2nd Semester
BSc.CSIT Second Semester Syllabus TU Syllabus
BSc.CSIT Second Semester Syllabus Overview
Syllabus of BSc.CSIT Second Semester comprises five compulsory courses that include Discrete Structures, ObjectOriented Programming, Microprocessor, Mathematics II, and Statistics I. They are a total of 15 credit hours with a total of 500 full marks.
BSc.CSIT Second Semester course code is shown below in table:
SN  Course Code  Course Title  Credit Hrs.  Full Marks 
1  CSC160  Discrete Structure  3  100 
2  CSC161  ObjectOriented Programming  3  100 
3  CSC162  Microprocessor  3  100 
4  MTH163  Mathematics II  3  100 
5  STA164  Statistics I  3  100 
Total  15  500 
 BSc.CSIT Second Semester Syllabus Overview
 Course Title 1: Discrete Structures
 Course Title 2: ObjectOriented Programming
 Unit 1: Introduction to Object Oriented Programming (3 Hrs.)
 Unit 2: Basics of C++ programming (5 Hrs.)
 Unit 3: Classes & Objects (8 Hrs.)
 Unit 4: Operator Overloading (7 Hrs.)
 Unit 5: Inheritance (7 Hrs.)
 Unit 6: Virtual Function, Polymorphism, and miscellaneous C++ Features (5 Hrs.)
 Unit 7: Function Templates and Exception Handling (4 Hrs.)
 Unit 8: File handling (6 Hrs.)
 Laboratory Works:
 Reference Books:
 Course Title 3: Microprocessor
 Unit 1: Introduction (4 Hrs.)
 Unit 2: Basic Architecture (7 Hrs.)
 Unit 3: Instruction Cycle (3 Hrs.)
 Unit 4: Assembly Language Programming (10 Hrs.)
 Unit 5: Basic I/O, Memory R/W and Interrupt Operations (6 Hrs.)
 Unit 6: Input/ Output Interfaces (6 Hrs.)
 Unit 7: Advanced Microprocessors (9 Hrs.)
 Laboratory Works:
 Reference Books:
 Course Title 4: Mathematics II
 Unit 1: Linear Equations in Linear Algebra (5 Hrs.)
 Unit 2:Transformation (4 Hrs.)
 Unit 3: Matrix Algebra (5 Hrs)
 Unit 4: Determinants (4 Hrs.)
 Unit 5: Vector Spaces (5 Hrs.)
 Unit 6: Vector Space Continued (4 Hrs.)
 Unit 7: Eigenvalues and Eigen Vectors (5 Hrs.)
 Unit 8: Orthogonality and Least Squares (5 Hrs.)
 Unit 9: Groups and Subgroups (5 Hrs.)
 Unit 10: Rings and Fields (4 Hrs.)
 Course Title 5 :Statistics I
 Unit 1: Introduction (4 Hrs.)
 Unit 2: Descriptive Statistics (6 Hrs.)
 Unit 3: Introduction to Probability (8 Hrs.)
 Unit 4: Sampling (3 Hrs.)
 5. Random Variables and Mathematical Expectation (5 Hrs.)
 Unit 6: Probability Distributions (12 Hrs.)
 Unit 7: Correlation and Linear Regression (7 Hrs.)
 Laboratory Works:
 Practical problems
 Reference Books:
Course Title 1: Discrete Structures
Full Marks: 60 + 20 + 20
Course No: CSC160
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: II
Course Description: The course covers fundamental concepts of discrete structure like introduce logic, proofs, sets, relations, functions, counting, and probability, with an emphasis on applications in computer science.
Course Objectives: The main objective of the course is to introduce basic discrete structures, explore applications of discrete structures in computer science, understand concepts of Counting, Probability, Relations and Graphs respectively.
Course Contents:
Unit 1: Basic Discrete Structures (7 Hrs.)
1.1.Sets: Sets and Subsets, Power Set, Cartesian Product, Set Operations, Venn Diagram, InclusionExclusion Principle, Computer Representation of Sets
1.2.Functions: Basic Concept, Injective and Bijective Functions, Inverse and Composite Functions, Graph of Functions, Functions for Computer Science (Ceiling Function, Floor Function, Boolean Function, Exponential Function), Fuzzy Sets and Membership Functions, Fuzzy Set Operations
1.3.Sequences and Summations: Basic Concept of Sequences, Geometric and Arithmetic Progression, Single and Double Summation
Unit 2: Integers and Matrices (6 Hrs.)
