BSc Computer Science 3rd year syllabus consists of some core subjects, most electives subjects and some practicals. The core subjects in BSc CS 3rd year syllabus are Internet Technologies, Artificial Intelligence, Theory of Computation, Computer Graphics, Information Security, Data Analysis and Visualization etc.
The elective subjects in BSc Computer Science are Digital Image Processing, Image Processing, Information Security, Data Mining, Advanced Algorithms etc. The lab subjects that students go through in BSc CS 3rd year syllabus are Internet Technologies Lab, Theory of Computation Lab, Data Analysis and Visualization Lab, System Programming Lab, Combinatorial Optimization Lab and Digital Image Processing Lab.
See Also: BSc Syllabus
Table of Contents
BSc CS 3rd year Syllabus
The third year of BSc Computer Science comprises the fifth and sixth semesters. The syllabus for the fifth and sixth semesters of BSc CS is listed below.
BSc CS Subjects 3rd year Semester 5 | BSc CS Subjects 3rd year Semester 6 |
---|---|
Internet Technologies | Artificial Intelligence |
Theory of Computation | Computer Graphics |
Data Analysis and Visualization | Information Security |
System Programming | Data Mining |
Combinatorial Optimization | Advanced Algorithms |
Digital Image Processing | Machine Learning |
Microprocessors | Deep Learning |
- | Unix Network Programming |
- | Project Work/ Dissertation |
BSc CS 3rd year Subjects
In BSc Computer Science 3rd year there are 16 subjects (both core and electives). In BSc CS fifth semester the core subjects are Internet Technologies and the Theory of Computation. The discipline-specific electives of the fifth semester are Data Analysis and Visualization, System Programming, Combinatorial Optimization, Digital Image Processing, and Microprocessors.
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In BSc CS sixth semester the core subjects are Artificial Intelligence and Computer Graphics. The discipline-specific electives of the sixth semester are Information Security, Data Mining, Advanced Algorithms, Machine Learning, Deep Learning, Unix Network Programming, and Project Work/ Dissertation.
BSc CS 5th Semester Subjects
- Internet Technologies: This subject focuses on the protocols used in the Internet, its architecture, and the security aspect of the Internet. The topics covered are Introduction to Internet Protocols, Web Servers, Javascript, jQuery, JSON, NODE.js, BOOTSTRAP, Search Engines, and Introduction to Cookies and Sessions.
- Theory of Computation: The Theory of Computation focuses on formal models of computation, like, finite automaton, pushdown automaton, and Turing machine. The topics covered are Languages, Regular Expressions and Finite Automata, Regular Languages, Non-Regular Languages and Context Free Grammars, Context-Free Languages (CFL) and PDA, and Turing Machines and Models of Computations.
- Data Analysis and Visualization: It focuses on data analysis and visualization in exploratory data science using Python. The topics covered are Introduction to Data Science, Essential Python Libraries, Getting Started with Pandas, Data Wrangling, Data Visualization Matplotlib, Data Aggregation and Group operations, and Advanced Pandas.
- System Programming: System Programming focuses on the design of the assembler and basic compiler. The topics covered are Assemblers & Loaders, Linkers, Lexical Analysis, Parsing & Intermediate representations, and Storage organization & Code generation.
- Combinatorial Optimization: Combinatorial Optimization focuses on the fundamentals of combinatorial optimization in terms of theory and application. The topics covered are Introduction to Combinatorial Optimization, Theory of Linear Programming and Algorithmic Perspective to Simplex Method, Primal-Dual Algorithms, and Network Flows.
- Digital Image Processing: This subject focuses on the fundamentals of digital image processing, and various image transforms, image restoration techniques, image compression and segmentation used in digital image processing. The topics covered are Spatial Domain Filtering, Filtering in Frequency Domain, Image Restoration, Image Compression, and Morphological Image Processing.
- Microprocessors: This subject focuses on internal architecture, the programming model of Intel Microprocessors and assembly language programming using an assembler. The topics covered are Microprocessor architecture, Microprocessor programming, Interfacing, Data transfer schemes, Microprocessor controllers, and Advance microprocessor architecture.
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BSc CS 6th Semester Subjects
- Artificial Intelligence: This subject, as the name suggests, focuses on the basic concepts and techniques of Artificial Intelligence (AI). The topics covered are Introduction to AI, Knowledge Representation, Reasoning with Uncertain Knowledge, Problem Solving and Searching Techniques, Game Playing, and Understanding Natural Languages.
