Program Specific Outcome
- PSO1: Ability to apply the knowledge of Information Technology with recent trends aligned with research and industry.
- PSO2: Ability to apply IT in the field of Computational Research, Soft Computing, Big Data Analytics, Data Science, Image Processing, Artificial Intelligence, Networking and Cloud Computing.
- PSO3: Ability to provide socially acceptable technical solutions in the domains of Information Security, Machine Learning, Internet of Things and Embedded System, Infrastructure Services as specializations.
- PSO4: Ability to apply the knowledge of Intellectual Property Rights, Cyber Laws and Cyber Forensics and various standards in interest of National Security and Integrity along with IT Industry.
- PSO5: Ability to write effective project reports, research publications and content development and to work in multidisciplinary environment in the context of changing technologies
Course Outcome
Semester I
- Research in Computing
- To be able to conduct business research with an understanding of all the latest theories.
- To develop the ability to explore research techniques used for solving any real world or innovate problem.
A learner will be able to:
- Solve real world problems with scientific approach.ï€
- Develop analytical skills by applying scientific methods.ï€
- Recognize, understand and apply the language, theory and models of the field of business analyticsï€
- Foster an ability to critically analyze, synthesize and solve complex unstructured business problemsï€
- Understand and critically apply the concepts and methods of business analytics
- Identify, model and solve decision problems in different settingsï€
- Interpret results/solutions and identify appropriate courses of action for a given managerial situation whether a problem or an opportunityï€
- Create viable solutions to decision making problemsï€
- Data Science
- Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics.ï€
- Practice problem analysis and decision-making.ï€
- Gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences.ï€
- Apply quantitative modeling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.
- Recognize and analyze ethical issues in business related to intellectual property, data security, integrity, and privacy.
- Apply ethical practices in everyday business activities and make well-reasoned ethical business and data management decisions.
- Demonstrate knowledge of statistical data analysis techniques utilized in business decision-making.
- Apply principles of Data Science to the analysis of business problems.
- Use data mining software to solve real-world problems.
- Employ cutting edge tools and technologies to analyze Big Data.
- Apply algorithms to build machine intelligence.
- Demonstrate use of teamwork, leadership skills, decision-making and organization theory.
- Cloud Computing
- To learn how to use Cloud Services.ï€
- To implement Virtualization.ï€
- To implement Task Scheduling algorithms.ï€
- Apply Map-Reduce concept to applications.ï€
- To build Private Cloud.ï€
- Broadly educate to know the impact of engineering on legal and societal issues involved.
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Course Outcome :
- Analyze the Cloud computing setup with its vulnerabilities and applications using different architectures.
- Design different workflows according to requirements and apply map reduce programming model.
- Apply and design suitable Virtualization concept, Cloud Resource Management and design scheduling algorithms.
- Create combinatorial auctions for cloud resources and design scheduling algorithms for computing clouds
- Assess cloud Storage systems and Cloud security, the risks involved, its impact and develop cloud application
- Broadly educate to know the impact of engineering on legal and societal issues involved in addressing the security issues of cloud computing.
- Soft Computing Techniques
- Soft computing concepts like fuzzy logic, neural networks and genetic algorithm, where Artificial Intelligence is mother branch of all.
- All these techniques will be more effective to solve the problem efficiently
Course Outcome :
- Identify and describe soft computing techniques and their roles in building intelligent machines
- Recognize the feasibility of applying a soft computing methodology for a particular problem
- Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems
- Apply genetic algorithms to combinatorial optimization problems
- Apply neural networks for classification and regression problems
- Effectively use existing software tools to solve real problems using a soft computing approach
- Evaluate and compare solutions by various soft computing approaches for a given problem.
Semester II
- BigData Analytics
Course Objectives:
- To provide an overview of an exciting growing field of big data analytics.
- To introduce the tools required managing and analyzing big data like Hadoop, NoSql MapReduce.
- To teach the fundamental techniques and principles in achieving big data analytics with scalability and streaming capability.
- To enable students to have skills that will help them to solve complex realworld problems in for decision support
Course Objectives:
- Understand the key issues in big data management and its associated applications in intelligent business and scientific computing.
- Acquire fundamental enabling techniques and scalable algorithms like Hadoop, Map Reduce and NO SQL in big data analytics.
- Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
- Achieve adequate perspectives of big data analytics in various applications like recommender systems, social media applications etc.
- Modern Networking
Course Objectives
- To understand the state-of-the-art in network protocols, architectures and applications.
- Analyze existing network protocols and networks.
- Develop new protocols in networking
- To understand how networking research is done
- To investigate novel ideas in the area of Networking via term-long research projects.
Course Objectives
- Demonstrate in-depth knowledge in the area of Computer Networking.
