Semester - I
and Semester -II
F.Y.B.Sc.(Computer Science)
| Sr. No. | Subject | Download link |
|---|---|---|
| 1 | Computer Science | |
| 2 | Mathematics | |
| 3 | Electronic Science | |
| 4 | STATISTICS |
SYBSC(comp.sci.) Syllabus
2019 pattern. Effective from 2020,
S.Y.B.Sc.(Computer Science)
| Sr. No. | Subject | Download link |
|---|---|---|
| 1 | Computer Science | |
| 2 | Mathematics |
|
| 3 | Electronic Science | |
| 4 | ENGLISH |
Sem-1.
Group and Coding Theory,
Semester III
MTC-231 : Groups and Coding Theory
Unit 1. Integers [05 Lectures]
1.1 Division Algorithm (without Proof)
1.2 G.C.D. using division algorithm and expressing it as linear combination
1.3 Euclid’s lemma
1.4 Equivalence relation (revision), Congruence relation on set of integers, Equivalence class partition
Unit 2. Groups [03 Lectures]
2.1 Binary Operation
2.2 Group: Definition and Examples
2.3 Elementary Properties of Groups
Unit 3. Finite Groups and Subgroups [10 Lectures]
3.1 Order of a group, order of an element
3.2 Examples (Zn, +) and (U(n), *)
3.3 Subgroup definition, Finite subgroup test, subgroups of Zn
3.4 Generator, cyclic group, finding generators of Zn( Corollary 3,4 without proof)
3.5 Permutation group, definition, composition of two permutations, representation
as product of disjoint cycles, inverse and order of a permutation, even/ odd permutation
3.6 Cosets: Definition, Examples and Properties, Lagrange Theorem(without Proof)
Unit 4. Groups and Coding Theory [18 Lectures]
4.1 Coding of Binary Information and Error detection
4.2 Decoding and Error Correction
4.3 Public Key Cryptography
Text Books:-
1. Contemporary Abstract Algebra By J. A, Gallian (Seventh Edition)
Unit 1:Chapter 0, Unit 2: Chapter 2, Unit 3: Chapter 3 ,4, 5 and 7
2. Discrete Mathematical Stuctures By Bernard Kolman, Robert C. Busby and Sharon Ross (6th Edition) Pearson Education Publication
Unit 4: Chapter 11
MTC-232 : Numerical Techniques
Unit 1: Algebraic and Transcendental Equation [04 Lectures]
1.1 Introduction to Errors
1.2 False Position Method
1.3 Newton-Raphson Method
Unit 2: Calculus of Finite Differences and Interpolation [16 Lectures]
2.1 Differences
2.2. Forward Differences
2.3 Backward Differences
2.4 Central Differences
2.5 Other Differences (δ, μ operators)
2.6 Properties of Operators
2.7 Relation between Operators
2.8 Newton’s Gregory Formula for Forward Interpolation
2.9 Newton’s Gregory Formula for Backward Interpolation
2.10 Lagrange’s Interpolation Formula
2.11 Divided Difference
2.12 Newton’s Divided Difference Formula
Unit 3: Numerical Integration [08 Lectures]
3.1 General Quadrature Formula
3.2 Trapezoidal Rule
3.3 Simpson’s one-Third Rule
3.4 Simpson’s Three-Eight Rule
Unit 4: Numerical Solution of Ordinary Differential Equation [08 Lectures]
4.1 Euler’s Method
4.2 Euler’s Modified Method
4.3 Runge-Kutta Methods
Text Book:-
1. A textbook of Computer Based Numerical and Statistical Techniques, by A. K. Jaiswal and Anju Khandelwal. New Age International Publishers.
Unit 1: Chapter 2: Sec. 2.1, 2.5, 2.7
Unit 2: Chapter 3: Sec. 3.1, 3.2, 3.4, 3.5, Chapter 4: Sec. 4.1, 4.2, 4.3,
Chapter 5: Sec. 5.1, 5.2, 5.4, 5.5
Unit 3: Chapter 6: Sec. 6.1, 6.3, 6.4, 6.5, 6.6, 6.7
Unit 4: Chapter 7: Sec. 7.1, 7.4, 7.5, 7.6
Reference Books:-
1. S.S. Sastry; Introductory Methods of Numerical Analysis, 3rd edition, Prentice Hall of India, 1999.
2. H.C. Saxena; Finite differences and Numerical Analysis, S. Chand and Company.
3. K.E. Atkinson; An Introduction to Numerical Analysis, Wiley Publications. 4. Balguruswamy; Numerical Analysis.
Practical based on python programming language-1.
