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WBUT Syllabus 2018 Revised B.Tech, M.Tech, B.Sc, M.Sc (UG, PG) Syllabus

WBUT Syllabus

Maulana AbulKalam Azad University of Technology, West Bengal (Formerly known as West Bengal University of Technology. The courses conducts in this institution are based on WBUT Syllabus. There are various (UG, PG) courses conducted like B.Tech, M.Tech, B.Sc, M.Sc etc. and each course has its separate WBUT Syllabus 2018.  Each year the university releases the Revised WBUT Syllabus on the official web portal i.e.

The University is steadfast in its twin objectives the first one is to serve as a Centre of Excellence in teaching and research in technology and management area and the one is to provide framework of industrialization based on knowledge economy. In order tocomprehend its objective of being developed as a Centre of Excellence, the MaulanaAbulKalam Azad University of Technology, West Bengal has created three Schools of Studies viz. School of Engineering & Technology (SET), School of Biotechnology & Biological Sciences (SBTBS) and School of Management and Sciences (SOMS) comprising presently of five departments viz. Department of Computer Science & Engineering, Department of Industrial Engineering & Management, Department of Biotechnology, Department of Bioinformatics and Department of Management and Sciences.

Each school has its own curriculum and a separate WBUT Syllabus 2018 and here on this page of we are providing the details about the WBUT Syllabus 2018

WBUT Syllabus

M.Sc. (Information Science)


(MI 101) Discrete Mathematics

Basic concept of Sets, Relations, Functions, Groups, Rings and Fields

  • Recursion and Recurrence Relation: Basic idea, Sequence and discrete function. Generating functions and applications.
  • Mathematical Logic: Propositional logic, Logical connectives, logical equivalence, validity of arguments, WFF, Predicates and quantifiers.
  • Combinatorics: Inclusion-Exclusion principle, Pigeonhole principle, Generalized Pigeonhole principle and related problems.
  • Advanced Graph Theory: Basic terminologies, Handshaking theorem, Isomorphism of graphs, Metrix Representation of Graphs, Walks, Paths, Circuits, Shortest Path Problem, Dijkstra’s Algorithm, Trees, Properties of Trees, Cotrees and Fundamental Circuits, Shortest Spanning Trees – Kruskal’sAlgorithm,Prim’sAlgorithm,DFS,BFS, Cut Sets, Fundamental Cut Sets and Cut Vertices, Planar and Dual Graphs, Networks, Flow Augmenting Path, Ford-Fulkerson Algorithm for Maximum Flow, Graph coloring, Chromatic polynomial, Matching and Covering.

(MI 102) Fundamentals of Information Science

Unit – I: Introduction to Information Science

Definition, scope, transition to modern information science, research in information science
Unit – II: Information and references SourcesTypes and Importance; Documentary Sources: Primary, Secondary and Tertiary; Non print materials including digital information sources, Traditional Vs. Digital sources of information; Institutional and Human Sources; Reference Sources including Indian reference sources; Evaluation of Reference and Information Sources
Unit – III: Fundamental concepts of Information and Reference ServicesDefinition & Characteristics of Information, Linguistic & biological approaches to Information Concept, definition, scope and types of references Search strategy and techniques;
Unit – IV: Users of informationInformation users and their information needs; Categories of information users; Information needs – definition, scopes and models; Information seeking behaviour; User studies: Methods, techniques and evaluation and User education.
Unit-V: Introduction to ICTData, information and knowledge, ICT – definition, scope, application in human activities, social implication, Application of ICT in activities of library and information centres;
Unit-VI: Information Science as Profession

Categories of Information science profession, Information creation/discovery profession, Management related profession, Storage related profession, Changes in the Information science Profession,

(MI 103) Principles of Computer Programming

Introduction: Characteristics of Computers, Evolution of Computing, Binary Number Systems, Types of Computer Software, Software Development Steps, Types of Programming Languages, Internet Evolution, Basic Internet Terminology, Getting Connected to Internet Applications. Problem Solving Techniques using Computers: Algorithm, Flow Charts, Pseudocode

Basic Programming Concepts: Problem solving steps using Computer.

Introduction to Programming Language C: Overview of C language, Lexical elements of C- Data Types, managing input/output operations, Operators and Hierarchy of Operations, Expressions in C, Decision Making and Repetitive Statements, break, continue, Array, Pointers, dynamic memory allocation, String handling, Functions: User Defined Functions and Library Functions, Parameter Passing, Storage Classes, enumerated data types, Command line arguments, C Preprocessors, Union & Structures, File handling in C.

