| Department of Computer Science and Engineering

B.Tech. in Computer Science & Engineering


Total Credits


Core Credits


Major Electives


CCC + UWE credits

Core & Elective Courses

Core Courses

Core courses are compulsory courses that provide critical foundations to the undergraduate program. These courses are mandatory in order to develop in-depth knowledge of the discipline.

Course code
Introduction to Computing and Programming

Basics of computer programming, Introduction to C programming, data types, operators, control statements, functions, arrays, pointers, strings, formatted I/O, structures, unions, bit manipulation, file processing, brief introduction to data structures.
Module 1: Introduction  Explain computers, hardware and software.  Understand the basic terminologies used in programming. Explain personal, distributed, client server computing. Explain machine languages, assembly languages and high level languages. 
Module 2: Basics of C programming   
Introduction to C programming, different data types, various operators (arithmetic, logical, bitwise, assignment) Control Structures: If, if…else, while, do…while, for, switch, break, continue  
Module 3: 
Arrays, Functions and Pointers  Functions: Defining and accessing, Calling Functions by value and by reference, Recursion.  Arrays: Defining arrays, passing arrays to functions, multidimensional arrays, sorting and searching arrays. Pointers: Declarations, operations on pointers, passing pointers to function, pointer arithmetic, pointers and arrays.  
Module 4: Characters and Strings, Structures, Formatted I/O  Fundamentals of characters and strings, Character handling and string handling Structures: Defining, accessing structure members, using structures with functions  Module 
5: File Processing, Data Structures  Reading/Writing  data  from/to  sequential  access  and  random access file. Introduction to stacks, queues, linked list and trees.

Data Structures

Prerequisite: CSD101
The goal of the course is to teach fundamental data structures, whichallow one to store  collections of data with fast updates and queries. A detailed schedule will evolve as the semester progresses. Key topics will definitely include: C refresher, abstract data types, fundamental data structures, complexity of algorithms, analysis tools, linked lists, stacks and queues, search trees,maps, hashing, priority queues, graphs.

Database Systems

Prerequisite: CSD201, CSD205Database Concepts 
- File system and databes. - Database systems - Database Models 
Relational Database Model 
- A logical view of data - Keys - Integrity  rules - Relational Database Catalogue 
Structured Query Language 
- DDL commands - DML commands - DCL  commands - TCL  commands - Queries 
Design Concepts 
- Data Models - Conceptual Model - External Model - Physical Model - Entity Relationship  Model (E-R  Model ) - Developing E-R diagram, EER – diagram. - Converting E-R model into a database structure. - Normalization - Normal Forms - De normalization. 
Transaction Management. 
Concurrency control 
Buffer Management 
Query Processing. 
Concept of object oriented management system. 
Distributed database management system. 
Other databases.

Operating Systems

Prerequisites: CSD206, CSD207*
Fundamental functions and basic structure of operating Systems, process concept, process and CPU scheduling, and interprocess communication. Multithreading, process synchronization, deadlock management, main memory and virtual memory, mass storage structures, File Systems, I/O Systems.

Discrete Mathematics

Prerequisite: NA
Counting: Permutation & Combination, Derangement, Pigeonhole Principle, Binomial Coefficient, Principle of Inclusion and Exclusion.  
Set Theory: Operations on sets, Cartesian product of sets, General proofs of some fundamental identities on sets.  
Group Theory: Functions: Definition, Classification of functions, Operations on functions, Algebraic Structures: Definition, Groups, Subgroups and order, Cyclic Groups, Cosets, Lagrange's theorem, Normal Subgroups, Permutation and Symmetric groups, Group Homeomorphisms, Definition and elementary properties of Rings and Fields, Integers Modulo n.
Relation Algebra:  Relations and Digraphs, Properties of relations, Equivalence relations and equivalence classes, Operations on relations, Connection between relations and some data structures, Transitive Closure and Warshall’s algorithm. Partial order relations, Partial order sets: Definition, Partial order sets, Combination of partial order sets, Hasse diagram. Lattices: Definition, Properties of lattices – Bounded, Complemented and Complete lattice.
Graph Theory: Basic of Graph, Euler paths and circuits, Hamiltonian paths and circuits, isomorphic graphs, Connected Graph, Trees, Labeled trees, Tree searching, Undirected trees, Isomorphic trees, Minimal spanning trees, Prim’s algorithm, Planar Graph,

 Matching problems, Coloring graphs, Transport networks.

