

Course Outcomes

  • Evaluate practical aspects of network services, demonstrating a deep understanding of their application.
  • Examine network layers, analyzing their structure, format, and individual roles, showcasing comprehension of layered network architecture.
  • Design and implement various network applications such as data transmission between client and server, file transfer, real-time multimedia transmission.
  • Implement various Routing Protocols/Algorithms and Internetworking.
  • Create programs using computer network programming principles.

Course Overview

This course focusses on enhancing personal and professional skills to foster holistic growth. Topics include communication skills, emotional intelligence, time management, and leadership development. Through workshops, exercises, and mentorship, participants cultivate self-awareness, confidence, and interpersonal skills essential for success in academic, professional, and personal spheres.

Course Outcomes

  • Construct coherent and concise technical reports, demonstrating advanced written communication skills in English.
  • Apply effective verbal communication strategies in professional settings, such as meetings and presentations.
  • Evaluate and analyze complex technical documents, showcasing a high level of English language comprehension.
  • Demonstrate proficiency in using appropriate corporate English vocabulary and language conventions in engineering contexts.
  • Synthesize and communicate engineering concepts clearly and persuasively in English, fostering effective collaboration in a corporate environment.
  • Construct coherent and concise technical reports, demonstrating advanced written communication skills in English.
  • Apply effective verbal communication strategies in professional settings, such as meetings and presentations.
  • Evaluate and analyze complex technical documents, showcasing a high level of English language comprehension.
  • Demonstrate proficiency in using appropriate corporate English vocabulary and language conventions in engineering contexts.
  • Synthesize and communicate engineering concepts clearly and persuasively in English, fostering effective collaboration in a corporate environment.
  • Construct coherent and concise technical reports, demonstrating advanced written communication skills in English.
  • Apply effective verbal communication strategies in professional settings, such as meetings and presentations.
  • Evaluate and analyze complex technical documents, showcasing a high level of English language comprehension.
  • Demonstrate proficiency in using appropriate corporate English vocabulary and language conventions in engineering contexts.
  • Synthesize and communicate engineering concepts clearly and persuasively in English, fostering effective collaboration in a corporate environment.

Course Overview

This course is designed to make high-quality learning resources accessible to the students. They continue to evolve, incorporating innovative technologies and pedagogical approaches to enhance the learning experience. MOOCs provide certification options for those who successfully complete the course requirements.

Course Outcomes

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Odd Semester

Courses for this semester

Course Overview

The "Artificial Intelligence" course equips students with a deep understanding of AI principles, techniques, and applications. Through lectures, labs, and projects, students explore topics such as intelligent agents, machine learning, natural language processing, and computer vision. Emphasis is placed on ethical considerations and societal implications, preparing students to design and deploy AI solutions responsibly. By course end, students are adept in applying AI algorithms, critically evaluating their impact, and contributing ethically to technological advancement.

Course Outcomes

  • Demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations. Demonstrate awareness and fundamental understanding of various applications of AI.
  • Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.
  • Convert world knowledge into FOPL formula and construct well-crafted prolog programmes of moderate size Apply truth functional propositional Logic(PL) and first order predicate logic (FOPL) to world knowledge
  • Experience AI development tools such as an Prolog. Demonstrate proficiency developing applications in Prolog.
  • Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications. Explore the current scope, potential, limitations, and implications of intelligent systems.

Course Overview

This course provides students with a foundational understanding of statistical concepts and techniques essential for data analysis and decision-making in various fields. Covering topics such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis, this course emphasizes practical applications through hands-on exercises and real-world examples. Students learn to interpret data, conduct statistical tests, and make informed decisions based on statistical findings. Emphasizing critical thinking and analytical skills, the course equips students with the tools necessary to analyze and draw meaningful conclusions from data sets encountered in academic, professional, and everyday contexts.

