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B-SC-HONS-COMPUTER-SCIENCE in Computer Science at Sri Guru Tegh Bahadur Khalsa College
Delhi, Delhi
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About the Specialization
What is Computer Science at Sri Guru Tegh Bahadur Khalsa College Delhi?
This Computer Science program at Sri Guru Tegh Bahadur Khalsa College focuses on building a strong foundation in theoretical and practical aspects of computing. It''''s designed to equip students with critical thinking and problem-solving skills highly relevant to India''''s burgeoning IT sector. The curriculum covers core areas like programming, data structures, algorithms, databases, and emerging fields like AI and Machine Learning.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and logical reasoning, aspiring to build a career in software development, data science, or research. It''''s also suitable for students keen on pursuing higher education (M.Sc, MCA) or competitive exams in computer science. Basic programming exposure is beneficial but not strictly required.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Software Developers, Data Analysts, Web Developers, or Junior AI Engineers. Entry-level salaries typically range from INR 3.5-6 LPA, with significant growth potential up to INR 10-15 LPA with experience. The program provides a solid base for industry-recognized certifications in programming languages, cloud platforms, and data analytics.
Student Success Practices
Foundation Stage
Master Programming Fundamentals with Daily Practice- (Semester 1-2)
Dedicate 1-2 hours daily to coding practice on platforms like HackerRank, LeetCode (easy problems), or CodeChef, focusing on Python and C++. Understand fundamental data types, control flow, functions, and basic algorithms. Regularly review class assignments and solve additional problems.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks Python tutorials
Career Connection
Strong coding fundamentals are crucial for technical interviews and form the bedrock for advanced topics. This practice builds problem-solving ability, essential for any software development role.
Build a Strong Mathematical & Logical Foundation- (Semester 1-2)
Actively engage with Discrete Mathematics and Mathematics for Computing courses. Solve problems from textbooks and online resources like Khan Academy. Focus on logic, set theory, graph theory, and combinatorics. Participate in quizzes or puzzle-solving groups.
Tools & Resources
Khan Academy, NPTEL videos for Discrete Mathematics, Relevant textbooks
Career Connection
Mathematical and logical reasoning is vital for algorithm design, data analysis, and understanding complex computer science concepts, directly impacting roles in AI, Machine Learning, and core development.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups to discuss complex topics, share understanding, and work together on small programming assignments. Initiate mini-projects with classmates, even simple ones like a command-line calculator or a basic game, to apply learned concepts in a team setting.
Tools & Resources
GitHub for version control, Google Docs for collaborative documentation, College library study rooms
Career Connection
Collaboration skills are highly valued in the IT industry. Working on projects with peers simulates real-world team environments and helps in building a portfolio for internships.
Intermediate Stage
Deep Dive into Data Structures and Algorithms (DSA)- (Semester 3-5)
Beyond theoretical understanding, implement various data structures (linked lists, trees, graphs) and algorithms (sorting, searching, dynamic programming) from scratch. Regularly participate in competitive programming challenges and online contests to hone your problem-solving speed and efficiency.
Tools & Resources
LeetCode (medium-hard), Codeforces, InterviewBit, Grokking Algorithms book
Career Connection
DSA proficiency is the single most critical skill for securing placements in top tech companies in India. Mastering it unlocks opportunities for Software Engineer, Backend Developer, and Algorithm Engineer roles.
Undertake Mini-Projects and Internships- (Semester 3-5)
Apply classroom knowledge by building practical projects in areas like web development, app development, or data management. Seek out summer internships or part-time projects to gain industry exposure and network with professionals. Even non-paid projects add immense value.
Tools & Resources
GitHub, LinkedIn for internship search, Online project platforms (e.g., freeCodeCamp, The Odin Project)
Career Connection
Practical project experience and internships are essential for converting theoretical knowledge into marketable skills, making your resume stand out for placements and entry-level jobs in India''''s competitive job market.
Explore and Specialize in Elective Domains- (Semester 3-5)
Carefully choose your Skill Enhancement and Discipline Specific Elective courses based on your interest (e.g., AI, Machine Learning, Web Design, Cloud Computing). Supplement these with online courses or certifications from platforms like Coursera, Udemy, or NPTEL to build specialized expertise in a chosen domain.
Tools & Resources
Coursera, Udemy, NPTEL, FreeCodeCamp, HackerEarth challenges
Career Connection
Early specialization helps in identifying a niche and building a stronger profile for specific roles like AI Engineer, Data Scientist, or Cloud Architect, which are in high demand in the Indian tech industry.
Advanced Stage
Focus on Capstone Project and Portfolio Building- (Semester 6)
Undertake a significant Capstone Project or Dissertation in your final year, ideally solving a real-world problem or exploring a research question. Document your project thoroughly on GitHub and create a professional online portfolio or personal website showcasing your skills and projects.