Propositional Logic and Predicate Calculus: Propositions and Logical operations, Conditional statements, First order predicate, well-formed formula of predicate, quantifiers, Inference theory of predicate logic. 
Methods of proof, Mathematical induction, Recursively defined functions, Growth of Functions, Recurrence relations.

Computer Organization

Prerequisite: CSD101

The course aims at providing an overview of computer organization and how computer design has evolved, analyzing major components including internal and external memory, input–output (I/O) bus interconnection system, analyzing internal organization of processor, control unit and use of hardwired & microprogrammed control unit. Additionally, computer arithmetic and instruction set architecture will be focused upon.

Object Oriented Programming in Java

Prerequisite: CSD101

This course includes the introductory and advanced concepts and  implementation of the Object Oriented Paradigm using the Java programming language. Topic would include Introduction, Elementary Programming, Selections, Loops, Methods, Arrays, Strings, Objects and Classes, Inheritance and Polymorphism, GUI Basics and Components, Graphics, Exceptions, Abstract Classes and Interfaces, Event-Driven Programming, Binary I/O, Recursion, and Java Collections Framework.

11.    Course Aims
This course will enable the students to understand the concepts of object oriented programming using java, and apply its structures and techniques in various programming problems.

Computer Architecture

Prerequisite: CSD206

1. Revision of Computer Organisation. Floating point operations,
2. Instruction Set Architecture-8051/ARM
3. Pipelined Processor-Hazards
4. Advanced Pipelining and Instruction-Level Parallelism
5. Memory Systems
6. Storage Systems and I/O
7. Interconnection Networks
8. Multiprocessors
9. Parallel and Distributed Computing
10. High Performance Computing
11. Embedded Systems

Introduction to Probability and Statistics

Prerequisite: CSD205
Applications of inferential statistics in engineering problems; Measures of central tendency, Measures of Dispersion, Time series analysis & Trend Analysis. Karl Pearson and Spearman rank correlation, Regression equations and their application, Partial and Multiple correlation & regression. Sampling theory; Formulation of Hypotheses; Application of Z test, t-test, F-test and Chi-Square test & application to engineering problems. Concept of probability and its uses in business decision-making; Addition and multiplication theorems; Bayes’ Theorem and its applications. Probability Theoretical Distributions: Concept and application of Binomial; Poisson and Normal distributions, Introduction to Stochastic Processes.

Software Engineering

Prerequisite: CSD201, CSD207*
This course aims at helping students build up an understanding of how to develop a software system from scratch by guiding them through the development process and giving them the fundamental principles of system development with object oriented technology using UML. The course will initiate students to the different software process models, project management, software requirements engineering process, systems analysis and design as a problem-solving activity, key elements of analysis and design, and the place of the analysis and design phases within the system development life cycle.

Design and Analysis of Algorithms

Prerequisite: CSD201, CSD205
Asymptotic notations, analysis of iterative and recursive algorithms, randomized algorithms, divide and conquer, greedy method, dynamic programming, graph algorithms, backtracking, NP-Hard and NP-Complete problems.

Theory of Computation

Prerequisite: CSD201, CSD205
Finite automata, nondeterminism, regular languages ; pushdown automata, context-free languages, grammars; Turing machines, computability; complexity, NP-completeness ; hot topics (possibly including cryptography, on-line algorithms, game theory, social networks, randomization, and quantum computing).

Computer Networks

Prerequisite: CSD204, CSD207*
Protocol layers, physical and data link layers, multi-access, IP naming, addressing and forwarding, transport protocols, congestion control, routing protocols, wireless networks, quality of service, network security.

Internet and Web Systems

Prerequisite: CSD304*

This course aims on the concepts used in building Internet and web systems. The students would be able to understand how a web server is built. 