Course Outcomes

  • Demonstrate a deep understanding of fundamental statistical concepts, including probability theory, hypothesis testing, and sampling techniques.
  • Effectively use statistical software such as R, Python, or SPSS to analyze data sets, perform descriptive statistics, and conduct inferential analyses.
  • Design experiments and surveys, including selecting appropriate sample sizes, randomization methods, and data collection procedures to ensure valid and reliable results.
  • Interpret the results of statistical analyses accurately, including drawing conclusions, making inferences, and identifying limitations of the analyses.
  • Apply advanced statistical techniques such as regression analysis, factor analysis, and non-parametric methods to analyze complex data sets. Effectively communicate statistical findings to both technical and non-technical audiences through written reports, oral presentations, and data visualization techniques.
  • Demonstrate a deep understanding of fundamental statistical concepts, including probability theory, hypothesis testing, and sampling techniques.
  • Effectively use statistical software such as R, Python, or SPSS to analyze data sets, perform descriptive statistics, and conduct inferential analyses.
  • Design experiments and surveys, including selecting appropriate sample sizes, randomization methods, and data collection procedures to ensure valid and reliable results.
  • Interpret the results of statistical analyses accurately, including drawing conclusions, making inferences, and identifying limitations of the analyses.
  • Apply advanced statistical techniques such as regression analysis, factor analysis, and non-parametric methods to analyze complex data sets. Effectively communicate statistical findings to both technical and non-technical audiences through written reports, oral presentations, and data visualization techniques.
  • Demonstrate a deep understanding of fundamental statistical concepts, including probability theory, hypothesis testing, and sampling techniques.
  • Effectively use statistical software such as R, Python, or SPSS to analyze data sets, perform descriptive statistics, and conduct inferential analyses.
  • Design experiments and surveys, including selecting appropriate sample sizes, randomization methods, and data collection procedures to ensure valid and reliable results.
  • Interpret the results of statistical analyses accurately, including drawing conclusions, making inferences, and identifying limitations of the analyses.
  • Apply advanced statistical techniques such as regression analysis, factor analysis, and non-parametric methods to analyze complex data sets. Effectively communicate statistical findings to both technical and non-technical audiences through written reports, oral presentations, and data visualization techniques.
  • Demonstrate a deep understanding of fundamental statistical concepts, including probability theory, hypothesis testing, and sampling techniques.
  • Effectively use statistical software such as R, Python, or SPSS to analyze data sets, perform descriptive statistics, and conduct inferential analyses.
  • Design experiments and surveys, including selecting appropriate sample sizes, randomization methods, and data collection procedures to ensure valid and reliable results.
  • Interpret the results of statistical analyses accurately, including drawing conclusions, making inferences, and identifying limitations of the analyses.
  • Apply advanced statistical techniques such as regression analysis, factor analysis, and non-parametric methods to analyze complex data sets. Effectively communicate statistical findings to both technical and non-technical audiences through written reports, oral presentations, and data visualization techniques.
  • Demonstrate a deep understanding of fundamental statistical concepts, including probability theory, hypothesis testing, and sampling techniques.
  • Effectively use statistical software such as R, Python, or SPSS to analyze data sets, perform descriptive statistics, and conduct inferential analyses.
  • Design experiments and surveys, including selecting appropriate sample sizes, randomization methods, and data collection procedures to ensure valid and reliable results.
  • Interpret the results of statistical analyses accurately, including drawing conclusions, making inferences, and identifying limitations of the analyses.
  • Apply advanced statistical techniques such as regression analysis, factor analysis, and non-parametric methods to analyze complex data sets. Effectively communicate statistical findings to both technical and non-technical audiences through written reports, oral presentations, and data visualization techniques.

Course Overview

This course provides students with the necessary skills and methodologies to conduct comprehensive and critical reviews of existing literature within their respective academic disciplines. Through this course, students learn to identify relevant sources, evaluate their credibility and relevance, and synthesize key findings to provide a coherent overview of existing knowledge and research gaps. Emphasizing the importance of scholarly inquiry and academic writing, students develop proficiency in literature search strategies, citation management, and synthesizing information from diverse sources.