Tools & Resources
GitHub Pages, LinkedIn profiles, Personal website platforms (e.g., WordPress, Squarespace)
Career Connection
A strong capstone project and well-maintained portfolio are invaluable assets for job applications and interviews, providing tangible evidence of your abilities and dedication to potential employers in India.
Intensive Placement Preparation and Mock Interviews- (Semester 6)
Engage in rigorous placement preparation, including aptitude tests, logical reasoning, verbal ability, and technical interview rounds. Participate in mock interviews with faculty, alumni, or professional platforms to refine your communication, technical, and behavioral skills. Research target companies thoroughly.
Tools & Resources
GeeksforGeeks interview preparation, Aptitude test platforms, Mock interview services, Company-specific preparation guides
Career Connection
Comprehensive preparation is key to navigating the highly competitive Indian placement landscape, maximizing your chances of securing offers from reputable companies during campus drives.
Network Actively and Seek Mentorship- (Semester 6)
Attend industry seminars, tech conferences, and alumni meetups. Connect with professionals, seniors, and alumni on LinkedIn. Seek mentorship from experienced individuals in your target domain for career guidance, insights into industry trends, and potential job leads.
Tools & Resources
LinkedIn, College alumni network portals, Tech event platforms (e.g., Meetup, Eventbrite)
Career Connection
Networking opens doors to hidden job opportunities, valuable career advice, and professional development, significantly enhancing your long-term career prospects in the Indian IT ecosystem.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with one Language, Mathematics, and any two subjects from Physics, Chemistry, Computer Science/Informatics Practices. Mathematics is mandatory. Admission based on CUET scores. Minimum 40% in theory component of qualifying examination for General category.
Duration: 3 years (6 semesters)
Credits: 164 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS.DSC.1.1 | Programming using Python | Core (DSC) | 4 | Python basics and syntax, Data types, operators and expressions, Control flow statements (if, else, loops), Functions, modules and packages, Lists, tuples, dictionaries, sets, File handling and exceptions |
| CS.DSC.1.2 | Computer System Architecture | Core (DSC) | 4 | Digital logic circuits (Boolean algebra, gates), Combinational and sequential circuits, Processor organization (ALU, registers), Memory hierarchy and cache, Input/Output organization, Instruction set architecture |
| CS.DSC.1.3 | Mathematics for Computing | Core (DSC) | 4 | Set theory and functions, Mathematical logic and proofs, Relations and partial orderings, Basic graph theory, Counting principles and probability, Introduction to matrices |
| GE-1 | Generic Elective Course - I | Generic Elective (GE) | 4 | Topics depend on the specific course chosen from the university-wide pool. |
| AECC-1 | Environmental Science | Ability Enhancement Compulsory Course (AECC) | 2 | Ecosystems and biodiversity, Natural resources and sustainable development, Environmental pollution and control, Global environmental issues, Environmental policies and practices |
| VAC-1 | Value Addition Course - I | Value Addition Course (VAC) | 2 | Topics depend on the specific course chosen from the university-wide pool. |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS.DSC.2.1 | Data Structures | Core (DSC) | 4 | Arrays, linked lists, stacks, queues, Trees (binary, BST, AVL), Graphs (representations, traversals), Sorting algorithms (bubble, merge, quick), Searching algorithms (linear, binary), Hashing techniques |
| CS.DSC.2.2 | Discrete Mathematics | Core (DSC) | 4 | Propositional and predicate logic, Proof techniques (induction, contradiction), Relations, functions and cardinality, Group theory and algebraic structures, Combinatorics (permutations, combinations), Recurrence relations |
| CS.DSC.2.3 | Computer Networks | Core (DSC) | 4 | Network models (OSI, TCP/IP), Physical and Data Link layer functions, Network layer (IP addressing, routing), Transport layer (TCP, UDP), Application layer protocols (HTTP, DNS), Network security fundamentals |
| GE-2 | Generic Elective Course - II | Generic Elective (GE) | 4 | Topics depend on the specific course chosen from the university-wide pool. |
| AECC-2 | English/MIL Communication | Ability Enhancement Compulsory Course (AECC) | 2 | Theories of communication, Grammar and vocabulary building, Reading comprehension and critical analysis, Writing skills (essays, reports), Listening and speaking skills, Presentation techniques |
| VAC-2 | Value Addition Course - II | Value Addition Course (VAC) | 2 | Topics depend on the specific course chosen from the university-wide pool. |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS.DSC.3.1 | Object Oriented Programming with C++ | Core (DSC) | 4 | OOP concepts (classes, objects), Inheritance and polymorphism, Function and operator overloading, Constructors and destructors, Virtual functions and abstract classes, Templates and exception handling |