2.1.Integers: Integers and Division, Primes and Greatest Common Divisor, Extended Euclidean Algorithm, Integers and Algorithms, Applications of Number Theory (Linear
Congruencies, Chinese Remainder Theorem, Computer Arithmetic with Large Integers)
2.2.Matrices: ZeroOne Matrices, Boolean Matrix Operations
Unit 3: Logic and Proof Methods (6 Hrs.)
3.1.Logic: Propositional Logic, Propositional Equivalences, Predicates and Quantifiers, Negation of Quantified Statements, Proof of quantified statements, Nested Quantifiers, Rules of Inferences
3.2.Proof Methods: Basic Terminologies, Proof Methods (Direct Proof, Indirect Proof, Proof by Contradiction, Proof By Contraposition, Exhaustive Proofs and Proof by Cases), Mistakes in Proof
Unit 4: Induction and Recursion (5 Hrs.)
4.1.Induction: mathematical Induction, Strong Induction and Well Ordering, Induction in General
4.2.Recursive Definitions and Structural Induction, Recursive Algorithms, Proving Correctness of Recursive Algorithms
Unit 5: Counting and Discrete Probability (9 Hrs.)
5.1.Counting: Basics of Counting, Pigeonhole Principle, Permutations and Combinations, Two Element Subsets, Counting Subsets of a Set, Binomial Coefficients, Generalized
Permutations and Combinations, Generating Permutations and Combinations
5.2.Discrete Probability: Introduction to Discrete Probability, Probability Theory, Probability Calculation in Hashing, Expected Value and Variance, Randomized Algorithms
5.3.Advanced Counting: Recurrence Relations, Solving Recurrence Relations (Homogeneous and NonHomogeneous equations), Introduction to Divide and Conquer Recurrence Relations
Unit 6: Relations and Graphs (12 Hrs.)
6.1.Relations: Relations and their Properties, Nary Relations with Applications, Representing Relations, Closure of Relations, Equivalence Relations, Partial Ordering
6.2.Graphs: Graphs Basics, Graph Types, Graph Models, Graph Representation, Graph Isomorphism, Connectivity in Graphs, Euler and Hamiltonian Path and Circuits, Matching Theory, Shortest Path Algorithm (Dijkstra’s Algorithm), Travelling Salesman Problem, Graph Coloring
6.3.Trees: Introduction and Applications, Tree Traversals, Spanning Trees, Minimum Spanning Trees (Kruskal’s Algorithm)
6.4.Network Flows: Graph as Models of Flow of Comodities, Flows, Maximal Flows and Minimal Cuts, The Max FlowMin Cut Theorem
Laboratory Works:
The laboratory work consists of implementing the algorithms and concepts discussed in the class.
Student should implement problems with following concepts;
 Set Operations and Boolean Matrix Operations
 Primility Testing, Number Theory Algorithms, and Operations on Integers
 Counting and Some Recursive Algorithms
 Algorithms for Relations, Graphs
Text Books:
 Kenneth H. Rosen, Discrete mathematics and its applications, Seventh Edition McGraw Hill Publication, 2012.
 Bernard Kolman, Robert Busby, Sharon C. Ross, Discrete Mathematical Structures, Sixth Edition Pearson Publications, 2015
 Joe L Mott, Abraham Kandel, Theodore P Baker, Discrete Mathematics for Computer Scientists and Mathematicians, Printice Hall of India, Second Edition, 2008
Reference Books:
1.Ken Bogart, Scot Drysdale, Cliff Stein, Discrete Mathematics for Computer Scientists, First Edition AddisonWesley, 2010
Course Title 2: ObjectOriented Programming
Full Marks: 60 + 20 + 20
Course No: CSC161
Pass Marks: 24 + 8 + 8
Nature of Course: Theory + Lab
Credit Hrs: 3
Semester: II
Course Description: The course covers the basic concepts of object oriented programming using C++ programming language.
Course Objectives:The main objective of this course is to understand object oriented programming and advanced C++ concepts such as composition of objects, operator overloads, inheritance and polymorphism, file I/O, exception handling and templates.
Course Contents:
Unit 1: Introduction to Object Oriented Programming (3 Hrs.)