11. Course Aims
1.    To introduce the concepts of Distributed systems, Cloud computing, web servers.
2.    To understand big data and streaming data processing on web servers.
3.    Understand concepts of naming and locating resources, directory systems, distributed data structures and applications.
4.    Apply the concepts learnt by applying them in an end-to-end project.

Software Project Management

Prerequisite: CSD301
Key concepts in software project management, planning and its execution. Working knowledge of software project life cycle, create project plan, write business user requirements, estimate the project size, plan Agile sprints, set-up development environment by applying continuous integration and deployment tools, test software project quality, and apply skills to manage stakeholders.

Research Methods in Computing

Prerequisite: CSD428*
Foundations of Research practices, state of the art, research problem formulation, theoretical and experimental research, paper reading and writing, use of tools and techniques in research.


Course description not available.

Intro. to Electrical Engg.

Circuit Analysis Review of KCL and KVL, Basic Circuit Terminology-Node, loop, mesh, circuit, branch and path. Ideal sources, Source transformation, Star-Delta transformation. AC analysis - Phasor, Complex impedance, complex power, power factor, power triangle, impedance triangle, series and parallel circuits
Network Theorems
Network Theorems (A.C. and D.C Circuits) - Mesh and Nodal analysis, Thevenin, Norton, Maximum Power transfer, Millman, Tellegen and Superposition theorem.
Resonance and Transient Analysis
Introduction to Resonance-series and parallel, half power frequency, resonant frequency, Bandwidth, Q factor. Transient Analysis-Step response, Forced Response of RL, RC & RLC Series circuits with Sinusoidal Excitation – Time Constant & Natural frequency of Oscillation – Laplace Transform applications.
Electronic Devices and Components
Review of Energy band diagram- Intrinsic and Extrinsic semiconductors- PN junction diodes and Zener diodes – characteristics, Diode Applications-Rectifiers, Clippers and Clampers. Transistors-PNP and NPN – operation, characteristics and applications, Biasing of Transistors. Operational Amplifiers-Introduction and Applications - Inverting, Non Inverting, Voltage follower, Integrator, differentiator and difference amplifier, Summer, log and Antilog.
Three Phase and Transformers Introduction to three phase, power measurements in three phase. Transformer-Principle of operation, construction, phasor diagram of Ideal and practical transformer with load (R,L,C and their combinations) and no load, equivalent circuit, efficiency and voltage regulation of single phase transformer, O.C. and S.C. tests. Introduction to D.C. Machines.

Signals and Systems

1. Classification and representation of signals and systems, Continuous time & Discrete time signals and systems, Impulse and Step response of a system, linear systems, linearity, time invariance, causality, signal properties -LTI systems, Convolution
2. Fourier series, Fourier transform and properties, relation between Fourier transform and Fourier series, Sampling and reconstruction, FFT, DIT FFT, DIF FFT Algorithm, Inverse DFT and Convolution using FFT
3. Laplace transforms- representation of signals using continuous time complex exponentials, relation of Laplace and Fourier transform, concept of ROC and transfer function- block diagram representation, Inverse Laplace transform, properties, analysis and characterization of LTI systems using Laplace transforms
4. Z transforms- representation of signals using discrete time complex exponentials-properties, inverse Z transforms, ROC, Analysis and characterization of LTI systems using Z transforms, block diagram, transfer functions
5. Introduction to random variable and random process, State space analysis, Introduction to Two port networks and parameters

Digital Electronics

Digital Processing of Information – Basic information processing steps – logic and arithmetic; Number Systems and Arithmetic – Positional number systems, Arithmetic operations on binary numbers; Combinational Logic – Basic logic operations, Boolean algebra, Boolean functions, De Morgan’s laws, Truth table and Karnaugh map representations of Boolean functions, Combinational circuit design using gates and multiplexers; Sequential Logic – Latches and Flip-flops, Ripple counters, Sequence generator using flip-flops, State Diagram, Synchronous counters, Shift Registers; Introduction to the Microprocessor – Basic constituents of a processor, Instruction set – machine language and assembly language.

Mathematical Methods I

Core course for all B.Tech. Optional for B.Sc. (Research) Chemistry. Not open as UWE.

Credits (Lec:Tut:Lab)= 3:1:0 (3 lectures and 1 tutorial weekly)

Prerequisites: Class XII Mathematics.