Course Outcomes

  • Conduct comprehensive literature reviews by identifying, accessing, and critically evaluating relevant sources in their field of study. They will develop the ability to synthesize existing knowledge, identify gaps, and articulate research questions based on their review.
  • Gain proficiency in designing research projects by selecting appropriate methodologies, sampling techniques, and data collection methods. They will learn to develop clear and focused research questions, hypotheses, or objectives that guide their research endeavors.
  • Develop the ability to critically evaluate research methods and methodologies used in academic literature. They will assess the strengths and limitations of various research approaches, considering factors such as validity, reliability, generalizability, and ethical considerations.
  • Demonstrate an understanding of ethical principles and guidelines governing research conduct. They will learn to recognize ethical issues related to data collection, participant consent, confidentiality, and potential conflicts of interest, and develop strategies to address these issues in their research practice.
  • To communicate research findings effectively through written reports, presentations, and other forms of scholarly communication. They will learn to structure and format their work according to academic conventions, present findings clearly and coherently, and engage with feedback to improve their research outputs.

Course Overview

Design and Analysis of Algorithms is a course that explores the theory and practice of designing and analyzing algorithms for solving problems. It covers topics such as algorithms for sorting and searching, graph algorithms, and algorithmic techniques for designing efficient solutions to problems. It also covers the application of algorithmic techniques to problems in various disciplines, including computer science, engineering, operations research, and mathematics. The course emphasizes the practical aspects of designing and analyzing algorithms, as well as the theoretical aspects

Course Outcomes

  • Analyze algorithms, apply asymptotic notations, solve mathematical analyses, showing proficiency in problem-solving.
  • Apply sorting and searching algorithms, matrix multiplication, using brute force and divide-and-conquer strategies.
  • Apply greedy approaches and dynamic programming to optimize problems, showcasing expertise in algorithmic design.
  • Apply strategies to solve problems like the N-Queen, knapsack, and traveling salesperson problems.
  • Evaluate decision tree lower bounds, grasp P, NP, and NP-Complete complexity classes, showing understanding of lower bound theory's algorithmic implications.

Course Overview

This course is to introduce the basic theory underlying the different components and phases of a compiler like parsing, code generation etc. Simultaneously, the students will be familiarized with the various tools that are used for building modern compilers.

Course Outcomes

  • Identify compiler phases and explain lexical analysis principles.
  • Apply parsing techniques to build a language-specific syntax analyzer.
  • Analyze syntax-directed translations, evaluation order, and type checking.
  • Understand the association of runtime storage allocation with control flow and procedure calls.
  • Apply code optimization on intermediate code for target code generation.

Course Overview

Design and Analysis of Algorithms is a course that explores the theory and practice of designing and analyzing algorithms for solving problems. It covers topics such as algorithms for sorting and searching, graph algorithms, and algorithmic techniques for designing efficient solutions to problems. It also covers the application of algorithmic techniques to problems in various disciplines, including computer science, engineering, operations research, and mathematics. The course emphasizes the practical aspects of designing and analyzing algorithms, as well as the theoretical aspects

Course Outcomes

  • Analyze algorithms, apply asymptotic notations, solve mathematical analyses, showing proficiency in problem-solving.
  • Apply sorting and searching algorithms, matrix multiplication, using brute force and divide-and-conquer strategies.
  • Apply greedy approaches and dynamic programming to optimize problems, showcasing expertise in algorithmic design.
  • Apply strategies to solve problems like the N-Queen, knapsack, and traveling salesperson problems.
  • Evaluate decision tree lower bounds, grasp P, NP, and NP-Complete complexity classes, showing understanding of lower bound theory's algorithmic implications.

Course Overview

This course is to introduce the basic theory underlying the different components and phases of a compiler like parsing, code generation etc. Simultaneously, the students will be familiarized with the various tools that are used for building modern compilers.