- Computer Graphics: This subject focuses on fundamental concepts of Computer Graphics with a focus on modeling, rendering, and interaction aspects of computer graphics. The topics covered are Introduction to Graphics systems, Drawing and clipping primitives, Transformation and Viewing, Geometric Modeling, and Visible Surface Determination and Surface Rendering.
- Information Security: This subject focuses on the fundamentals of information security covering error correction or detection, cryptography, steganography, and malware. The topics covered are Error detection or correction, Cryptography, Malicious software, and Security in Internet-of-Things.
- Data Mining: This course focuses on data mining techniques. The topics covered are Introduction to Data Mining, Data Pre-processing, Classification, Model Evaluation, Association rule mining, and Cluster Analysis.
- Advanced Algorithms: This subject focuses on advanced data structures and algorithms for solving problems efficiently and their theoretical behavior. The topics covered are Advanced Data Structures, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Network Flows, NP-Completeness, and Backtracking.
- Machine Learning: This subject focuses on the basic concepts and techniques of machine learning. The topics covered are Regression, Classification, and Clustering.
- Deep Learning: This subject focuses on deep learning algorithms and their applications in order to solve real problems. The topics covered are Introduction, Neural Networks, Convolution Neural Networks, Sequence Modeling, Autoencoders, and Structuring Machine Learning Projects.
- Unix Network Programming: This subject focuses on the concepts of Internet protocols, ports used during communication, Client/Server concepts and various transport protocols used in computer network applications and services. The topics covered are Connection-oriented and Connectionless client server Applications, Socket Options, Connection-oriented and connectionless Sockets, Elementary name and Address conversions, and Advanced Sockets.
- Project Work: It will cover prototype development of real-life software.
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Top BSc CS Colleges in India
Some of the top colleges in India offering BSc Computer Science courses are listed below.
BSc Computer Science Colleges | 1st year fee (INR) |
---|---|
Women's Christian College, Chennai | 62,210 |
Kristu Jayanti College, Bangalore | 60,000 |
Thiagarajar College, Madurai | 40,170 |
B.K Birla College Of Arts Science & Commerce, Thane | 27,760 |
Kishinchand Chellaram College, Mumbai | 38,610 |
BSc CS Books in Second Year
Subjects | Books | Authors |
---|---|---|
Internet Technologies | Web enabled commercial application development using HTML, JavaScript, DHTML and PHP | I. Bayross |
The Internet Book: Everything You need to know about Computer networking and how the internet works | D Comer | |
JavaScript and JQuery: Interactive Front-End Web Development | J. Duckett | |
Web Technologies | A.S. Godbole and A. Kahate | |
Fundamentals of Internet and WWW | R. Greenlaw and E. Hepp | |
Theory of Computation | Introduction to Computer Theory | D.I.A. Cohen |
Elements of the Theory of Computation | H.R. Lewis and H.R. Papadimitriou | |
Automata and Computability: A programmer's perspective | G.L. Gopalkrishnan | |
An Introduction to Formal Languages and Automata | P. Linz | |
Data Analysis and Visualization | Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython | W. McKinney |
Doing Data Science: Straight Talk from the Frontline | C. O’Neil and R. Schutt | |
System Programming | System Software | S. Chattopadhyaya |
Compilers: Principles, Techniques, and Tools | A. Aho, M. Lam, R. Sethi, and J.D. Ullman | |
System Software: An Introduction to System Programming | L. Beck and D. Manjula | |
Systems Programming | D.M. Dhamdhere | |
Combinatorial Optimization | Understanding and Using Linear Programming | Matousek & Gartner |
Combinatorial Optimization: Algorithms and complexity | C.H. Papadimitriou and K. Steiglitz | |
Combinatorial Optimization | B. Korte and J. Vygen | |
Digital Image Processing | Digital Image Processing | R.C. Gonzalez and R.E. Woods |
Fundamentals of Digital Image Processing | A.K. Jain | |
Microprocessors | The Intel Microprocessors: Architecture, Programming and Interfacing | B.B. Brey |
The 8088 and 8086 Microprocessors Programming, Interfacing, Software, Hardware and Applications | W.A. Triebel and A. Singh | |
Artificial Intelligence | Artificial Intelligence | E. Rich and K. Knight |
Artificial Intelligence - A Modern Approach | S.J. Russell and P. Norvig | |
Computer Graphics | Computer Graphics | D.H. Baker |
Computer Graphics: Principles and Practice in C | J.D. Foley, A.V. Dam, S.k. Feiner, and J.F. Hughes | |
Information Security | Security in Computing | C.P. Pfleeger, S.L. Pfleeger, and J. Margulies |
Error Control Coding: Fundamentals and applications | S. Lin and D.J. Costello | |
Cryptography and network security | W. Stallings | |
Data Mining | Data Mining: Concepts and Techniques | J. Han, M. Kamber, and P. Jian |
Principles of Data Mining | D. Hand, H. Mannila, and P. Smyth | |
Advanced Algorithms | Introduction to Algorithms | T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein |
Algorithm Design | J. Kleinberg and E. Tardos | |
Machine Learning | Machine Learning | T.M. Mitchell |
Machine Learning: The Art and Science of Algorithms that Make Sense of Data | P. Flach | |
Pattern Recognition and Machine Learning | Christopher & Bishop | |
Deep Learning | Fundamentals of Deep Learning | N. Bunduma |
Deep Learning and Neural Networks | J. Heaton | |
Unix Network Programming | Unix Network Programming: The Sockets Networking API | R.W. Stevens, B. Fenner, and A.M. Rudoff |
Data Communication and Networking | B.A. Forouzan |
BSc CS 3rd Year Practical Subjects
All subjects of BSc 3rd year have practicals and they are listed below.