Practicals:
Practical 1: Introduction to Python, Python Data Types-I (Unit 1)
Practical 2: Python Data Types- II (Unit 2)
Practical 3: Control statements in Python-I (Unit 3- 3.1, 3.2)
Practical 4: Control statements in Python-II (Unit 3- 3.3)
Practical 5: Application : Matrices (Unit 4 – 4.1-4.3)
Practical 6: Application : Determinants, system of Linear Equations (Unit 4- 4.4, 4.5)
Practical 7: Application : System of equations (Unit 4- 4.5)
Practical 8: Application : Eigenvalues, Eigenvectors (Unit 4 – 4.6)
Practical 9: Application : Eigenvalues, Eigenvectors (Unit 4 – 4.6)
Practical 10: Application : Roots of equations (Unit 5 – 5.1)
Practical 11: Application : Numerical integration (Unit 5 – 5.2, 5.3)
Practical 12: Application : Numerical integration (Unit 5 – 5.4)
Detail syllabus given in following pdf.
Sem-2. Computational Geometry,
Operations Research,
Practical based on python programming language-2.
Semester - IV
MTC-241: Computational Geometry
Unit 1. Two dimensional transformations: [12 Lectures]
1.1 Introduction.
1.2 Representation of points.
1.3 Transformations and matrices.
1.4 Transformation of points.
1.5 Transformation of straight lines
1.6 Midpoint Transformation
1.7 Transformation of parallel lines
1.8 Transformation of intersecting lines
1.5 Transformation: rotations, reflections, scaling, shearing.
1.6 Combined transformations.
1.7 Transformation of a unit square.
1.8 Solid body transformations.
1.9 Translations and homogeneous coordinates.
1.10 Rotation about an arbitrary point.
1.11 Reflection through an arbitrary line.
Unit 2. Three dimensional transformations: [08 Lectures]
2.1 Introduction.
2.2 Three dimensional – Scaling, shearing, rotation, reflection, translation.
2.3 Multiple transformations.
2.4 Rotation about – an axis parallel to coordinate axes, an arbitrary line
2.5 Reflection through – coordinate planes, planes parallel to coordinate
planes , an arbitrary plane
Unit 3. Projection [08 Lectures]
3.1 Orthographic projections.
3.2 Axonometric projections.
3.3 Oblique projections
3.4 Single point perspective projection
Unit 4. Plane and space Curves: [08 Lectures ]
4.1 Introduction.
4.2 Curve representation.
4.3 Parametric curves.
4.4 Parametric representation of a circle and generation of circle.
4.5 Bezier Curves – Introduction, definition, properties (without proof),
Curve fitting (up to n = 3), equation of the curve in matrix form (upto n = 3)
Textbook:
1. D. F. Rogers, J. A. Adams, Mathematical elements for Computer graphics,
Mc Graw Hill Intnl Edition.
Unit 1: Chapter 2: Sec. 2-1 to 2.17
Unit 2: Chapter 3: Sec. 3.1 to 3.10,
Unit 3: Chapter 3: Sec. 3.12 to 3.14
Unit 4: Chapter 4: Sec. 4.1, 4.2, 4.5, Chapter 5: Sec. 5.1, 5.8
Reference books:
1. Computer Graphics with OpenGL, Donald Hearn, M. Pauline Baker, Warren Carithers, Pearson (4th Edition)
2. Schaum Series, Computer Graphics.
Detail syllabus given in following pdf.
MTC-242: Operations Research
Unit 1: Linear Programming Problem I [12 Lectures]
1.1 Introduction Definition and Examples
1.2 Problem solving using Graphical method
1.3 Theory of Linear Programming, Slack and surplus variables, Standard form of LPP,
Some important definitions, Assumptions in LPP, Limitations of Linear
programming, Applications of Linear programming, Advantages of
programming Techniques
1.4 Simplex method, Big- M-method
Unit 2: Linear Programming Problem II [08 Lectures]
2.1 Special cases of LPP : Alternative solution, Unbounded solution, Infeasible solution
2.2 Duality in Linear Programming, Primal to dual conversion, Examples
Unit 3: Assignment Models [06 Lectures ]
3.1 Assignmment Model -Introduction
3.2 Hungerian method for Assignment problem
Unit 4: Transportation Models [10 Lectures]
4.1 Introduction, Tabular representation
4.2 Methods of IBFS (North-West rule, Matrix-minima, Vogel’s Approximation), Algorithms
4.3 The Optimality Test of Transportation Model (MODI method only)
Text Book:-
Operation Research (12 th Edition), by S.D.Sharma.