Course Outcomes

  • Identify compiler phases and explain lexical analysis principles.
  • Apply parsing techniques to build a language-specific syntax analyzer.
  • Analyze syntax-directed translations, evaluation order, and type checking.
  • Understand the association of runtime storage allocation with control flow and procedure calls.
  • Apply code optimization on intermediate code for target code generation.
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Even Semester

Courses for this semester

Course Overview

This course offers an in-depth exploration of cryptographic principles and their application in securing computer networks and communication systems. Through lectures, practical exercises, and case studies, students gain a comprehensive understanding of cryptographic algorithms, protocols, and techniques used to ensure confidentiality, integrity, and authentication of data in networked environments. Topics covered include symmetric and asymmetric encryption, cryptographic hash functions, digital signatures, key management, and secure communication protocols. Emphasizing both theoretical concepts and practical implementations, the course equips students with the knowledge and skills necessary to design, implement, and manage secure networked systems, addressing contemporary challenges and emerging threats in cybersecurity.

Course Outcomes

  • Understand fundamental security threats and services and their identifications.
  • Apply symmetric and asymmetric key algorithms for cryptographic purposes.
  • Formulate a security strategy for a specified application.
  • Evaluate key management techniques and emphasize the significance of number theory.
  • Scrutinize the challenges and framework of Authentication Service and Electronic Mail Security.

Course Overview

Digital image processing deals with processing of images which are digital in nature. Study of the subject is motivated by three major applications. The first application is in improvement of pictorial information for human perception i.e. enhancing the quality of the image so that the image will have a better look. The second is for autonomous machine applications which have wider applications in industries, particularly for quality control in assembly automation and many similar applications. This course will introduce various image processing techniques, algorithms and their applications.

Course Outcomes

  • Understand image processing basics and apply enhancement and restoration techniques to improve image quality.
  • Understand segmentation principles and extract meaningful features using edge detection and texture analysis methods.
  • Apply statistical classification and decision theory to recognize patterns and categorize data effectively.
  • Analyze supervised and unsupervised learning algorithms, including SVM and neural networks, for pattern recognition tasks.
  • Evaluate computer vision, medical imaging, and biometric systems, considering advanced pattern recognition techniques for improved performance.

Course Overview

This course provides a comprehensive overview of key topics in technology, including programming, data analysis, cybersecurity, and emerging technologies, in just 30 hours. Learners gain practical skills and theoretical knowledge essential for success in the tech industry, with a flexible schedule and accessible online platform for convenient learning.

Course Outcomes

Course Overview

This course provides a fast track to a globally recognized certification in collaboration with Coursera and Amazon Web Services (AWS). Participants gain expertise in cloud computing and AWS services, preparing them for the AWS Certified Solutions Architect – Associate exam and advancing their careers in the cloud industry

Course Outcomes

Course Overview

This course provides a fast track to a globally recognized certification in collaboration with Coursera and Amazon Web Services (AWS). Participants gain expertise in cloud computing and AWS services, preparing them for the AWS Certified Solutions Architect – Associate exam and advancing their careers in the cloud industry

Course Outcomes

Course Overview

This course provides a fast track to a globally recognized certification in collaboration with Coursera and Amazon Web Services (AWS). Participants gain expertise in cloud computing and AWS services, preparing them for the AWS Certified Solutions Architect – Associate exam and advancing their careers in the cloud industry

Course Outcomes

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Scholarship

Apply Scholarship through CST

CST- Common scholarship test is a national and international level online MCQ based examination funded for intellectual empowerment by Assam down town University.

CST- Maximum enrolment each year is 120 seats and any 10+2 students can apply. Adtu is northeast India’s first placement driven university to provide 100% scholarship benefits worth 10 cr.

CST aims to inspire brilliant and competent students to pursue further education. Accredited with a prestigious grade by NAAC, UGC and AICTE.

Apply Scholarship Through

Explore more scholarships that can help you reach out your goal with financial aid.

This scholarship is valid on the basis of the board/university examination

95% & above 100% Scholarship on all semester
90%-94.9% 50% Scholarship on all semester
80%-89.9% 25% Scholarship on all semester

This scholarship is valid on the basis of the board/university exam

National & International Level 100% Scholarship on all semester
State Level 50% Scholarship on all semester
District Level 25% Scholarship on all semester

This scholarship is valid on the basis of the board/university exam

National & International Level 100% Scholarship on all semester
State Level 50% Scholarship on all semester
District Level & NCC Certificate Holder 25% Scholarship on all semester

A 50% scholarship on total semester fees is provided to all specially abled students.