Overview:  In this course we study multi-variable calculus. Concepts of derivatives and integration will be developed for higher dimensional spaces. This course has direct applications in most engineering applications. 

Detailed Syllabus:

  1. Review of high school calculus.
  2. Parametric curves (Vector functions): plotting, tangent, arc-length, polar coordinates, derivatives and integrals.                                                                    
  3. Functions of several variables: level curves and surfaces, differentiation of functions of several variables, gradient, unconstrained and constrained optimization.
  4. Double and triple integrals: integrated integrals, polar coordinates, cylindrical and spherical coordinates, change of variables.
  5. Vector fields, divergence and curl, Line and surface integrals, Fundamental Theorems of Green, Stokes and Gauss.


  1. A Banner, The Calculus Lifesaver, Princeton University Press.
  2. James Stewart, Essential Calculus – Early Transcendentals, Cengage.
  3. G B Thomas and R L Finney, Calculus and Analytic Geometry, Addison-Wesley.
  4. Erwin Kreyszig, Advanced Engineering Mathematics, Wiley.

Past Instructors: Ajit Kumar, Sneh Lata

Mathematical Methods II

Core course for all B.Tech. Programs. Optional for B.Sc. (Research) Chemistry. Not available as UWE.

Credits (Lec:Tut:Lab)= 3:1:0 (3 lectures and 1 tutorial weekly)

Prerequisites: Class XII Mathematics

Overview:  We will study Ordinary Differential Equations which are a powerful tool for solving many science and engineering problems. This course also covers some basic linear algebra which is needed for systems of ODEs.

Detailed Syllabus:

  1. First order ODEs: separable, exact, linear
  2. Second order ODEs: homogeneous and nonhomogeneous linear, linear with constant coefficients, Wronskian, undetermined coefficients, variation of parameters
  3. Laplace transform: definition and inverse, linearity, shift, derivatives, integrals, initial value problems, time shift, Dirac’s delta function and partial fractions, convolution, differentiation and integration of transform
  4. Matrices: operations, inverse, determinant, eigenvalues and eigenvectors, diagonalization
  5. Systems of ODEs: superposition principle, Wronskian, constant coefficient systems, phase plane, critical points, stability


  1. James Stewart, Essential Calculus – Early Transcendentals, Cengage.
  2. Erwin Kreyszig, Advanced Engineering Mathematics, Wiley.

Past Instructors: Ajit Kumar, Neha Gupta

Introduction to Physics I

The aim of this course is to bridge the gap between the various boards across the country at 10+2 level and bring everyone at the standard undergraduate level. All the engineering branches have their origin in the basic physical sciences. In this course we aim to understand the basic physical laws and to develop skills for application of various physical concepts to the science and engineering through problem solving. This will involve the use of elementary calculus like differentiation and integration.   

Detailed Syllabus        

Mechanics: The inertial reference frames, Newton’s laws of motion in vector notation, Conservation of energy, Application of Newton’s laws of motion, Dynamical stability of systems: Potential energy diagram, Collisions: Impulse, conservation of energy and linear, momentum, Conservation of angular momentum and rotation of rigid bodies in plane Thermal Physics: Averages, probability and probability distributions, Thermal equilibrium and macroscopic variables, Pressure of an ideal gas from Newton’s laws - the kinetic theory of gases. Maxwell’s velocity distribution, Laws of Thermodynamics and the statistical origin of the second law of thermodynamics, Application of thermodynamics: Efficiency of heat engines and air-conditioners, Thermodynamics of batteries and rubber bands

Introduction to Physics II

This is a continuation of PHY 101 and is meant for engineers and non-physics majors. The course will introduce students to Electricity and Magnetism, Maxwell’s equations, Light as an electromagnetic wave, and Wave optics. 
Vector calculus: Gradient, Divergence, Curl and fundamental theorems of vector calculus. Basic laws in electricity and magnetism, Classical image problem, displacement current and continuity equation, Maxwell’s Equations, electromagnetic wave equation and its propagation in free space, conducting media and dielectric medium, Poynting theorem, Electromagnetic spectrum. 
Wave Optics: 
Interference of light waves: Young’s double slit experiment, displacement of fringes, Interference in thin films 
Diffraction: Fresnel’s and Fraunhofer’s class of diffraction, diffraction from single, double & N- Slits, Gratings. 
Polarization: Concept of Polarization in electromagnetic waves, types of polarized waves.

