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BACHELOR-OF-SCIENCE in Computer Science at Dr. Ambedkar First Grade College (Evening College)
Bengaluru, Karnataka
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About the Specialization
What is Computer Science at Dr. Ambedkar First Grade College (Evening College) Bengaluru?
This Computer Science program at Dr. B.R. Ambedkar First Grade Evening College, affiliated with Bengaluru City University, focuses on foundational computing principles and advanced applications relevant to the Indian IT landscape. It emphasizes problem-solving, programming skills, and a deep understanding of core computer science domains, preparing students for diverse roles in India''''s booming technology sector. The curriculum is designed to foster innovation and critical thinking.
Who Should Apply?
This program is ideal for fresh graduates with a science background (PUC/10+2) seeking entry into the dynamic IT field in India. It also suits individuals passionate about technology, programming, and logical problem-solving, who aim for careers in software development, data science, cybersecurity, or research. Students aspiring to pursue higher education or entrepreneurship in tech will also find this program beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue robust career paths in India as Software Developers, Data Analysts, Web Developers, Network Engineers, or IT Support Specialists. Entry-level salaries typically range from INR 3-6 lakhs per annum, with significant growth potential up to INR 10-20 lakhs for experienced professionals in leading Indian and multinational companies. The program aligns with industry demands for skilled tech talent.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate extra time to core programming concepts (C, Java, Python). Utilize online platforms like HackerRank and CodeChef for daily practice. Understand data structures and algorithms thoroughly, as they are the bedrock for all advanced CS topics and critical for placement interviews.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, NPTEL Programming Courses
Career Connection
Strong fundamentals in programming and DSA are essential for clearing technical rounds in placements for any IT role, especially at companies like TCS, Infosys, Wipro, and numerous Indian startups. This builds a robust problem-solving ability.
Build a Portfolio of Mini-Projects- (Semester 1-2)
Beyond lab assignments, identify small problems and build practical solutions. Start with simple web pages using HTML/CSS/JavaScript, or command-line tools with Python. Document your code and host it on GitHub to showcase your abilities to potential employers.
Tools & Resources
GitHub, VS Code, FreeCodeCamp, The Odin Project
Career Connection
A demonstrable project portfolio significantly enhances your resume, providing tangible proof of your skills and initiative. It helps recruiters visualize your practical application of learned concepts, crucial for entry-level jobs in India.
Engage in Peer Learning and Technical Clubs- (Semester 1-2)
Form study groups to discuss complex topics and help each other with assignments. Join college technical clubs (e.g., Coding Club, AI/ML Interest Group) to participate in workshops, hackathons, and expand your network with like-minded peers and seniors.
Tools & Resources
College technical clubs, Discord/WhatsApp study groups, Local meetups
Career Connection
Peer learning improves understanding and communication skills, vital in team-based IT environments. Club participation provides exposure to diverse technologies and networking opportunities, which can lead to mentorship and job referrals in the Indian tech ecosystem.
Intermediate Stage
Focus on Domain Specialization and Certifications- (Semester 3-5)
By Semester 3-4, identify an area of interest (e.g., Web Development, Data Science, Cyber Security) and deep dive. Pursue online courses (Coursera, Udemy) and consider vendor-specific certifications (e.g., AWS Cloud Practitioner, Google Analytics) to validate your skills.
Tools & Resources
Coursera, Udemy, edX, NPTEL, AWS Certifications, Google Certifications
Career Connection
Specialized skills and certifications make you highly marketable for specific roles in the Indian IT industry, demonstrating expertise beyond generic degree knowledge. This can lead to better internship and job offers from niche companies.
Seek Out Summer Internships/Projects- (Semester 3-5)
Actively look for summer internships or industry projects starting from your second or third year. Platforms like Internshala or LinkedIn can be useful. Even unpaid internships offer invaluable practical experience and professional networking opportunities within the Indian market.
Tools & Resources
Internshala, LinkedIn, College placement cell
Career Connection
Internships are crucial for gaining real-world experience, making industry contacts, and often lead to pre-placement offers. This directly impacts your chances of securing a good job upon graduation in India, providing a competitive edge.