A 100% scholarship on the last semester fee is provided to all the alumni of Assam down town University.

A 100% scholarship on total semester fee for Economically Backward Classes

Campus Life

Our Facilities

World Class Facilities

Discover a multitude of world-class amenities and cutting-edge resources at Assam down town University, enhancing your academic journey to new heights.

Some of our Facilities
  • Library
  • Swimming Pool
  • Play Ground
  • Amphitheatre
  • Basketball Court
  • Cinema Hall
  • Cafeteria
  • Canteen
  • Indoor stadium
  • Yoga Studio
  • Gym
  • ATM

Start-Up &
Incubation Centre

The Start-Up & Incubation Centre at Assam down town University provides a supportive environment for young entrepreneurs to develop and grow their business ideas. The center provides mentorship, funding, and networking opportunities to help innovative ideas become successful businesses.

Rural Empowerment with SFURTI scheme

SFURTI scheme to support rural entrepreneurs and innovators, an initiative by the Ministry of MSME

TIDE 2.0 scheme for ICT-based startups

TIDE 2.0 scheme for ICT-based startups which provides a grant of Rs. 4L and Rs. 7L under EiR and Grant categories respectively, an initiative by the Ministry of MeitY.

dtVL Ideation interest-free loans up to Rs. 2 lakhs.

dtVL Ideation, an incubation program for early-stage entrepreneurs with a market-ready solution/product, offering interest-free loans up to Rs. 2 lakhs.

Innovation with Sprout UP program

Sprout UP, an incubation program for students, faculties, and researchers with innovative business ideas, prototypes, or technology solutions.

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What our Students say

Kailash Timsina
Student

"I am a BBA student of 3rd semester. I hail from Bhutan. I vow that I am having a great experience i...

Juliush Mushahary
Student

"AdtU is amazing. I am a BBA student of 2019-22 batch and I am just grateful for the amount of oppor...

Reshi Prasad Pokhrel
Student

Let us be grateful to the people and place who makes us happy. They are the charming gardeners whom ...

Debapriya Paul
Student

Currently I am pursuing MBA in Assam Down Town University. MBA is the professional course through wh...

Priti Jain
Student

AdtU is a university that focuses on giving knowledge, education and simultaneously making the stude...

Madhurya Bujar Barua
Student

The Assam downtown University has been a great learning experience. The university has provided me w...

Naeem Hussain
Student

My experience with AdtU has been splendid one indeed. Little needs to said about its scenic infrastr...

Dr Dipanjali Hazarika
Student

As a student I am very glad that I have got an opportunity to study here in Assam downtown universi...

Sakhyajit Roy
Student

My name is Sakhyajit Roy. I?m from Tripura. I joined the university on Auguest, 2017 as a student of...

Runi Bharadwaj
Student

I share immense pleasure to share my post graduate program experience in Assam down town University....

Jenifer Dhar
Student

AdtU is a platform where I got golden opportunities to feed my zeal for knowledge through the dynami...

Salehah Hussain Uthman
Student

I am fortunate to get an opportunity to study here in Assam Downtown University. The best thing abou...

Nisha Nirola
Student

Our university is one of the best place for developing ourselves in the field of research and acedem...

Liangsi Hallam
Student

ADTU is a university that is very good interms of infrastructure, academics and placements. Our tea...

Anushmita Kashyap
Student

It is one of best private colleges in North East India, it also provides a good environment for ed...

Dasanibha Mawphlang
Student

ADTU is a good University which provides the students with best quality lectures and ensures comfort...

Farhin zakia
Student

The environment of Assam downtown university is very pleasant.The department of BMLT is very good a...

Anamika Das
Student

The university has all the necessary facilities and amenities for students . The classrooms and the ...

Susmita Sinha
Student

Assam downtown University is well recognised all over india. In the ongoing pandemic situation it ha...

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