Follow us on:
M.Sc.(Information Technology) Links
Courses Outcome
M.Sc. IT Part I - Semester I ❯
| Class | Course | Outcomes (Students will be able to) |
|---|---|---|
| M.Sc. IT Part I Sem I Major |
Data Science |
|
| M.Sc. IT Part I Sem I Major |
Data Science Practical |
|
| M.Sc. IT Part I Sem I Major |
Soft Computing Techniques |
|
| M.Sc. IT Part I Sem I | Soft Computing Techniques Practical Major |
|
| M.Sc. IT Part I Sem I Major |
Cloud Computing |
|
| M.Sc. IT Part I Sem I Elective |
Security Breaches and Countermeasures Practical |
|
| M.Sc. IT Part I Sem I Research Methodology |
Research Methodology |
|
M.Sc. IT Part I - Semester II ❯
| Class | Course | Outcomes (Students will be able to) |
|---|---|---|
| M.Sc. IT Part I Sem II Major |
Big Data Analytics |
|
| M.Sc. IT Part I Sem II Major |
Big Data Analytics Practical |
|
| M.Sc. IT Part I Sem II Major |
Modern Networking |
|
| M.Sc. IT Part I Sem II Major |
Modern Networking Practical |
|
| M.Sc. IT Part I Sem II Major |
Microservices Architecture |
|
| M.Sc. IT Part I Sem II Elective |
Malware Analysis Practical |
|
| M.Sc. IT Part I Sem II QJT |
OJT |
|
M.Sc. IT Part II - Semester III ❯
| Class | Course | Outcomes (Students will be able to) |
|---|---|---|
| M.Sc. IT Part II Sem III Major |
Advanced AI |
|
| M.Sc. IT Part II Sem III Major |
Advanced AI Practical |
|
| M.Sc. IT Part II Sem III Major |
Machine Learning |
|
| M.Sc. IT Part II Sem III Major |
Machine Learning Practical |
|
| M.Sc. IT Part II Sem III Major |
Storage as a Service |
|
| M.Sc. IT Part II Sem III Electives |
Natural Language Processing |
|
| M.Sc. IT Part II Sem III Research Projects |
Research Project |
|
M.Sc. IT Part II - Semester IV ❯
| Class | Course | Outcomes (Students will be able to) |
|---|---|---|
| M.Sc. IT Part II Sem IV Major |
Blockchain |
|
| M.Sc. IT Part II Sem IV Major |
Blockchain Practical |
|
| M.Sc. IT Part II Sem IV Major |
Deep Learning |
|
| M.Sc. IT Part II Sem IV Major |
Deep Learning Practical |
|
| M.Sc. IT Part II Sem IV Elective |
Robotic Process Automation |
|
| M.Sc. IT Part II Sem IV Research Projects |
Research Project |
|
M.Sc. IT Part I - Semester I ❯
Major
Data Science
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: 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.
- CO2: Recognize and analyze ethical issues in business related to intellectual property, data security, integrity, and privacy.
- CO3: Apply ethical practices in everyday business activities and make well-reasoned ethical business and data management decisions.
- CO4: Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
- CO5: Apply principles of Data Science to the analysis of business problems.
- CO6: Use data mining software to solve real-world problems.
- CO7: Employ cutting edge tools and technologies to analyze Big Data.
- CO8: Apply algorithms to build machine intelligence.
- CO9: Demonstrate use of team work, leadership skills, decision making and organization theory.
Major
Data Science Practical
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: 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.
- CO2: Recognize and analyze ethical issues in business related to intellectual property, data security, integrity, and privacy.
- CO3: Apply ethical practices in everyday business activities and make well-reasoned ethical business and data management decisions.
- CO4: Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
- CO5: Apply principles of Data Science to the analysis of business problems.
- CO6: Use data mining software to solve real-world problems.
- CO7: Employ cutting edge tools and technologies to analyze Big Data.
