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 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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