Overview of structured programming approach, Object oriented programming approach, Characteristics of object oriented languages
Unit 2: Basics of C++ programming (5 Hrs.)
C++ Program Structure, Character Set and Tokens, Data Type, Type Conversion, Preprocessor Directives, Namespace, Input/Output Streams and Manipulators, Dynamic Memory Allocation with new and delete, Control Statements.
Functions: Function Overloading, Inline Functions, Default Argument, Pass by Reference, Return by Reference, Scope and Storage Class.
Pointers: Pointer variables declaration & initialization, Operators in pointers, Pointers and Arrays, Pointer and Function.
Unit 3: Classes & Objects (8 Hrs.)
A Simple Class and Object, Accessing members of class, Initialization of class objects:
(Constructor, Destructor), Default Constructor, Parameterized Constructor, Copy Constructor,
The Default Copy Constructor, Objects as Function Arguments, Returning Objects from Functions, Structures and Classes, Memory allocation for Objects, Static members, Member functions defined outside the class.
Unit 4: Operator Overloading (7 Hrs.)
Fundamental of operator overloading, Restriction on operator overloading, Operator functions as a class members, Overloading unary and binary operator, Data Conversion (basic to basic, basic to userdefined, userdefined to basic, userdefined to userdefined)
Unit 5: Inheritance (7 Hrs.)
Introduction to inheritance, Derived Class and Base Class, Access Specifiers (private, protected, and public), Types of inheritance, Public and Private Inheritance, Constructor and Destructor in derived classes, Aggregation
Unit 6: Virtual Function, Polymorphism, and miscellaneous C++ Features (5 Hrs.)
Concept of Virtual functions, Late Binding, Abstract class and pure virtual functions, Virtual Destructors, Virtual base class, Friend function and Static function, Assignment and copy initialization, Copy constructor, This pointer, Concrete classes, Polymorphism and its roles.
Unit 7: Function Templates and Exception Handling (4 Hrs.)
Function templates, Function templates with multiple arguments, Class templates, templates and inheritance, Exceptional Handling (Try, throw and catch), Use of exceptional handling.
Unit 8: File handling (6 Hrs.)
Stream Class Hierarchy for Console Input /Output, Unformatted Input /Output, Formatted Input /Output with ios Member functions, Formatting with Manipulators, Stream Operator Overloading, File Input/output with Streams, Opening and Closing files, Read/Write from File, File Access Pointers and their Manipulators, Sequential and Random Access to File, Testing Errors during File Operations
Laboratory Works:
Students should be able to implement the concepts of Object Oriented Programming using C++ language.
Text Book:
 Robert Lafore, Object Oriented Programming in C++, Fourth Edition, SAMS publications.
 Herbert Schildt, C++ The Complete Reference, Fourth Edition, Tata McGraw Hill Publication.
Reference Books:
 Deitel and Deitel, C++ How to Program, Third Edition, Pearson Publication.
 Joyce Farrell, Objectoriented programming using C++, Fourth Edition, Cengage Learning.
Course Title 3: Microprocessor
Full Marks: 60 + 20 + 20
Code: CSC162
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: II
Course Description: This course contains of fundamental concepts of computer organization, basic I/O interfaces and Interrupts operations.
Course Objectives: The course objective is to introduce the operation, programming and application of microprocessor.
Course Contents:
Unit 1: Introduction (4 Hrs.)
Introduction to Microprocessor, Components of a Microprocessor: Registers, ALU and control & timing, System bus (data, address and control bus), Microprocessor systems with bus organization
Unit 2: Basic Architecture (7 Hrs.)
Microprocessor Architecture and Operations, Memory, I/O devices, Memory and I/O operations, 8085 Microprocessor Architecture, Address, Data And Control Buses, 8085 Pin Functions,
Demultiplexing of Buses, Generation Of Control Signals
Unit 3: Instruction Cycle (3 Hrs.)
Fetch Operation and Timing Diagram; Execute Operation and Timing Diagram, Instruction Cycle, Machine Cycle, TStates, TStates, Memory Interfacing
Unit 4: Assembly Language Programming (10 Hrs.)
Assembly instruction format, Instruction Types, Mnemonics, Operands, Macro assemblers, Linking, Assembler directives, Addressing Modes, Simple sequence programs, Flags, Branch, Jumps, WhileDo, RepeatUntil, IfThenElse and Multiple Ifthen Programs, Debugging
Unit 5: Basic I/O, Memory R/W and Interrupt Operations (6 Hrs.)