| CS.DSC.3.2 | Operating Systems | Core (DSC) | 4 | Operating system concepts and services, Process management and CPU scheduling, Deadlocks and concurrency control, Memory management (paging, segmentation), Virtual memory and file systems, I/O systems and disk scheduling |
| CS.DSC.3.3 | Database Management Systems | Core (DSC) | 4 | Database system architecture, Relational model and integrity constraints, SQL (DDL, DML, DCL), Entity-Relationship (ER) model, Normalization (1NF, 2NF, 3NF, BCNF), Transaction management and concurrency control |
| GE-3 | Generic Elective Course - III | Generic Elective (GE) | 4 | Topics depend on the specific course chosen from the university-wide pool. |
| CS.SEC.3.1 | Python Programming (Advanced) | Skill Enhancement Course (SEC) | 2 | Object-oriented programming in Python, Advanced data structures (collections, iterators), GUI programming with Tkinter, Web scraping with Python, Database access (SQLite), Introduction to scientific computing libraries |
| CS.SEC.3.2 | R Programming | Skill Enhancement Course (SEC) | 2 | Introduction to R and RStudio, Data types and data structures in R, Importing and exporting data, Data manipulation (dplyr, tidyr), Statistical graphics (ggplot2), Basic statistical analysis in R |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS.DSC.4.1 | Design and Analysis of Algorithms | Core (DSC) | 4 | Asymptotic notation and complexity analysis, Divide and conquer algorithms, Greedy algorithms, Dynamic programming, Graph algorithms (BFS, DFS, shortest path), NP-completeness and approximation algorithms |
| CS.DSC.4.2 | Software Engineering | Core (DSC) | 4 | Software development life cycle models, Requirements engineering and analysis, Software design principles and patterns, Software testing techniques (unit, integration), Software project management, Agile development methodologies |
| CS.DSC.4.3 | Artificial Intelligence | Core (DSC) | 4 | Introduction to AI and its applications, Problem-solving via search algorithms (BFS, DFS, A*), Knowledge representation and reasoning, Machine learning fundamentals, Natural Language Processing basics, Introduction to neural networks |
| GE-4 | Generic Elective Course - IV | Generic Elective (GE) | 4 | Topics depend on the specific course chosen from the university-wide pool. |
| CS.SEC.4.1 | Web Design | Skill Enhancement Course (SEC) | 2 | HTML5 structure and semantics, CSS3 for styling and layout, Responsive web design (media queries), Introduction to JavaScript for interactivity, Web hosting and domain names, Basic UI/UX principles |
| CS.SEC.4.2 | Cloud Computing Fundamentals | Skill Enhancement Course (SEC) | 2 | Introduction to cloud computing, Cloud service models (IaaS, PaaS, SaaS), Cloud deployment models (public, private, hybrid), Virtualization concepts, Cloud storage and networking, Cloud security basics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS.DSC.5.1 | Theory of Computation | Core (DSC) | 4 | Finite Automata and Regular Expressions, Context-Free Grammars and Pushdown Automata, Turing Machines and Computability, Decidability and Undecidability, Complexity classes (P, NP), Chomsky Hierarchy of Languages |
| CS.DSC.5.2 | Computer Graphics | Core (DSC) | 4 | Introduction to computer graphics, Output primitives (lines, circles), 2D and 3D geometric transformations, Viewing transformations and clipping, Surface rendering techniques, Introduction to animation |
| CS.DSE.5.1 | Data Mining | Discipline Specific Elective (DSE) | 4 | Introduction to data mining and KDD, Data preprocessing and cleaning, Association rule mining (Apriori), Classification techniques (decision trees, naive Bayes), Clustering algorithms (K-means, hierarchical), Outlier detection |
| CS.DSE.5.2 | Machine Learning | Discipline Specific Elective (DSE) | 4 | Supervised learning (regression, classification), Unsupervised learning (clustering), Neural networks and deep learning basics, Support Vector Machines (SVM), Model evaluation and selection, Bias-variance tradeoff |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS.DSE.6.1 | Mobile Application Development | Discipline Specific Elective (DSE) | 4 | Introduction to Android/iOS development, User Interface design (layouts, widgets), Activity lifecycle and intents, Data storage (SQLite, SharedPreferences), Networking and API integration, Publishing mobile apps |
| CS.DSE.6.2 | Big Data Analytics | Discipline Specific Elective (DSE) | 4 | Introduction to Big Data concepts, Hadoop ecosystem (HDFS, MapReduce), NoSQL databases (MongoDB, Cassandra), Data streams and real-time analytics, Big Data tools and technologies, Data visualization for Big Data |
| CS.DSE.6.3 | Embedded Systems | Discipline Specific Elective (DSE) | 4 | Introduction to embedded systems, Microcontrollers and microprocessors, Sensors and actuators, Interfacing techniques, Real-time operating systems (RTOS), Embedded system design challenges |
| CS.DSE.6.4 | Project Work / Dissertation | Discipline Specific Elective (DSE) | 4 | Project proposal and literature review, System design and implementation, Testing and debugging, Project report writing, Presentation and viva-voce, Problem identification and solution formulation |