- To demonstrate scholarship of knowledge through performing in a group to identify, formulate and solve a problem related to Computer Networks
- Prepare a technical document for the identified Networking System Conducting experiments to analyze the identified research work in building Computer Networks
- Microservice Architecture
Course Objectives
- Gain a thorough understanding of the philosophy and architecture of
- Web applications using ASP.NET Core MVC;ï€
- Gain a practical understanding of.NET Core;ï€
- Acquire a working knowledge of Web application development using
- ASP.NET Core MVC 6 and Visual Studioï€
- Persist data with XML Serialization and ADO.NET with SQL Serverï€
- Create HTTP services using ASP.NET Core Web API;ï€
- Deploy ASP.NET Core MVC applications to the Windows Azure cloud.ï€
Course Outcome
- Develop web applications using Model View Control.ï€
- Create MVC Models and write code that implements business logic within Model methods, properties, and events.ï€
- Create Views in an MVC application that display and edit data and interact with Models and Controllers.ï€
- Boost your hire ability through innovative and independent learning.ï€
- Gaining a thorough understanding of the philosophy and architecture of .NET Coreï€
- Understanding packages, meta packages and frameworksï€
- Acquiring a working knowledge of the .NET programming modelï€
- Implementing multi-threading effectively in .NET applications
- Image Processing
- Review the fundamental concepts of a digital image processing system.
- Analyze images in the frequency domain using various transforms.
- Evaluate the techniques for image enhancement and image restoration.
- Categorize various compression techniques.
- Interpret Image compression standards.
- Interpret image segmentation and representation techniques.
Course Outcome :
- Understand the relevant aspects of digital image representation and their practical implications.
- Have the ability to design pointwise intensity transformations to meet stated specifications.
- Understand 2-D convolution, the 2-D DFT, and have the abitilty to design systems using these concepts.
- Have a command of basic image restoration techniques.
- Understand the role of alternative color spaces, and the design requirements leading to choices of color space.
- Appreciate the utility of wavelet decompositions and their role in image processing systems.
- Have an understanding of the underlying mechanisms of image compression, and the ability to design systems using standard algorithms to meet design specifications.
Semester III
- Technical Writing
Course Objectives:
- This course aims to provide conceptual understanding of developing strong foundation in general writing, including research proposal and reports.
- It covers the technological developing skills for writing Article, Blog, E-Book, Commercial web Page design, Business Listing Press Release, E-Listing and Product Description.
- This course aims to provide conceptual understanding of innovation and entrepreneurship development.
Course Outcomes:
After completion of the course, a student should be able to:
- CO1: Develop technical documents that meet the requirements with standard guidelines. Understanding the essentials and hands-on learning about effective Website Development.
- CO2: Write better quality content which ranks faster at Search Engines. Build effective Social Media Pages.
- CO3: Evaluate the essentials parameters of effective Social Media Pages.
- CO4: Understand importance of innovation and entrepreneurship.
- CO5: Analyze research and development projects.
- Applied AI
Course Objectives: -
- To explore the applied branches of artificial intelligence –
- To enable the learner to understand applications of artificial intelligence –
- To enable the student to solve the problem aligned with derived branches of artificial intelligence.
Course Outcomes:
After completion of course the learner will:
- CO1: be able to understand the fundamentals concepts of expert system and its applications.
- CO2: be able to use probability and concept of fuzzy sets for solving AI based problems.
- CO3: be able to understand the applications of Machine Learning. The learner can also apply fuzzy system for solving problems.
- CO4: learner will be able to apply to understand the applications of genetic algorithms in different problems related to artificial intelligence.
- CO5: A learner can use knowledge representation techniques in natural language processing.
- Machine Learning
Course Objectives:
- Understanding Human learning aspects.
- Understanding primitives in learning process by computer.
- Understanding nature of problems solved with Machine Learning
Course Outcomes:
After completion of the course, a student should be able to:
- CO1: Understand the key issues in Machine Learning and its associated applications in intelligent business and scientific computing.
- CO2: Acquire the knowledge about classification and regression techniques where a learner will be able to explore his skill to generate data base knowledge using the prescribed techniques.
- CO3: Understand and implement the techniques for extracting the knowledge using machine learning methods.
- CO4: Achieve adequate perspectives of big data analytics in various applications like recommender systems, social media applications etc.
- CO5: Understand the statistical approach related to machine learning. He will also Apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.
- Robotic Process Automation
Course Objectives:
- To make the students aware about the automation today in the industry.
- To make the students aware about the tools used for automation.
- To help the students automate a complete process
Course Outcomes: After completing the course, a learner will be able to:
- CO1: Understand the mechanism of business process and can provide the solution in an optimize way.
- CO2: Understand the features use for interacting with database plugins.
- CO3: Use the plug-ins and other controls used for process automation.
- CO4: Use and handle the different events, debugging and managing the errors.
- CO5: Test and deploy the automated process.