The Bachelor of Science (B.Sc.) & Master of Science (M.Sc.) in Computer Science Integrated program offers an in-depth curriculum that covers both foundational and advanced concepts in computer science. Students gain expertise in areas like programming, AI, machine learning, data science, and cybersecurity, preparing them for leadership roles in the technology industry.
Bachelor of Science (B.Sc.) in Computer Science - Years 1 to 3
Year
Subject
Description
Year 1
Introduction to Computer Science
Overview of computer science, its history, and applications. Basic concepts in computing and problem-solving techniques.
Programming Languages
Introduction to programming in Python, C++, and Java. Focus on writing basic programs and solving problems.
Discrete Mathematics
Mathematical structures and techniques in logic, sets, and combinatorics. Essential for understanding algorithms and problem-solving.
Computer Organization and Architecture
Study of computer hardware, memory management, and processor architecture.
Data Structures and Algorithms
Data organization, including arrays, linked lists, trees, graphs, and algorithmic strategies like sorting and searching.
Computer Networks
Fundamentals of networking, including protocols, OSI model, and the internet.
Mathematical Logic
Study of formal logic, proof methods, and its application in algorithms and theoretical computer science.
Year
Subject
Description
Year 2
Database Management Systems
Introduction to databases, SQL, data modeling, and database design principles. Concepts like normalization and relational databases.
Software Engineering
Overview of the software development life cycle (SDLC), including requirements gathering, design, implementation, and testing.
Operating Systems
Core concepts of operating systems such as process management, memory management, file systems, and scheduling.
Web Technologies
Basics of web development, including HTML, CSS, and JavaScript. Introduction to server-side scripting.
Object-Oriented Programming (OOP)
Deep dive into OOP concepts like encapsulation, inheritance, polymorphism, and abstraction using Java or C++.
Computer Graphics
Principles of 2D and 3D graphics, rendering, and animation techniques.
Probability and Statistics
Statistical methods for data analysis and their applications in machine learning and data science.
Year
Subject
Description
Year 3
Advanced Data Structures
Study of advanced data structures like heaps, hashing, balanced trees, and their applications in problem-solving.
Artificial Intelligence (AI)
Introduction to AI techniques like search algorithms, knowledge representation, and machine learning.
Design and Analysis of Algorithms
Advanced techniques for analyzing the complexity of algorithms, including greedy algorithms, dynamic programming, and divide-and-conquer.
Cybersecurity
Key concepts in network security, cryptography, ethical hacking, and firewalls.
Distributed Systems
Study of distributed computing concepts, cloud computing, and distributed algorithms.
Mobile Application Development
Introduction to developing mobile applications for Android or iOS, focusing on user interface (UI) design and UX.
Internship/Project
Practical hands-on experience, working in teams to develop software or solutions for real-world problems.
Master of Science (M.Sc.) in Computer Science - Years 4 and 5
Year
Subject
Description
Year 4
Machine Learning
In-depth study of supervised and unsupervised learning, neural networks, and deep learning algorithms.
Cloud Computing
Principles and architectures of cloud computing, including virtualization, cloud storage, and cloud service models (IaaS, PaaS, SaaS).
Big Data Analytics
Techniques for processing and analyzing large-scale data sets using tools like Hadoop, Spark, and NoSQL databases.
Data Science
Study of statistical and computational methods for analyzing large amounts of data. Topics include data wrangling, predictive modeling, and data visualization.
Computer Vision
Introduction to the field of computer vision, including image processing, feature extraction, and object recognition.
Natural Language Processing (NLP)
Overview of NLP concepts, including text mining, sentiment analysis, and language models.
Research Methodology
Techniques for conducting research in computer science, including literature review, hypothesis formulation, and data analysis.
Year
Subject
Description
Year 5
Advanced Topics in AI
Specialized courses in AI domains, such as reinforcement learning, computer vision, and robotics.
Blockchain Technology
Study of blockchain architecture, cryptocurrency, and its applications in various industries.
Advanced Software Engineering
Focus on modern software development techniques, agile methodologies, and DevOps practices.
Advanced Cybersecurity
Techniques for securing cloud infrastructure, network security, and cryptographic protocols.
Capstone Project/Thesis
Research-based project in collaboration with industry or academic mentors. Students are required to apply advanced concepts learned throughout the program to solve real-world problems.
Electives (based on specialization)
Students choose advanced elective courses in areas like data mining, advanced databases, quantum computing, or internet of things (IoT).
Key Skills and Tools Covered Throughout the Program:
Tools and Skills
Description
Programming
Proficiency in Python, C++, Java, R, and SQL.
Software Development Tools
Familiarity with IDEs, version control systems (Git), and debugging techniques.
Data Handling
Knowledge of tools like Excel, Tableau, and Power BI for data visualization.
Frameworks and Libraries
Use of libraries like TensorFlow, PyTorch, and Scikit-learn for AI and machine learning.
Project Management Tools
Exposure to tools like Jira, Trello, and Agile methodologies.