Memory Read, Memory Write, I/O Read, I/O Write, Direct Memory Access, Interrupt, Types, Interrupt Masking
Unit 6: Input/ Output Interfaces (6 Hrs.)
Interfacing Concepts, Ports, Interfacing Of I/O Devices, Interrupts In 8085, Programmable Interrupt Controller 8259A, Programmable Peripheral Interface 8255A
Unit 7: Advanced Microprocessors (9 Hrs.)
8086: logical block diagram and segments, 80286: Architecture, Registers, (Real/Protected mode), Privilege levels, descriptor cache, Memory access in GDT and LDT, multitasking, addressing modes, flag register 80386: Architecture, Register organization, Memory access in protected mode, Paging
Laboratory Works:
The laboratory work includes Assembly language programming using 8085/8086/8088 trainer kit. The programming should include: Arithmetic operation, base conversion, conditional branching etc. The lab work list may include following concepts:
 Assembly language program using 8085 microprocessor kit.
 Use of all types of instructions and addressing modes.
 Arrays and the concept of Multiplications and Division operations on Microprocessor.
 Assembly language programming, using any types of Assembler, including the different functions of Int 10h, and 12h
Text Books:
1.Ramesh S.Gaonkar, Microprocessor Architecture, Programming, and Applications with 8085, Prentice Hall
Reference Books:
 A.P.Malvino and J.A.Brown, Digital Computer Electronics, 3rd Edition, Tata McGraw
 Hill D.V.Hall, Microprocessors and Interfacing – Programming and Hardware, McGraw Hill
 8000 to 8085 Introduction to 8085 Microprocessor for Engineers and Scientists, A.K.Gosh, Prentice Hall
Course Title 4: Mathematics II
Full Marks: 80 + 20
Code: MTH163
Pass Marks: 32 + 8
Nature of the Course: Theory
Credit Hrs: 3
Semester: II
Course Description: The course contains concepts and techniques of linear algebra. The course topics include systems of linear equations, determinants, vectors and vector spaces, eigen values and eigenvectors, and singular value decomposition of a matrix.
Course Objectives: The main objective of the course is to make familiarize with the concepts and techniques of linear algebra, solve system of linear equation with GaussJordon method, to impart knowledge of vector space and subspace, eigenvalues and eigenvectors of a matrix and get the idea of diagonalization of a matrix, linear programming, Group, Ring, and Field.
Course Contents:
Unit 1: Linear Equations in Linear Algebra (5 Hrs.)
System of linear equations, Row reduction and Echelon forms, Vector equations, The matrix equations Ax = b, Applications of linear system, Linear independence
Unit 2:Transformation (4 Hrs.)
Introduction to linear transformations, the matrix of a linear Transformation, Linear models in business, science, and engineering
Unit 3: Matrix Algebra (5 Hrs)
Matrix operations, The inverse of a matrix, Characterizations of invertible matrices, Partitioned matrices, Matrix factorization, The Leontief input output model, Subspace of R^{n}, Dimension and rank
Unit 4: Determinants (4 Hrs.)
Introduction, Properties, Cramer’s rule, Volume and linear transformations
Unit 5: Vector Spaces (5 Hrs.)
Vector spaces and subspaces, Null spaces, Column spaces, and Linear transformations, Linearly independent sets: Bases, Coordinate systems
Unit 6: Vector Space Continued (4 Hrs.)
Dimension of vector space and Rank, Change of basis, Applications to difference equations, Applications to Markov Chains
Unit 7: Eigenvalues and Eigen Vectors (5 Hrs.)
Eigenvectors and Eigenvalues, The characteristic equations, Diagonalization, Eigenvectors and linear transformations, Complex eigenvalues, Discrete dynamical systems, Applications to differential equations
Unit 8: Orthogonality and Least Squares (5 Hrs.)
Inner product, Length, and orthoganility, Orthogonal sets, Orthogonal projections, The GramSchmidt process, Least squares problems, Application to linear models, Inner product spaces, Applications of inner product spaces
Unit 9: Groups and Subgroups (5 Hrs.)
Binary Operations, Groups, Subgroups, Cyclic Groups
Unit 10: Rings and Fields (4 Hrs.)