- CO8: Apply algorithms to build machine intelligence.
- CO9: Demonstrate use of team work, leadership skills, decision making and organization theory.
Major
Soft Computing Techniques
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: Gain a solid understanding of the fundamental concepts underlying soft computing, including the differences between soft computing and traditional hard computing methods.
- CO2: Familiarize with a variety of soft computing techniques such as fuzzy logic, neural networks, genetic algorithms, swarm intelligence, and probabilistic reasoning.
- CO3: Apply soft computing techniques to solve real-world problems from various domains such as engineering, finance, healthcare, and more.
- CO4: Formulate problems in a way that lends itself to the application of soft computing techniques, taking into account the uncertainties and imprecisions present in real-world data.
- CO5: Understand of how fuzzy logic works and its applications in modeling and decision-making under uncertainty.
- CO6: Gain knowledge of neural network architectures, training algorithms, and their applications in pattern recognition, regression, and classification tasks.
- CO7: Understand genetic algorithms, their components, and their use in optimization problems and search spaces.
- CO8: Familiarize with swarm intelligence algorithms such as ant colony optimization and particle swarm optimization, and their applications in optimization and search problems.
Major
Soft Computing Techniques Practical
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: Identify and describe soft computing techniques and their roles in building intelligent machines
- CO2: Recognize the feasibility of applying a soft computing methodology for a particular problem
- CO3: Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems
- CO4: Apply genetic algorithms to combinatorial optimization problems
- CO5: Apply neural networks for classification and regression problems
- CO6: Effectively use existing software tools to solve real problems using a soft computing approach
- CO7: Evaluate and compare solutions by various soft computing approaches for a given problem.
Major
Cloud Computing
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: Analyze the Cloud computing setup with its vulnerabilities and applications using different architectures.
- CO2: Design different workflows according to requirements and apply map reduce programming model.
- CO3: Apply and design suitable Virtualization concept, Cloud Resource Management and design scheduling algorithms.
- CO4: Create combinatorial auctions for cloud resources and design scheduling algorithms for computing cloud.
- CO5: Assess cloud Storage systems and Cloud security, the risks involved, its impact and develop cloud application
- CO6: Broadly educate to know the impact of engineering on legal and societal issues involved in addressing the security issues of cloud computing.
Elective
Security Breaches and Countermeasures Practical
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: The student should be able to identify the different security breaches that can occur. The student should be able to evaluate the security of an organization and identify the loopholes. The student should be able to perform enumeration and network scanning.
- CO2: The student should be able to identify the vulnerability in the systems, breach the security of the system, identify the threats due to malware and sniff the network. The student should be able to do the penetration testing to check the vulnerability of the system towards malware and network sniffing.
- CO3: The student should be able to perform social engineering and educate people to be careful from attacks due to social engineering, understand and launch DoS and DDoS attacks, hijack and active session and evade IDS and Firewalls. This should help the students to make the organization understand the threats in their systems and build robust systems.
- CO4: The student should be able to identify the vulnerabilities in the Web Servers, Web Applications, perform SQL injection and get into the wireless networks. The student should be able to help the organization aware about these vulnerabilities in their systems.
- CO5: The student should be able to identify the vulnerabilities in the newer technologies like mobiles, IoT and cloud computing. The student should be able to use different methods of cryptography.
Core
Research Methodology
M.Sc. IT Part I Sem I
Course Outcomes:
- CO1: solve real world problems with scientific approach.
- CO2: develop analytical skills by applying scientific methods.