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(MI – 104) Algorithms & Data Structures


 Algorithm – pseudo code for expressing algorithms analysis – time complexity and space complexity – efficiency of algorithms – O-notation – Omega notation and Theta notation.
DIVIDE AND CONQUER General method binary search – merge sort – quick sort.
GREEDY METHOD General method- Knapsack problem – job sequencing with deadlines – minimum-cost spanning trees : Prim’s and Kruskal’s algorithms – Single source shortest paths : Dijkstra’s algorithm.
DYNAMIC PROGRAMMING General method – Multistage Graphs All pairs shortest paths, Single source shortest paths – optimal binary search trees – O/1 Knapsack problem – Traveling sales person problem.
BACK TRACKING General method – n-queen problem – sum of subsets problem – graph colouring – Hamiltonian cycles – Knapsack problem.
BRANCH AND BOUND Least Cost(LC) search, Bounding – LC branch and bound – FIFO branch and bound – Travelling sales person problem.
Basic Data StructuresArrays, Stacks, Queues, dequeue, Linked Lists, Trees, AVL tree, Priority Queues, and Heap. Basic algorithms for Creation, Manipulation and Applications of Data Structures
HashingHash Functions, Hash Table, and Collision Resolution Techniques.
Algorithmic ComplexityTime-Space trade-off, and Asymptotic notations.
Searching Algorithms Linear Search and Binary search.
Sorting Algorithms Selection, Bubble, Insertion, Quick, Merge, Heap Sort and Radix sort
TreeBinary tree, Complete binary tree, AVL Tree, Binary Tree Traversal, threaded Binary Tree, Heterogeneous Binary Tree, Representing lists as binary Tree, Trees and their application, B+ Tree, RED BLACK Tree and their application.

 Application of graphs(C representation of graph, Transitive closure, Warshall’s algorithm, Shortest path algorithm), Flow Problem, Linked Representation of graph, Graph traversal and Spanning Forests.

(MI 105) Computer Organization & Architecture

  • Concepts and Terminology: Digital computer components Hardware & Software and their dual nature, Role of Operating Systems (OS).
  • The ALU: ALU organization, Integer representation, Serial and Parallel Adders, is 1s and 2s complement, binary arithmetic, Multiplication of signed binary numbers, Logic gates, basic logic operations, truth tables, Boolean expression, simplification, Basics of Combinational circuits and Sequential circuits Floating point number arithmetic, Overflow detection, Status flags.
  • Memory Unit: Memory classification, Bipolar and MOS storage cells. Organization of RAM, address decoding, Registers and stack, ROM and PROM-basic cell. Organization and erasing schemes, Magnetic memories-recording formats and methods. Disk and tape Units. Concept of memory map. Timing diagrams, T-States, Timing diagram Controlling arithmetic and logic instructions. Instruction sequencing with examples. Introduction to Microprogramming, Variations in Micro-programming configuration.
  • General Organization: Instruction work formats, Addressing modes registers, Von-Neumann concept, interconnecting system components, Interfacing buses, Timing diagrams, Examples from popular machines.

(MI-193) Programming Lab with C

  • C programming on variables and expressions.
  • Precedence of operators, Type casting.
  • Decision control structures— if and nested if-else.
  • Loop controls— do, while, for and case control structure.
  • Unconditional jumps— break, continue, goto.
  • Modular program development using functions.
  • Arrays and matrix operations—add, subtract, multiply.
  • Recursion
  • Pointers, address operators and pointer arithmetic.
  • Structures and Unions, Accessing their members.
  • Self-Referential Structures and Linked lists.
  • Files and file operations, standard streams.
  • Dynamic memory allocation and deallocations.
  • Different mathematical operations using <math.h>.
  • Pointers to pointers, arrays, functions, structures and unions.
  • Command line arguments, enums and prepocessors.

(MI-194) Algorithm & Data Structure Lab

  1. Matrix Operations-Add, Multiply, Rank, Det.etc.
  2. Stack & Queue operations using Arrays.
  3. Self-referential structures & single linked list operations.
  4. Implementing Stack and queues using linked lists.
  5. Implementing Polish Notations using Stacks.
  6. Circular and double linked list operations.
  7. Implementing priority queue &dequeue using lists.
  8. Evaluating polynomial operations using Linked lists.
  9. Implementing set related operations & Hashing.
  10. linear& binary search, bubble sort technique.
  11. Insertion sort, selection sort & merge sort techniques.
  12. Quick sort, counting sort and Shell sort techniques.
  13. Radix (bucket) and address calculation sort methods.
  14. Binary tree traversals (preorder, inorder, postorder).
  15. Heap sort & AVL tree implementations.
  16. Graph representation with matrix & adjacency lists.
  17. Divide and conquer method (quick sort, merge sort, Strassen’s matrix multiplication)