Participate in Coding Competitions & Hackathons- (Semester 3-5)
Regularly participate in online coding contests (e.g., Google Kick Start, ICPC) and college/inter-college hackathons. This sharpens your problem-solving skills under pressure, exposes you to new technologies, and allows you to work collaboratively on innovative projects.
Tools & Resources
Google Kick Start, ICPC, College Hackathons, Devfolio
Career Connection
Success in these competitions is highly valued by top tech companies in India, showcasing your competitive programming skills, innovation, and ability to perform under pressure. It''''s a direct indicator of your technical prowess and can open doors to prestigious companies.
Advanced Stage
Undertake a Significant Final Year Project- (Semester 6-8)
Choose a challenging final year project that aligns with your specialization. Aim for a real-world problem, collaborate with peers or mentors, and ensure it involves complex system design and implementation. Document it thoroughly and be ready to present it technically.
Tools & Resources
Open-source frameworks, Cloud platforms (AWS/Azure/GCP free tiers), Project management tools
Career Connection
A strong final year project is often a key talking point in interviews, demonstrating your ability to apply theoretical knowledge to practical, large-scale problems. It is a critical differentiator for placements, especially in product-based companies in India.
Intensive Placement Preparation & Mock Interviews- (Semester 6-8)
Begin placement preparation early in your final year. Practice aptitude tests, revise core CS subjects, and engage in multiple mock interviews (technical and HR) with faculty, seniors, or professional trainers. Focus on communication skills and body language.
Tools & Resources
Placement training cells, InterviewBit, Glassdoor, Mock interview platforms
Career Connection
Thorough preparation for placements is paramount for securing jobs in top-tier companies. Mock interviews help in identifying weak areas and building confidence, leading to successful navigation of the rigorous Indian campus recruitment process.
Network Strategically and Build Professional Brand- (Semester 6-8)
Attend industry seminars, workshops, and career fairs in Bengaluru. Connect with professionals on LinkedIn, seek mentorship, and actively engage in discussions. Cultivate a strong online professional presence showcasing your skills and achievements.
Tools & Resources
LinkedIn, Professional conferences (e.g., India AI Conference), Industry meetups
Career Connection
Networking opens doors to opportunities not advertised publicly, including off-campus placements and referrals. A strong professional brand enhances visibility and credibility, which is increasingly important for career progression in the competitive Indian tech job market.
Program Structure and Curriculum
Eligibility:
- As per Bengaluru City University norms (typically PUC/10+2 with Science stream)
Duration: 8 semesters (4 years for Honours)
Credits: 164 credits (for 4-year B.Sc. Honours) Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSC 1.1 | Discrete Structures | Core | 4 | Set Theory, Logic and Propositional Calculus, Relations and Functions, Graph Theory, Trees and Combinatorics |
| CSDSC 1.2 | Problem Solving Techniques | Core | 4 | Problem Solving Methodologies, Algorithm and Flowchart Design, Data Representation, Control Structures, Arrays and String Processing, Functions and Recursion |
| CSDSC 1.3 | Discrete Structures Lab | Lab | 2 | Implementing set operations, Logic gate simulations, Graph traversal algorithms, Tree manipulation algorithms |
| CSDSC 1.4 | Problem Solving Techniques Lab | Lab | 2 | Algorithm implementation using C/C++, Control flow exercises, Array and string manipulation programs, Basic searching and sorting |
| AECC 1.1 | English I | Ability Enhancement Compulsory Course | 2 | Basic English Grammar, Reading Comprehension, Paragraph and Essay Writing, Verbal Communication Skills, Introduction to Literary Texts |
| AECC 1.2 | Indian Language I | Ability Enhancement Compulsory Course | 2 | Grammar and Vocabulary, Prose and Poetry, Basic Communication in regional language, Cultural Context, Translation Exercises |