- CO3: recognize, understand and apply the language, theory and models of the field of business analytics
- CO4: foster an ability to critically analyze, synthesize and solve complex unstructured business problems
- CO5: understand and critically apply the concepts and methods of business analytics
- CO6: identify, model and solve decision problems in different settings
- CO7: interpret results/solutions and identify appropriate courses of action for a given managerial situation whether a problem or an opportunity
- CO8: create viable solutions to decision making problems
M.Sc. IT Part I - Semester II ❯
Major
Big Data Analytics
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: Understand Big Data Concepts
- CO2: Do Data Collection and Integration
- CO3: Develop Data Storage and Management
- CO4: Perform Data Preprocessing and Cleaning
- CO5: Understand Data Transformation and Feature Engineering
- CO6: Perform Exploratory Data Analysis (EDA)
- CO7: Use Big Data Analytics Tools
Major
Big Data Analytics Practical
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: Understand the key issues in big data management and its associated applications in intelligent business and scientific computing.
- CO2: Acquire fundamental enabling techniques and scalable algorithms like Hadoop, Map Reduce and NO SQL in big data analytics.
- CO3: Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
- CO4: Achieve adequate perspectives of big data analytics in various applications like recommender systems, social media applications etc.
Major
Modern Networking
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: Understand the modern networking concepts and implement
Major
Modern Networking Practical
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: Demonstrate in-depth knowledge in the area of Computer Networking.
- CO2: To demonstrate scholarship of knowledge through performing in a group to identify, formulate and solve a problem related to Computer Networks
- CO3: Prepare a technical document for the identified Networking System Conducting experiments to analyze the identified research work in building Computer Networks
Major
Microservices Architecture
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: Develop web applications using Model View Controller.
- CO2: Think and apply the microservices way to software development.
Elective
Malware Analysis Practical
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: Understand the concepts of VMM, SDN, NAS , HyperV etc.
- CO2: Understand and use of Service manager with various deployments that can be performed using it.
- CO3: Understand and use SCCM and Demonstrate the use of Configuration Manager
- CO4: Use automation with runbooks and demonstrate the use of Windows Orchestrator
- CO5: Use Data Protection Manager
Core
OJT
M.Sc. IT Part I Sem II
Course Outcomes:
- CO1: apply concepts learned in classrooms to real-world work environments, enhancing their understanding and skills.
- CO2: show insights into the challenges, opportunities, and culture of different workplaces, preparing them for future employment.
- CO3: navigate through various learning modalities effectively through exposure to hybrid learning models.
- CO4: show evidence of research aptitude and skills of critical thinking, analytical skills, and ethical research conduct in the conduct, and communication of their work
- CO5: use and appreciate the use of emerging technologies and their applications, enhancing their technological literacy and adaptability.
- CO6: display problem-solving abilities in making informed decisions in complex scenarios through practical situations.
- CO7: work in teams and collaborate to achieve common goals in diverse work environments through collaborative projects.
- CO8: give examples and cite ways of contributing to the field of work in a manner that displays social responsibility and sustainability.
- CO9: display integrity in their dealings with their work and the people that they interact with by upholding professional principles and ethical standards.
M.Sc. IT Part II - Semester III ❯
Major
Advanced AI
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: Understand the fundamental principles and concepts of Artificial Intelligence.
- CO2: Implement intelligent agents for different applications.
- CO3: Understand advanced AI concepts and techniques
- CO4: Demonstrate proficiency in deep learning and neural networks
- CO5: Understand the concepts and applications of generative AI
- CO6: Implement generative adversarial networks and variational autoencoders
- CO7: Develop skills in using neural networks for image recognition and text generation
- CO8: Create and train GAN models for image synthesis
Major
Advanced AI Practical
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: Understand Deep Learning Fundamentals
- CO2: Understand NLP Fundamentals
- CO3: Implement Chatbot Architectures
- CO4: Implement Deep Learning approach
- CO5: Understand Computer Vision
- CO6: Understand Generative Adversarial Networks (GANs)
- CO7: Understand reinforcement learning algorithms
- CO8: Understand Transfer learning fundamentals
- CO9: Implement anomaly detection technique
- CO10: Understand Automated techniques
- CO11: Implement evolutionary algorithms
- CO12: Deploying a machine learning model
- CO13: Deploying Python Libraries
- CO14: Understand Generative Adversarial Networks implementation
Major
Machine Learning
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: Define and demonstrate an understanding of Machine Learning and its related terms conceptually and mathematically
- CO2: Identify and differentiate the advantages and limitations of Machine Learning algorithms and their use cases.