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(MI – 201) Quantitative Techniques for Information Science

  1. Probability distributions
  • Discrete distributions – Binomial, Poisson
  • Continuous distributions – Uniform, Exponential, Normal, Log Normal
  1. Sampling Methods and Sampling Distributions
  • Statistics and Parameter
  • Types of sampling – random and non-random sampling
  • Sampling distributions – conceptual basis; standard error; sampling from normal populations; Central Limit Theorem; relationship between sample size and standard error; Finite Population Multiplier
  1. Estimation
  • Point Estimation – properties of estimators; the method of moments and the method of maximum likelihood
  • Interval Estimation – basic concepts; interval estimates and confidence interval; calculation of interval estimates of mean and proportion from large samples; interval estimation using the t distribution; determining the sample size in estimation
  1. Hypothesis Testing
  • Basic Concepts – Null and Alternative Hypotheses; Type I and Type II errors; the p – value; the significance level; power of a test
  • One Sample Tests – hypothesis testing of means when the population standard deviation is known and when it is unknown; hypothesis testing of proportions for large samples
  • Two Sample Tests – tests for difference between means – large sample sizes and small sample sizes; test for difference between proportions – large sample sizes; testing difference between means with dependent samples
  1. Chi–square and Analysis of Variance
  • Chi-square as a test of (a) independence and (b) goodness of fit
  • ANOVA – basic concepts; the F distribution and the F statistic; inferences about a population variance; inferences about two population variances
  1. Non-parametric tests (demonstration of software package)
  • Basic concepts
  • The Sign Test
  • The Signed-Rank Test
  • Rank Sum Tests – The Mann-Whitney U Test; The Kruskal-Wallis Test
  • Tests based on runs
  • Rank Correlation
  • Kolmogorov-Smirnov Test

  (MI – 202) Knowledge Management & Systems

Overview: An overview of the key concepts, terminology and the historical context of practical Knowledge Management in the workplace. How every successful organizations use Knowledge Management. Knowledge as an organizational process versus simply a collection of data that can be stored in a database.

Knowledge Workers : Knowledge Management from the employees’ perspective, KM techniques to enhance employee effectiveness in organization.
Process : Knowledge Management as a process. Process reengineering, competency measurement, how to best apply collaborative systems, approaches to unobtrusive knowledge capture, filtering and refining knowledge, methodologies for applying knowledge for decision support, Knowledge Management & traditional business processes, business models.
Technology : Computer and communications technologies that can be used to enhance the organizational and behavioral aspects of a Knowledge Management initiative. A survey of technologies for knowledge collection (e.g., data mining, text summarizing, the use of intelligent agents, and a variety of information retrieval methodologies), knowledge storage and retrieval (e.g., knowledge bases and information repositories), and knowledge dissemination and application (e.g., intranets and internets, groupware, decision support tools, and collaborative systems).
Solutions : KPI (Key Performance Indicators), Benchmarking, Evaluation of KM initiatives, Impact on the corporate vision
Economics of knowledge Organization: Financial aspects of Knowledge Management, from a return-on-investment perspective. Pricing models for information infrastructure development, overhead costs, contractual issues, and hidden costs of Knowledge Management, and how to justify the cost of investing in new technologies, knowledge economy in terms of the knowledge value chain.
Successful implementation: Resource analysis for practical knowledge management implementation challenges, working with vendors, achieving employee buy-in.
Building and Managing Systems: Building Information Systems Managing Projects Managing Global Systems

Decision Support Systems and Business Intelligence: The Concept of Decision Support Systems (DSS), A Framework for Business Intelligence (BI), A Work System View of Decision Support, The Major Tools and Techniques of Managerial Decision Support, Implementing Computer Based Managerial Decision Support Systems