- Internet Technologies Lab
- Theory of Computation Lab
- Data Analysis and Visualization Lab
- System Programming Lab
- Combinatorial Optimization Lab
- Digital Image Processing Lab
- Microprocessors Lab
- Artificial Intelligence Lab
- Computer Graphics Lab
- Information Security Lab
- Data Mining Lab
- Advanced Algorithms Lab
- Machine Learning Lab
- Deep Learning Lab
- Unix Network Programming Lab
BSc CS 3rd Year Projects
The projects allotted during the BSc CS 3rd year are listed below.
- Face detection
- Online auction system
- e-Authentication system
- Android battery saver system
- Symbol recognition
- Search engine
BSc CS 3rd Year Electives
In the fifth semester of BSc Computer Science there are two sets of discipline-specific electives known as DSE-1 and DSE-2. Each set has some subjects and students have to choose any one.
There are three subjects in DSE-1 and they are listed below.
- Data Analysis and Visualization
- System Programming
- Combinatorial Optimization
There are two subjects in DSE-2 and they are listed below.
- Digital Image Processing
- Image Processing
In the sixth semester of BSc Computer Science there are two sets of discipline-specific electives known as DSE-3 and DSE-4. Each set has some subjects and students have to choose any one.
There are three subjects in DSE-3 and they are listed below.
- Information Security
- Data Mining
- Advanced Algorithms
The subjects under DSE-4 category are listed below.
- Machine Learning
- Deep Learning
- Unix Network Programming
- Project Work/ Dissertation
BSc CS Subjects 3rd Year: FAQs
Ques. What are the subjects in BSc Computer Science final year?
Ans. The subjects covered in BSc Computer Science final year are listed below.
- Internet Technologies
- Theory of Computation
- Data Analysis and Visualization
- System Programming
- Combinatorial Optimization
- Digital Image Processing
- Microprocessors
- Artificial Intelligence
- Computer Graphics
- Information Security
- Data Mining
- Advanced Algorithms
- Machine Learning
- Deep Learning
- Unix Network Programming
- Project Work
Ques. Can I do BSc CS without Maths?
Ans. There are some Mathematics topics in BSc CS such as algorithms, hence, you cannot have BSc CS without Maths.
Ques. Is BSc Computer Science hard?
Ans. BSc Computer Science is a very challenging subject that requires complete dedication.
Ques. Is computer science a 3 year course?
Ans. Yes, BSc Computer Science is a 3 year course.
Ques. Is BSc Computer Science a good career?
Ans. Yes it is. After completing BSc CS, one can work as a computer scientist, software developer, web developer, programmer, data scientist, and many more high-paying jobs.
Ques. Which is better B.Tech or BSc in CS?
Ans. B.Tech is a 4-year engineering and BSc CS is a 3-year degree. BSc CS is more theory-oriented.
Ques. Is BSc computer science and BCA same?
Ans. No, BSc computer science and BCA are different courses.
Ques. What is after BSc computer science?
Ans. After BSc computer science, one can either pursue further studies in courses like MSc CS, MSc Data Science, or MSc IT or can work in the IT field.
Ques. What are the 5 subjects in BSc Computer Science?
Ans. The 5 subjects in BSc CS are Internet Technologies, Theory of Computation, Artificial Intelligence, Computer Graphics, and Machine Learning.
Ques. Is BSc tough than BCA?
Ans. Both subjects require hardwork and dedication, so they can’t be compared on the toughness parameter.
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