- CO3: Implement classifier algorithms for supervised learning tasks.
- CO4: Apply feature engineering techniques to improve a dataset for machine learning.
- CO5: Evaluate the performance of different machine learning models on a given dataset.
- CO6: Diagnose reasons for poor performance in a machine learning model.
- CO7: Analyze the ethical implications of a machine learning system.
- CO8: Critique the suitability of a machine learning solution for a real-world problem, considering factors beyond just model performance.
- CO9: Assess the potential biases and fairness concerns in a machine learning model.
- CO10: Develop a custom machine learning algorithm for a specific real-world problem.
- CO11: Propose a comprehensive machine learning solution to address a complex real-world challenge.
Major
Machine Learning Practical
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: Preprocess the given dataset and perform a relevant assessment of data.
- CO2: Implement classifier algorithms for supervised learning tasks.
- CO3: Apply feature engineering techniques to improve a dataset for machine learning.
- CO4: Evaluate the performance of different machine learning models on a given dataset.
- CO5: Diagnose reasons for poor performance in a machine learning model.
- CO6: Assess the potential biases and fairness concerns in a machine learning model.
- CO7: Develop a custom machine learning algorithm for a specific real-world problem
Major
Storage as a Service
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: Covers the evolution of data access methods and introduces concepts like network storage architectures, storage networking functions, and storage I/O requirements.
- CO2: Discusses different types of storage devices (disk drives, tape drives) and subsystem architectures, along with storage interconnect technologies like SCSI.
- CO3: Explores the concept of storage virtualization, its technologies, and implications for performance and reliability, as well as the fundamentals of network backup.
- CO4: Covers the relationship between file systems and operating systems, network file system basics, and protocols like NFS and CIFS.
- CO5: Discusses clustered and distributed file systems, network storage for databases, and data management techniques including historical file versions and compliance storage.
- CO6: Provides a historical context for storage networking, reviews block and file storage protocols, optical technologies, virtualization implementations, and network operating principles.
Elective
Natural Language Processing
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: Students will get idea about know-hows, issues and challenge in Natural Language Processing and NLP applications and their relevance in the classical and modern context.
- CO2: Student will get understanding of Computational techniques and approaches for solving NLP problems and develop modules for NLP tasks and tools such as Morph Analyzer, POS tagger, Chunker, Parser, WSD tool etc.
- CO3: Students will also be introduced to various grammar formalisms, which they can apply in different fields of study.
- CO4: Students can take up project work or work in R&D firms working in NLP and its allied areas.
- CO5: Student will be able to understand applications in different sectors
Core
Research Project
M.Sc. IT Part II Sem III
Course Outcomes:
- CO1: The student is expected to gain expertise in research methodologies, critical thinking, data analysis, and problem-solving, equipping them with skills essential for further academic or professional endeavors.
- CO2: The research contributes new knowledge, insights, or innovations in the chosen field, pushing the boundaries of understanding and addressing current challenges or gaps in the literature.
- CO3: Completing the research project enhances the student's academic credibility and professional skills, positioning them for leadership roles, higher positions in academia, or specialized careers in their field.
- CO4: The research might lead to published papers, conference presentations, or recognition within academic communities, establishing the student as a contributor to scholarly discourse and increasing visibility in their field.
- CO5: The project often has practical applications in solving real-world problems, influencing policies, technologies, or practices within industries, healthcare, social services, or public administration.
- CO6: Through the research process, students may collaborate with faculty, industry professionals, or other researchers, broadening their academic and professional network and gaining exposure to interdisciplinary work.
- CO7: The research outcomes can contribute to societal progress by addressing pressing global issues, such as sustainability, health, education, or technology, potentially influencing public policy, industry standards, and community practices.