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  • Difference with procedure oriented programming.
  • Data Abstraction and Information Hiding : Objects, Classes and Methods, Encapsulation, Inheritance, Polymorphism, Object.
  • Fundamentals of Object Oriented design in UML: Static and dynamic models, why modeling, UML diagrams: Class diagram, interaction diagram: collaboration diagram, sequence diagram, state chart diagram, activity diagram, implementation diagram, UML extensibility- model constraints and comments,
  • Variables, Expressions and Statements: Values, Variables and keywords; Operators operator precedence, Expressions and Statements; Taking input and displaying output (print statement); Putting Comments.
  • Modules, importing Modules (entire module or selected objects), invoking built in functions, generating random numbers.
  • Defining functions, invoking functions, passing parameters (default parameter values, keyword arguments),scope of variables, void functions and functions returning values.
  • Conditional constructs and looping: if else statement, While, For (range function), break, continue, else, pass, Nested loops, use of compound expression in conditional constructs and looping.
  • Strings: Creating, initialising and accessing the elements; String operators: +, *, in, not in, range slice[n:m]; Comparing strings using relational operators; String functions
  • Lists: Concept of mutable lists, creating, initializing and accessing the elements, traversing, appending, updating and deleting elements; List operations (joining, list slices); List functions & methods: len, insert, append, extend, sort, remove, reverse, pop
  • Stacks and Queues with lists
  • Data File:Opening and closing files, file object, access_modes, reading and writing a file Read(), readline(), readlines(), write(), file positions (seek(), tell()), renaming and deleting a file.
  • Classes: Defining classes (attributes, methods), creating instance objects, accessing attributes & methods, using Built in class attributes (dict, doc, name, module, bases), using _ _init_ _() , _ _del_ _() method and __ str_ _( ) in a class, private attributes (limited support), importance of “self” 13. Inheritance: Single and multiple inheritance- Overriding methods, using super() in derived class to invoke _init_() or overriden methods of parent class

(MI – 204) Operating System


Definition, Design Goals, Evolution; Batch processing, Multi-programming, Time sharing; Structure and Functions of Operating System

Process Management:

Process states, State Transitions, Process Control Structure, Context Switching, Process Scheduling, Threads.

Memory Management:

Address Binding, Dynamic Loading and Linking Concepts, Logical and Physical Addresses, Contiguous Allocation, Fragmentation, Paging, Segmentation, Combined Systems, Virtual Memory, Demand Paging, Page fault, Page replacement algorithms, Global Vs Local Allocation, Thrashing, Working Set Model, Paging.

Concurrent Processes:

Process Interaction, Shared Data and Critical Section, Mutual Exclusion, Busy form of waiting, Lock and unlock primitives, Synchronization, Classical Problems of Synchronization, Semaphores, Monitors, Conditional Critical Regions, System Deadlock, Wait for Graph, Deadlock Handling Techniques: Prevention, Avoidance, Detection and Recovery.

File and Secondary Storage Management:

File Attributes, File Types, File Access Methods, Directory Structure, Allocation Methods, Free Space management; Disk Structure, Logical and Physical View, Disk Head Scheduling, Protection & Security.

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(MI – 205) Database Management Systems

  • Introduction: Database Systems, View of Data Models, Database Languages, DBMS Architecture, Database Users and Data Independence.
  • ER Modeling, relation types, role and Structural Constraints, Extended ER Modeling Features, Design of an ER Database Schema, Reduction of ER Schema to Tables.
  • Relational Model: Relational Model Concepts, Relational Algebra.
  • Introduction to SQL: SQL data types and literals, Types of SQL commands, SQL operators, Tables, views and indexes, Queries and sub queries, Aggregate functions.
  • Relational Database Design: Functional and multi-valued Dependencies, Desirable Properties of Decomposition, Normalization 1NF, 2NF, 3NF, 4NF, 5 NF and BCNF.
  • Selected Database Issues: Security, Transaction Management, Introduction to Query Processing and Query Optimization, Concurrency Control, and Recovery Techniques.

(MI-293) Object Technology Lab

  1. Simple Java applications – for understanding reference to an instance of a class (object), methods – Handling Strings in Java
  2. Simple Package creation. – Developing user defined packages in Java
  3. Interfaces – Developing user-defined interfaces and implementation – Use of predefined interfaces
  4. Threading – Creation of thread in Java applications – Multithreading
  5. Exception Handling Mechanism in Java – Handling pre-defined exceptions – Handling user-defined exceptions
  6. Swings and Applets

(MI – 295) DBMS Lab


  1. Creating, altering and dropping tables with integrity constraints.
  2. Retrieving and modifying data from a database
  3. Retrieving data from database using IN, BETWEEN, LIKE, ORDER BY, GROUP BY and HAVING clause.
  4. Use of scalar and aggregate functions.
  5. Retrieving data from a database using Equi , Non Equi , Outer and Self Join.
  6. Using sub queries, rowid and rownum for retrieving data.
  7. Use of views, indexes and sequences.


  1. Introduction to PL/SQL, using output from server.
  2. Use of implicit & explicit cursors in data handling.
  3. Exception handling – Oracle defined and User defined.
  4. Use of stored procedures & functions in data manipulation.
  5. Use of trigger in data manipulation.

Rest of the M.Sc. (Information Science) Syllabus>>Available Here

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