| CSSEC 1.1 | Office Automation Tools (Practical) | Skill Enhancement Course | 2 | Word Processing (MS Word), Spreadsheet Management (MS Excel), Presentation Design (MS PowerPoint), Database Management (MS Access), Mail Merge and Document Automation |
| OE 1.1 | Open Elective I | Open Elective | 3 | Topics vary based on chosen elective from university pool |
| VSC 1.1 | Vocational Course I | Vocational Skill | 3 | Topics vary based on chosen vocational course from university pool |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSC 2.1 | Data Structures | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees and Binary Search Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| CSDSC 2.2 | Object Oriented Programming using Java | Core | 4 | OOP Concepts: Encapsulation, Inheritance, Polymorphism, Classes, Objects, Methods, Packages and Interfaces, Exception Handling, Multithreading and File I/O |
| CSDSC 2.3 | Data Structures Lab | Lab | 2 | Implementing various data structures, Stack and queue applications, Linked list operations, Binary tree traversals, Sorting and searching algorithms implementation |
| CSDSC 2.4 | Object Oriented Programming Lab | Lab | 2 | Java program development for OOP concepts, GUI applications using Swing/AWT, File handling and database connectivity, Exception handling mechanisms |
| AECC 2.1 | English II | Ability Enhancement Compulsory Course | 2 | Advanced Grammar and Syntax, Report Writing and Official Correspondence, Creative Writing, Public Speaking and Presentation Skills, Literary Analysis and Criticism |
| AECC 2.2 | Indian Language II | Ability Enhancement Compulsory Course | 2 | Advanced Grammar and Usage, Literary Texts and Critical Appreciation, Formal Communication and Essay Writing, Cultural and Historical Contexts, Translation and Interpretation |
| CSSEC 2.1 | Web Designing (Practical) | Skill Enhancement Course | 2 | HTML for structure, CSS for styling, Introduction to JavaScript, Responsive Web Design, Basic Web Hosting Concepts |
| OE 2.1 | Open Elective II | Open Elective | 3 | Topics vary based on chosen elective from university pool |
| VSC 2.1 | Vocational Course II | Vocational Skill | 3 | Topics vary based on chosen vocational course from university pool |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSC 3.1 | Operating System | Core | 4 | Operating System Structures and Functions, Process Management and CPU Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency Control |
| CSDSC 3.2 | Database Management System | Core | 4 | DBMS Architecture and Data Models, Entity-Relationship (ER) Model, Relational Model and Algebra, Structured Query Language (SQL), Normalization and Transaction Management |
| CSDSC 3.3 | Operating System Lab | Lab | 2 | Linux/Unix Shell Commands, Shell Scripting, Process Creation and Management, CPU Scheduling Algorithms Simulation, Memory Allocation Algorithms |
| CSDSC 3.4 | Database Management System Lab | Lab | 2 | SQL Querying and DDL/DML Operations, Database Design and Implementation, Stored Procedures and Triggers, Report Generation with SQL |
| AECC 3.1 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Ecosystems and Biodiversity, Environmental Pollution and Control, Natural Resources and Conservation, Climate Change and Global Issues, Sustainable Development Practices |
| CSSEC 3.1 | Python Programming (Practical) | Skill Enhancement Course | 2 | Python Fundamentals and Data Types, Control Structures and Loops, Functions and Modules, List, Tuples, Dictionaries, Sets, File Handling and Exception Handling |
| OE 3.1 | Open Elective III | Open Elective | 3 | Topics vary based on chosen elective from university pool |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSC 4.1 | Computer Networks | Core | 4 | Network Topologies and Layered Architectures (OSI/TCP-IP), Data Link Layer Protocols, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, FTP, DNS) |
| CSDSC 4.2 | Computer Graphics | Core | 4 | Graphics Primitives and Rasterization, 2D and 3D Transformations, Viewing and Clipping, Projection Techniques, Color Models and Shading |
| CSDSC 4.3 | Computer Networks Lab | Lab | 2 | Network Configuration Commands, Socket Programming, Packet Analysis using Wireshark, Simulating Network Protocols, Client-Server Application Development |