- CO8: The postgraduate research project fosters self-reliance, perseverance, and the ability to manage complex tasks, helping students develop critical personal attributes like time management, resilience, and intellectual independence.
M.Sc. IT Part II - Semester IV ❯
Major
Blockchain
M.Sc. IT Part II Sem IV
Course Outcomes:
- CO1: provide conceptual understanding of the function of Blockchain as a method of securing distributed ledgers, how consensus on their contents is achieved.
- CO2: demonstrate blockchain applications.
- CO3: gain the ability to write and deploy basic smart contracts on the Ethereum blockchain using Solidity programming language and tools like Truffle and Remix.
- CO4: able to analyze cryptoeconomic design and learn NFT and permissioned blockchains.
Major
Blockchain Practical
M.Sc. IT Part II Sem IV
Course Outcomes:
- CO1: develop a mechanism for generating RSA key pairs, enabling users to securely encrypt and decrypt messages.
- CO2: explore the architecture and components of the Bitcoin blockchain, including blocks, transactions.
- CO3: demonstrate smart contracts and their deployment in decentralized applications (DApps).
- CO4: evaluate the security and privacy challenges in blockchain systems.
Major
Deep Learning
M.Sc. IT Part II Sem IV
Course Outcomes:
- CO1: Describe basics of mathematical foundation that will help the learner to understand the Concepts of Deep Learning.
- CO2: Understand and describe model of deep learning
- CO3: Understand various deep supervised learning architectures for text & image data.
- CO4: Gain knowledge about various deep learning models and architectures.
- CO5: Familiarize various deep learning techniques to design efficient algorithms for real-world applications.
Major
Deep Learning Practical
M.Sc. IT Part II Sem IV
Course Outcomes:
- CO1: Use tensors to implement deep learning algorithms and techniques.
- CO2: Apply deep neural network models.
- CO3: Analyze the impact of hyperparameter tuning on optimization.
- CO4: Evaluate and visualize the performance of the model.
Elective
Robotic Process Automation
M.Sc. IT Part II Sem IV
Course Outcomes:
- CO1: Recall and describe fundamental RPA and UiPath concepts, including key features of Studio and Orchestrator.
- CO2: Explain RPA principles and how UiPath facilitates process automation through its mechanisms.
- CO3: Interpret roles of components and tools in the UiPath ecosystem, understanding their functions.
- CO4: Summarize UiPath's data manipulation and integration capabilities for efficient automation.
- CO5: Utilize UiPath Studio to create workflows for simple business processes, incorporating data extraction and manipulation techniques.
Core
Research Project
M.Sc. IT Part II Sem IV
Course Outcomes:
- CO1: The student is expected to gain expertise in research methodologies, critical thinking, data analysis, and problem-solving, equipping them with skills essential for further academic or professional endeavors.
- CO2: The research contributes new knowledge, insights, or innovations in the chosen field, pushing the boundaries of understanding and addressing current challenges or gaps in the literature.
- CO3: Completing the research project enhances the student's academic credibility and professional skills, positioning them for leadership roles, higher positions in academia, or specialized careers in their field.
- CO4: The research might lead to published papers, conference presentations, or recognition within academic communities, establishing the student as a contributor to scholarly discourse and increasing visibility in their field.
- CO5: The project often has practical applications in solving real-world problems, influencing policies, technologies, or practices within industries, healthcare, social services, or public administration.
- CO6: Through the research process, students may collaborate with faculty, industry professionals, or other researchers, broadening their academic and professional network and gaining exposure to interdisciplinary work.
- CO7: The research outcomes can contribute to societal progress by addressing pressing global issues, such as sustainability, health, education, or technology, potentially influencing public policy, industry standards, and community practices.
- CO8: The postgraduate research project fosters self-reliance, perseverance, and the ability to manage complex tasks, helping students develop critical personal attributes like time management, resilience, and intellectual independence.