Programs Outcome

 

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

1. 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.
• Develop analytical skills by applying scientific methods.
• Recognize, understand and apply the language, theory and models of the field of business analytics 
• Foster an ability to critically analyze, synthesize and solve complex unstructured business problems
• Understand and critically apply the concepts and methods of business analytics
• Identify, model and solve decision problems in different settings
• Interpret results/solutions and identify appropriate courses of action for a given managerial situation whether a problem or an opportunity
• Create viable solutions to decision making problems

 

1. 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.
• Practice problem analysis and decision-making.
• Gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences.
• 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.

 

1. Cloud Computing

• To learn how to use Cloud Services.
• To implement Virtualization.
• To implement Task Scheduling algorithms.
• Apply Map-Reduce concept to applications.
• To build Private Cloud.
• Broadly educate to know the impact of engineering on legal and societal issues involved.

 



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.

 

1. 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

 

1. 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.

 

1. 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

 

1. Microservice Architecture

 

Course Objectives

 

• Gain a thorough understanding of the philosophy and architecture of
• Web applications using ASP.NET Core MVC;
• Gain a practical understanding of.NET Core;
• Acquire a working knowledge of Web application development using
• ASP.NET Core MVC 6 and Visual Studio
• Persist data with XML Serialization and ADO.NET with SQL Server
• Create HTTP services using ASP.NET Core Web API;
• Deploy ASP.NET Core MVC applications to the Windows Azure cloud.

 

Course Outcome

 

• Develop web applications using Model View Control.
• Create MVC Models and write code that implements business logic within Model methods, properties, and events.
• Create Views in an MVC application that display and edit data and interact with Models and Controllers.
• Boost your hire ability through innovative and independent learning.
• Gaining a thorough understanding of the philosophy and architecture of .NET Core
• Understanding packages, meta packages and frameworks
• Acquiring a working knowledge of the .NET programming model
• Implementing multi-threading effectively in .NET applications

 

1. 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

 

1. 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.

 

1. 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.

 

 

1. 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.

 

1. 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.