Gujarat University
Program Code
Level
Duration
Department name
| Semester | Sr no | CourseCode | Course | CourseCredit |
|---|---|---|---|---|
| Sem-1 | 1 | EC-101 | Yoga | 2 |
| Sem-1 | 2 | FC-101 | Communication Skills | 2 |
| Sem-1 | 3 | CC-101 | Mathematical Basics | 3 |
| Sem-1 | 4 | CC-102 | Descriptive Statistics and Probability | 3 |
| Sem-1 | 5 | CC-103 | Introduction to Artificial Intelligence & | 3 |
| Sem-1 | 6 | CC-103_1 | Machine Learning | 3 |
| Sem-1 | 7 | CC-104 | Basics of Computer Organization & | 3 |
| Sem-1 | 8 | CC-104_1 | Architecture | 3 |
| Sem-1 | 9 | CC-104_2 | Information and Communication | 3 |
| Sem-1 | 10 | CC_105_2 | Technology (ICT) | 3 |
| Sem-1 | 11 | CC-106 (P) | Practical Based on CC-101& CC-102 | 3 |
| Sem-1 | 12 | CC-107 (P) | Practical Based on CC-105 | 3 |
| Sem-10 | 1 | CC-511 | Dissertation/ Project Work | 16 |
| Sem-10 | 2 | CC-512 | Seminar/ Symposium/ Conference | 4 |
| Sem-10 | 3 | CC-513 | Research Paper/ MOOCs/ Internship/ OJT | 5 |
| Sem-2 | 1 | EC-111 | Environmental Studies | 2 |
| Sem-2 | 2 | FC-111 | Commercial Communication | 2 |
| Sem-2 | 3 | CC-111 | Calculus and Introduction to Matrices | 3 |
| Sem-2 | 4 | CC-112 | Numerical and Statistical Methods | 3 |
| Sem-2 | 5 | CC-113 | Algorithms and Data Structures | 3 |
| Sem-2 | 6 | CC-114 | Object-Oriented Programming with JAVA | 3 |
| Sem-2 | 7 | CC-115 | Programming with PYTHON | 3 |
| Sem-2 | 8 | CC-116 (P) | Practical Based on CC-114 | 3 |
| Sem-2 | 9 | CC-117 (P) | Practical Based on CC-115 | 3 |
| Sem-3 | 1 | FC-201 | Soft Skills I | 2 |
| Sem-3 | 2 | CC-201 | Matrix Algebra and Calculus | 3 |
| Sem-3 | 3 | CC-202 | Random Variable and Distributions | 3 |
| Sem-3 | 4 | CC-203 | Discrete Mathematics | 3 |
| Sem-3 | 5 | CC-204 | Operating System Concepts | 4 |
| Sem-3 | 6 | CC-205 | Database Management Systems using SQL and PL/SQL | 4 |
| Sem-3 | 7 | CC-206 (P) | Data Analysis using Excel | 3 |
| Sem-3 | 8 | CC-207 (P) | Practical Based on CC-205 | 3 |
| Sem-4 | 1 | FC-211 | Soft Skills II | 2 |
| Sem-4 | 2 | CC-211 | Linear Algebra | 3 |
| Sem-4 | 3 | CC-212 | Statistical Inference Theory | 3 |
| Sem-4 | 4 | CC-213 | Vector Calculus | 3 |
| Sem-4 | 5 | CC-214 | Introduction to Natural Language Processing | 4 |
| Sem-4 | 6 | CC-215 | Python for Machine Learning | 4 |
| Sem-4 | 7 | CC-216 (P) | R Programming – I | 3 |
| Sem-4 | 8 | CC-217 (P) | Practical Based on CC-215 | 3 |
| Sem-5 | 1 | FC-301 | Scientific Writing | 2 |
| Sem-5 | 2 | CC-301 | Differential Equations | 3 |
| Sem-5 | 3 | CC-302 | Regression Theory | 3 |
| Sem-5 | 4 | CC-303 | Distributed Platforms | 4 |
| Sem-5 | 5 | CC-304 | Supervised Machine Learning | 4 |
| Sem-5 | 6 | CC-305 (P) | Data Visualization | 3 |
| Sem-5 | 7 | CC-306 (P) | Practical Based on CC-303 | 3 |
| Sem-5 | 8 | CC-307 (P) | Practical Based on CC-304 | 3 |
| Sem-6 | 1 | FC-311 | Personality Development | 2 |
| Sem-6 | 2 | CC-311 | Operations Research | 4 |
| Sem-6 | 3 | CC-312 | Research Methodology | 3 |
| Sem-6 | 4 | CC-313 | Unsupervised Machine Learning | 4 |
| Sem-6 | 5 | CC-314 & CC-315 | PROJECT – I: Mini-Project | 6 |
| Sem-6 | 6 | CC-316 (P) | R Programming – II | 3 |
| Sem-6 | 7 | CC-317 (P) | Practical Based on CC-313 | 3 |
| Sem-7 | 1 | CC-401 | Advanced Algorithms | 4 |
| Sem-7 | 2 | CC-402 | Image Processing | 4 |
| Sem-7 | 3 | CC-403 | Deep Learning | 4 |
| Sem-7 | 4 | CC-404 (P) | Practical Based on CC-403 | 4 |
| Sem-7 | 5 | CC-405 (P) | Power BI & Tableau | 4 |
| Sem-7 | 6 | CC-406 | PROJECT – II: Project | 5 |
| Sem-8 | 1 | CC-411 | Multivariate Analysis | 4 |
| Sem-8 | 2 | CC-412 | Cloud Computing | 4 |
| Sem-8 | 3 | CC-413 | Big Data Analytics | 4 |
| Sem-8 | 4 | CC-414 | Advanced NLP | 4 |
| Sem-8 | 5 | CC-415 (P) | Practical Based on CC-414 | 4 |
| Sem-8 | 6 | CC-416 | PROJECT – III: Project | 5 |
| Sem-9 | 1 | CC-501 | Cyber Security | 4 |
| Sem-9 | 2 | CC-502 | Cloud Computing-II | 4 |
| Sem-9 | 3 | CC-503 | Blockchain Technology | 4 |
| Sem-9 | 4 | CC-504 | Reinforcement Learning | 4 |
| Sem-9 | 5 | CC-505 (P) | Practical Based on CC-504 | 4 |
| Sem-9 | 6 | CC-506 | PROJECT – IV: Project | 5 |