Rings and Fields, Integral domains
Text Books:
 Linear Algebra and Its Applications, David C. Lay, 4^{th} Edition, Pearson Addison Wesley.
 Linear Algebra and Its Applications, Gilbert Strang, 4^{th} Edition, Addison, CENGAGE Learning.
Course Title 5 :Statistics I
Full Marks: 60 + 20 + 20
Code: STA164
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: II
Course Description: This course contains basics of statistics, descriptive statistics, probability, sampling, random variables and mathematical expectations, probability distribution, correlation and regression.
Course Objectives: The main objective of this course is to impart the knowledge of descriptive statistics, correlation, regression, sampling, theoretical as well as applied knowledge of probability and some probability distributions.
Course Contents:
Unit 1: Introduction (4 Hrs.)
Basic concept of statistics; Application of Statistics in the field of Computer Science & Information technology; Scales of measurement; Variables; Types of Data; Notion of a statistical population
Unit 2: Descriptive Statistics (6 Hrs.)
Measures of central tendency; Measures of dispersion; Measures of skewness; Measures of kurtosis; Moments; Steam and leaf display; five number summary; box plot
Problems and illustrative examples related to computer Science and IT
Unit 3: Introduction to Probability (8 Hrs.)
Concepts of probability; Definitions of probability; Laws of probability; Bayes theorem; prior and posterior probabilities
Problems and illustrative examples related to computer Science and IT
Unit 4: Sampling (3 Hrs.)
Definitions of population; sample survey vs. census survey; sampling error and non sampling error; Types of sampling
5. Random Variables and Mathematical Expectation (5 Hrs.)
Concept of a random variable; Types of random variables; Probability distribution of a random variable; Mathematical expectation of a random variable; Addition and multiplicative theorems of expectation
Problems and illustrative examples related to computer Science and IT
Unit 6: Probability Distributions (12 Hrs.)
Probability distribution function, Joint probability distribution of two random variables; Discrete distributions: Bernoulli trial, Binomial and Poisson distributions; Continuous distribution: Normal distributions; Standardization of normal distribution; Normal distribution as an approximation of Binomial and Poisson distribution; Exponential, Gamma distribution
Problems and illustrative examples related to computer Science and IT
Unit 7: Correlation and Linear Regression (7 Hrs.)
Bivariate data; Bivariate frequency distribution; Correlation between two variables; Karl Pearson’s coefficient of correlation(r); Spearman’s rank correlation; Regression Analysis: Fitting of lines of regression by the least squares method; coefficient of determination
Problems and illustrative examples related to computer Science and IT
Laboratory Works:
The laboratory work includes using any statistical software such as Microsoft Excel, SPSS, STATA etc. whichever convenient using Practical problems to be covered in the Computerized Statistics laboratory
Practical problems
S. No.  Title of the practical problems  No. of practical problems 
1  Computation of measures of central tendency (ungrouped and grouped data) Use of an appropriate measure and interpretation of results and computation of partition Values  1 
2  Computation measures of dispersion (ungrouped and grouped data) and computation of coefficient of variation.  1 
3  Measures of skewness and kurtosis using method of moments, Measures of Skewness using Box and whisker plot.  2 
4  Scatter diagram, correlation coefficient (ungrouped data) and interpretation. Compute manually and check with computer output.  1 
5  Fitting of lines of regression (Results to be verified with computer output)  1 
6  Fitting of lines of regression and computation of correlation coefficient, Mean residual sum of squares, residual plot.  1 
7  Conditional probability and Bayes theorem  3 
8  Obtaining descriptive statistics of probability distributions  2 
9  Fitting probability distributions in real data (Binomial, Poisson and Normal)  3 
Total number of practical problems  15 
Text Books:
 Michael Baron (2013). Probability and Statistics for Computer Scientists. 2^{nd} Ed., CRC Press, Taylor & Francis Group, A Chapman & Hall Book.
 Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, & Keying Ye (2012).
 Probability & Statistics for Engineers & Scientists. 9^{th} Ed., Printice Hall.
Reference Books:
 Douglas C. Montgomery & George C. Ranger (2003). Applied Statistics and Probability for Engineers. 3^{rd} Ed., John Willey and Sons, Inc.
 Richard A. Johnson (2001). Probability and Statistics for Engineers. 6^{th} Ed., Pearson Education, India

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