Elective Courses

The department currently has 3 identified areas of growth, and students are expected to work with faculty in one of these areas. These 3 areas and some of their sub-areas of research are mentioned below: • Wireless, Mobile Computing and Networking: Internet of Things, Mobile Sensing, Cyber Physical Systems, Wireless Sensor Networks, Wireless Networks. • Data Science and Engineering: Data Mining, Machine Learning, Mobile Data Management, Data Analytics. Big Data. • Security and Privacy: Security of Cyber Physical Systems, IoT and Cyber Security.

Course code
Wireless and Mobile Systems

Unit 1:
Unit 2 (Mobile Radio Propagation):
•Types of Radio Waves.
•Propagation Mechanisms.
•Free Space Propagation.
•Land Propagation.
•Path loss and Fading.
•Doppler Effect, Delay Spread and Intersymbol Interference.
Unit 3 (Cellular Concept):
•Cell Area.
•Signal Strength and Cell parameters.
•Capacity of a cell.
•Frequency reuse.
•How to form a cluster.
•Cochannel Interference
•Cell Splitting and Cell Sectoring.
Unit 4 (Multiple division Techniques):
•Concepts and Models of Multiple Divisions (FDMA, TDMA, etc.)
Unit 5 (Channel Allocation):
•Static Allocation versus Dynamic Allocation.
•Fixed Channel Allocation.
•Dynamic Channel Allocation.
•Hybrid Channel Allocation.
•Allocation in specialized System Structure.
Unit 6 (Mobile Communication Systems):
•Cellular System Infrastructure.
•Handoff and Roaming Support.
•Security and Privacy.
Unit 7 (Wireless MANs, LANs and PANs):
•Wireless Metropolitan Area Networks (4G systems).
•Wireless Local Area Networks (IEEE 802.11).
•Wireless Personal Area Network (Bluetooth Networks).

Wireless Sensor Networks

Introduction to WSN, applications, and challenges.
• Understand Node and Network Architecture.
• Understand and Analyze WSN protocol stack.
• Understand and analyze MAC protocols.
• Understand Wake up strategies.
• Understand and analyze routing protocols.
• Explain clustering approaches and their applications.
• Understand various important topics: data aggregation, time synchronization, localization, topology control, node addressing,
error control, QoS.
• Examine WSN security issues.
• Laboratory classes will focus on simulating networks and various protocols; and implementing your own sensor nodes and working on them. For labs, you will be required to purchase prescribed hardware.

Information Theory

Information Theory

Data Mining & Data Warehousing

1 Introduction to data mining, Data Mining vs Knowledge discovery, Data Mining issues, Social Implications.
2 Introduction to data warehousing, Database/OLTP systems, Decision Support Systems, Multi-dimensional
data model, Data Warehouse components, architecture and implementation, OLAP
3 Data Preprocessing: Data Cleaning, Data Integration and Transformation, Data Reduction,
Discretization and Concept Hierarchy Generation
4 Statistical Measures: Point Estimation, Models based on Summarization, Bayes Theorem,
Hypothesis testing, Regression and Correlation; Similarity measures.
5 Mining Association Rules in large databases, Mining Multidimensional Association Rules from
Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis,
Constraint Based Association Mining
6 Classification, Prediction, Clustering and Visualization
7 Introduction to Advanced Topics: Mining complex data, Mining Time-series data, Spatial data
mining, Multimedia data mining, Text mining, Mining the WWW.
8 Social impact of Data mining, Recent trends in Data mining research, Challenges and Future Scope.

Internet of Things

The course will provide a hands on approach to the concept of IoT. Topics include:
Introduction to IoT, IoT Domains, IoT Platform Design and Methodologies, IoT Hardware Platforms and Sensors, IoT Web and Cloud Platforms, IoT Analytics, IoT Tools, IoT Security Case Studies and Projects.