| CourseType | Shift | |||||||
|---|---|---|---|---|---|---|---|---|
| General | EWS | SEBC | SC | ST | Male | Female | ||
| HPP/Self Finance | Noon | 60 | 6 | 12 | 3 | 3 | 37600 | 37600 |
| HPP/Self Finance | Noon | 45 | 3 | 6 | 0 | 3 | 37600 | 37600 |
Statements: 1. Proficiency in Verbal and Non-Verbal Communication: Students will enhance their ability to effectively communicate ideas and information through both verbal and non-verbal means, including active listening, clear articulation, body language, and tone of voice. 2. Writing Skills for Various Contexts: Students will develop strong writing skills tailored to different contexts, such as academic, professional, and informal settings, with a focus on structure, clarity, grammar, and audience appropriateness. 3. Critical Thinking and Interpersonal Communication: Students will improve their critical thinking abilities and interpersonal communication skills, enabling them to engage in meaningful dialogue, resolve conflicts, and collaborate effectively in team settings.
Statements: Introduction to Artificial Intelligence • Understand the history, evolution, and key concepts of AI. • Explain various AI techniques and their applications in solving real-world problems. • Develop simple AI models using basic algorithms and evaluate their performance.
Statements: • Apply supervised, unsupervised, and reinforcement learning techniques to solve problems. • Implement algorithms such as regression, classification, clustering, and decision trees. • Evaluate model performance using appropriate metrics and techniques.
Statements: 1. Understanding of Computer Architecture: Students will gain a foundational understanding of computer architecture, including the basic components of a computer system such as the CPU, memory, and I/O devices, and how they interact to perform tasks. 2. Knowledge of Data Representation and Instruction Set Architecture (ISA): Students will learn how data is represented at the machine level, including binary, hexadecimal, and ASCII representations, and will understand the basics of Instruction Set Architecture (ISA), including how instructions are fetched, decoded, and executed by the CPU. 3. Comprehension of Assembly Language and Machine-Level Programming: Students will develop skills in writing and understanding simple assembly language programs, gaining insight into how high-level code is translated into machine-level instructions that the computer hardware can execute.
Statements: 1. Effective Communication Skills: Students will develop the ability to craft clear, concise, and persuasive communications, including emails, reports, proposals, and presentations, tailored to diverse professional audiences. 2. Mastery of Communication Strategies: Students will learn to apply various communication strategies and techniques for different commercial contexts, such as negotiations, client interactions, and team collaboration, with an emphasis on clarity, tone, and professionalism. 3. Understanding of Digital and Cross-Cultural Communication: Students will gain insights into the role of digital tools and platforms in commercial communications, and will be equipped to navigate and adapt their communication style for global and cross-cultural business environments.
Statements: 1. Mastery of Fundamental Calculus Concepts: Students will develop a solid understanding of key calculus concepts, including limits, derivatives, integrals, and their applications in problem-solving, such as finding rates of change and areas under curves. 2. Ability to Solve Systems Using Matrices: Students will learn to perform basic operations with matrices, including addition, multiplication, and inversion, and will be able to apply these skills to solve systems of linear equations using techniques like Gaussian elimination and matrix inversion. 3. Application of Calculus and Matrices in Real-World Scenarios: Students will be able to apply calculus and matrix methods to model and solve practical problems in various fields, such as physics, engineering, economics, and data analysis, enhancing their analytical and problem-solving skills.