| CSDSC 4.4 | Computer Graphics Lab | Lab | 2 | OpenGL Programming, Drawing Basic Geometric Primitives, Implementing Transformations, Interactive Graphics Applications |
| AECC 4.1 | Indian Constitution | Ability Enhancement Compulsory Course | 2 | Historical Background of Indian Constitution, Preamble and Fundamental Rights, Directive Principles of State Policy, Structure and Functions of Union Government, State Government and Local Administration |
| CSSEC 4.1 | Android Programming (Practical) | Skill Enhancement Course | 2 | Introduction to Android SDK, Android Activities and Layouts, UI Widgets and Event Handling, Data Storage (SQLite, SharedPreferences), Permissions and Notifications |
| OE 4.1 | Open Elective IV | Open Elective | 3 | Topics vary based on chosen elective from university pool |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSE 5.1 | Software Engineering | Discipline Specific Elective | 4 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing and Quality Assurance, Software Project Management |
| CSDSE 5.2 | Theory of Computation | Discipline Specific Elective | 4 | Finite Automata and Regular Expressions, Context-Free Grammars and Pushdown Automata, Turing Machines and Computability, Decidability and Undecidability, Complexity Classes (P, NP) |
| CSDSE 5.3 | Software Engineering Lab | Lab | 2 | UML Diagramming Tools, Requirement Gathering and Analysis Tools, Software Testing Frameworks, Version Control Systems |
| CSDSE 5.4 | Theory of Computation Lab | Lab | 2 | Simulating Finite Automata, Implementing Context-Free Grammar Parsers, Designing Turing Machine Simulators, Formal Language Operations |
| CSDSE 5.5 | Artificial Intelligence | Discipline Specific Elective | 3 | Introduction to AI Agents, Search Algorithms (DFS, BFS, A*), Knowledge Representation and Reasoning, Machine Learning Basics, Expert Systems and Robotics |
| CSDSE 5.6 | Data Mining | Discipline Specific Elective | 3 | Data Preprocessing and Data Warehousing, Association Rule Mining, Classification Algorithms, Clustering Techniques, Web Mining and Text Mining |
| CSDSE 5.7 | Artificial Intelligence Lab | Lab | 1 | Implementing Search Algorithms, Prolog Programming for Logic, Introduction to AI/ML Libraries (e.g., scikit-learn) |
| CSDSE 5.8 | Data Mining Lab | Lab | 1 | Using Data Mining Tools (e.g., Weka), Implementing Association Rule Algorithms, Applying Classification and Clustering, Data Visualization |
| OE 5.1 | Open Elective V | Open Elective | 3 | Topics vary based on chosen elective from university pool |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSE 6.1 | Full Stack Development | Discipline Specific Elective | 4 | Frontend Technologies (HTML, CSS, JavaScript), Backend Development (Node.js/Python frameworks), Database Management (SQL/NoSQL), RESTful APIs and Web Services, Deployment and Version Control |
| CSDSE 6.2 | Cryptography and Network Security | Discipline Specific Elective | 4 | Symmetric and Asymmetric Cryptography, Hash Functions and Digital Signatures, Network Security Protocols (SSL/TLS, IPSec), Firewalls and Intrusion Detection Systems, Malware and Cyber Attacks |
| CSDSE 6.3 | Full Stack Development Lab | Lab | 2 | Building Responsive Web Interfaces, Developing Backend APIs, Database Integration, User Authentication and Authorization, Deployment to Cloud Platforms |
| CSDSE 6.4 | Cryptography and Network Security Lab | Lab | 2 | Implementing Cryptographic Algorithms, Network Scanning Tools (Nmap), Vulnerability Assessment Tools, Firewall Configuration, Packet Analysis with Wireshark |
| CSDSE 6.5 | Cloud Computing | Discipline Specific Elective | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization and Containerization, Cloud Security Challenges, Introduction to AWS/Azure/GCP Services |
| CSDSE 6.6 | Machine Learning | Discipline Specific Elective | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Feature Engineering, Introduction to Deep Learning |
| CSDSE 6.7 | Cloud Computing Lab | Lab | 1 | Deploying Applications on Cloud Platforms, Virtual Machine Management, Cloud Storage Services, Container Orchestration (Docker/Kubernetes Basics) |
| CSDSE 6.8 | Machine Learning Lab | Lab | 1 | Implementing ML Algorithms with Python (Scikit-learn), Data Preprocessing Techniques, Model Training and Testing, Data Visualization for ML |