Statements: 1. Foundational Understanding: Students will develop a strong foundation in the principles of data structures and algorithms, including the ability to implement and utilize common data structures like arrays, linked lists, stacks, queues, trees, and graphs. 2. Algorithmic Problem-Solving: Students will be able to design, implement, and analyze algorithms for a variety of computational problems, applying techniques such as sorting, searching, recursion, and basic algorithmic paradigms like greedy and divide-and-conquer. 3. Complexity Analysis Skills: Students will learn to perform time and space complexity analysis on algorithms, enabling them to evaluate efficiency and optimize code performance for different data structures and algorithms.
Statements: 1. Proficiency in Excel Functions and Tools: Students will gain proficiency in using essential Excel functions and tools for data analysis, including formulas, pivot tables, charts, and conditional formatting, to organize, manipulate, and visualize data effectively. 2. Data Cleaning and Preparation Skills: Students will learn techniques for cleaning and preparing datasets in Excel, such as handling missing values, removing duplicates, and performing data validation, to ensure data accuracy and reliability for analysis. 3. Application of Data Analysis Techniques: Students will develop the ability to apply various data analysis techniques in Excel, such as descriptive statistics, trend analysis, and data modeling, to draw meaningful insights and support decision-making in real-world scenarios.
Statements: • Understand key concepts in NLP, such as tokenization, stemming, and lemmatization. • Implement NLP tasks including sentiment analysis, text classification, and machine translation. • Apply language models and evaluate their performance using NLP-specific metrics.
Statements: 1. Understanding of Data Visualization Principles: Students will learn the fundamental principles of data visualization, including the effective use of color, design, and chart types, to create clear and impactful visual representations of data. 2. Proficiency in Visualization Tools and Techniques: Students will develop skills in using data visualization tools (such as Tableau, Power BI, or Python libraries like Matplotlib and Seaborn) to design and implement a variety of visualizations, including bar charts, line graphs, scatter plots, and dashboards. 3. Ability to Communicate Insights Visually: Students will be able to translate complex data into easy-to-understand visual narratives, allowing them to effectively communicate insights and findings to diverse audiences, including stakeholders and decision-makers.
Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project
Statements: 1. Design and Analysis Proficiency: Students will be able to design efficient algorithms for complex computational problems and rigorously analyze their time and space complexities using advanced techniques. 2. Algorithmic Paradigms Mastery: Students will gain a deep understanding of advanced algorithmic paradigms such as dynamic programming, greedy algorithms, and divide-and-conquer, and will be able to apply these paradigms to solve novel and complex problems. 3. Optimization and Approximation Skills: Students will develop the ability to solve optimization problems using advanced algorithms, including approximation and randomized algorithms, and will be able to evaluate the trade-offs between exact and heuristic approaches.
Statements: • Understand the architecture and functioning of neural networks, including feedforward, convolutional, and recurrent networks. • Implement deep learning models using frameworks like TensorFlow or PyTorch. • Apply deep learning techniques to tasks such as image recognition, natural language processing, and time-series analysis.
Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project
Statements: • Demonstrate the ability to use cloud-based AI/ML services (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) for building, training, and deploying machine learning models efficiently. • Design and implement scalable AI/ML solutions in the cloud, leveraging cloud-native tools and technologies such as serverless computing, auto-scaling, and distributed computing to handle large-scale data and complex model requirements. • Analyze and apply best practices for managing data and ensuring security in cloud-based AI/ML projects, including data storage, processing, privacy, compliance, and access control within cloud environments.
Statements: • Understand the challenges and techniques for handling large-scale data in AI systems. • Use big data tools like Hadoop, Spark, and NoSQL databases for data management and processing. • Design scalable AI solutions that can handle high volumes of data efficiently.
Statements: • Implement NLP tasks including sentiment analysis, text classification, and machine translation. • Apply language models and evaluate their performance using NLP-specific metrics.
Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project
Statements: • Identify and analyze various cyber threats (e.g., malware, phishing, ransomware) and implement appropriate defense mechanisms, such as firewalls, intrusion detection systems, and antivirus software. • Develop and apply security policies, procedures, and best practices to protect information systems, ensuring confidentiality, integrity, and data availability. • Demonstrate proficiency in network security techniques, including secure network design, encryption, cryptographic algorithms, and secure communication protocols. • Formulate and execute incident response plans to detect, respond to, and recover from cyber security incidents, including conducting forensic analysis and reporting. • Conduct ethical hacking and penetration testing to identify vulnerabilities in systems and applications, using industry-standard tools and techniques. • Evaluate legal, regulatory, and compliance requirements related to cyber security, including data protection laws, ethical considerations, and international standards.
Statements: • Demonstrate the ability to use cloud-based AI/ML services (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) for building, training, and deploying machine learning models efficiently. • Design and implement scalable AI/ML solutions in the cloud, leveraging cloud-native tools and technologies such as serverless computing, auto-scaling, and distributed computing to handle large-scale data and complex model requirements. • Analyze and apply best practices for managing data and ensuring security in cloud-based AI/ML projects, including data storage, processing, privacy, compliance, and access control within cloud environments.
Statements: • Explain the concepts of agents, states, actions, rewards, and policies in reinforcement learning. • Implement reinforcement learning algorithms such as Q-learning and Deep Q-Networks (DQNs). • Apply reinforcement learning to solve complex decision-making problems.
Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project.