| OE 6.1 | Open Elective VI | Open Elective | 3 | Topics vary based on chosen elective from university pool |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSE 7.1 | Big Data Analytics | Discipline Specific Elective | 4 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Spark for Data Processing, NoSQL Databases, Data Warehousing for Big Data |
| CSDSE 7.2 | Internet of Things | Discipline Specific Elective | 4 | IoT Architecture and Components, Sensors, Actuators, and Microcontrollers, IoT Communication Protocols, IoT Platforms (e.g., Arduino, Raspberry Pi), IoT Data Analytics and Security |
| CSDSE 7.3 | Big Data Analytics Lab | Lab | 2 | Setting up Hadoop Cluster, MapReduce Programming, Spark Application Development, Working with Hive and Pig, Analyzing Large Datasets |
| CSDSE 7.4 | Internet of Things Lab | Lab | 2 | Interfacing Sensors and Actuators, Programming Microcontrollers, Connecting Devices to IoT Platforms, Building Smart Home/Environment Prototypes |
| CSDSE 7.5 | Quantum Computing | Discipline Specific Elective | 3 | Quantum Bits (Qubits) and Superposition, Quantum Entanglement, Quantum Gates and Circuits, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Computing Hardware |
| CSDSE 7.6 | Digital Image Processing | Discipline Specific Elective | 3 | Image Representation and Fundamentals, Image Enhancement Techniques, Image Restoration and Filtering, Image Segmentation, Feature Extraction and Object Recognition |
| CSDSE 7.7 | Quantum Computing Lab | Lab | 1 | Qiskit/Cirq Programming, Simulating Quantum Circuits, Implementing Basic Quantum Algorithms, Quantum Error Correction Concepts |
| CSDSE 7.8 | Digital Image Processing Lab | Lab | 1 | Image Manipulation using OpenCV/MATLAB, Applying Filters and Transformations, Implementing Segmentation Algorithms, Feature Detection and Extraction |
| CSDSE 7.9 | Project Work (Minor Project) | Project | 3 | Project Planning and Scoping, Literature Survey and Problem Definition, System Design and Architecture, Implementation and Testing, Report Writing and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDSE 8.1 | Deep Learning | Discipline Specific Elective | 4 | Neural Network Architectures, Backpropagation and Optimization, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Generative Adversarial Networks (GANs) |
| CSDSE 8.2 | Ethical Hacking & Cyber Forensics | Discipline Specific Elective | 4 | Introduction to Ethical Hacking, Penetration Testing Phases, Vulnerability Assessment, Cyber Forensics Fundamentals, Incident Response and Digital Evidence |
| CSDSE 8.3 | Deep Learning Lab | Lab | 2 | Implementing Neural Networks with TensorFlow/Keras, Image Classification using CNNs, Natural Language Processing with RNNs, Model Training and Hyperparameter Tuning |
| CSDSE 8.4 | Ethical Hacking & Cyber Forensics Lab | Lab | 2 | Using Kali Linux Tools for Hacking, Network Reconnaissance and Scanning, Vulnerability Exploitation, Forensic Data Acquisition and Analysis |
| CSDSE 8.5 | Augmented Reality & Virtual Reality | Discipline Specific Elective | 3 | Fundamentals of AR/VR Technologies, 3D Graphics and Rendering for AR/VR, Interaction Techniques in AR/VR, AR/VR Development Platforms (Unity/Unreal), Applications of AR/VR |
| CSDSE 8.6 | Natural Language Processing | Discipline Specific Elective | 3 | Text Preprocessing and Tokenization, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation and Chatbots |
| CSDSE 8.7 | Augmented Reality & Virtual Reality Lab | Lab | 1 | Unity/Unreal Engine for AR/VR, Creating Virtual Environments, Developing AR Applications with Markers, Interacting with Virtual Objects |
| CSDSE 8.8 | Natural Language Processing Lab | Lab | 1 | NLTK/SpaCy for Text Processing, Implementing Sentiment Analysis Models, Building Simple Chatbots, Word Embeddings and Vector Spaces |
| CSDSE 8.9 | Project Work (Major Project) | Project | 5 | Advanced Project Design and Development, Research Methodology, System Implementation and Integration, Comprehensive Testing and Validation, Technical Report Writing and Thesis Defense |
| CSDSE 8.10 | Internship/Apprenticeship | Internship | 4 | Industry Exposure, Practical Skill Application in a Professional Setting, Problem Solving in Real-world Scenarios, Professional Networking and Ethics, Internship Report and Presentation |