Unit 1: Chapter 1: Sec. 1.1, 1.3-1, 1.3-2, 1.5, 1.6, 1.8, 1.9, 1.10, 1.11, 1.12,
Chapter 3: Sec. 3.1, 3.2, 3.3, 3. 4, 3.5-4,
Unit 2: Chapter 3: Sec. 3.8-1,3.8-2, Chapter 5: Sec. 5.1-1, 5.2-1,5.3,5.7-1, 5.7-2
Unit 3: Chapter 9: Sec. 9.1, 9.2, 9.4-1, 9.4-2, 9.5, 9.6, 9.7-1, 9.7-2
Unit 4: Chapter 10: 10.1, 10.2, 10.5, 10.8-1,10.9, 10.10
Reference Books:-
1. Operations Research by H. A. Taha
2.Operations Research by R. Panneerselvam, Prentice Hall of India.
3. Principles of Operations Research by H. M. Wagner, Prentice Hall of India.
4. Operations Research by Gupta and Hira.
5. Operation Research by J.K. Sharma
Detail syllabus given in following pdf.
MTC-243: Mathematics Practical: Python Programming Language-II
Unit 1: 2D, 3D Graphs
1.1 Installation of numpy, matplotlib packages
1.2 Graphs plotting of functions such as ... etc.
1.3 Different formats of graphs.
1.3 Three-dimensional Points and Lines
1.4 Three-dimensional Contour Plots
1.5 Wireframes and Surface Plots
1.6 Graphs plotting of functions such as... etc.
Unit 2: Computational Geometry
1.1 Points: The distance between two points, Lists of Points - the PointList class, Integer
point lists, Ordered Point sets, Extreme Points of a PointList, Random sets of Points not
in general position
2.2 Points: Displaying Points and other geometrical objects, Lines, rays, and line segments,
The geometry of line segments, Displaying lines, rays and line segments
2.3 Polygon : Representing polygons in Python, Triangles, Signed area of a triangle,
Triangles and the relationships of points to lines, is Collinear, is Left, is Left On, is Right, is Right On, Between
2.4 Two dimensional rotation and reflection
2.5 Three dimensional rotation and reflection
2.6 Generation of Bezier curve with given control points
Unit 3: Study of Operational Research in Python
3.1 Linear Programming in Python
3.2 Introduction to Simplex Method in Python
Practicals:
Practical 1: Graph Plotting (Unit 1 – 1.1 – 1.3)
Practical 2: Graph Plotting (Unit 1 – 1.4 – 1.7)
Practical 3: Application to Computational Geometry (Unit 2 – 2.1)
Practical 4: Application to Computational Geometry (Unit 2 – 2.2)
Practical 5: Application to Computational Geometry (Unit 2 – 2.3)
Practical 6: Study of Graphical aspects of Two dimensional transformation matrix using matplotlib
Practical 7: Study of Graphical aspects of Three dimensional transformation matrix using matplotlib
Practical 8: Study of Graphical aspects of Three dimensional transformation matrix using matplotlib
Practical 9: Study of effect of concatenation of Two dimensional and Three dimensional transformations
Practical 10: Generation of Bezier curve using given control points
Practical 11: Study of Operational Research in Python (Unit 3.1)
Practical 12: Study of Operational Research in Python (Unit 3.2)
Text Books:-
1. Jaan Kiusalaas, Numerical Methods in Engineering with Python, Cambridge University Press, (2005)
Sections: 3
2. Robert Johansson, Introduction to Scientific Computing in Python Section: 1
3. Jason Brownlee, Basics of Linear Algebra for Machine Learning, Discover the Mathematical Language of Data in Python
Sections: 2
Reference Books:-
1. Lambert K. A., Fundamentals of Python - First Programs, Cengage Learning India, 2015.
2. Guzdial, M. J., Introduction to Computing and Programming in Python, Pearson India.
3. Perkovic, L., Introduction to Computing Using Python, 2/e, John Wiley, 2015.
4. Zelle, J., Python Programming: An Introduction to Computer Science, Franklin, Beedle and Associates Inc.
5. Jim Arlow, Interactive Computational Geometry in Python Note:
Detail syllabus given in following pdf.
(i) In paper -I , paper-II and paper-III, each course is of 50 marks ( 35 marks theory and 15 marks internal examination). (ii) Paper III: Mathematics Practical - MTC-233 and MTC-243 is practical course and is of 50 marks. Practicals shall be perforemed on computer. Examination:
A) Pattern of examination: Paper- I, Paper-II and paper-III: Semesterwise B) Pattern of question papers: For Paper -I and Paper-II
Q 1. Attempt any 05 out of 07 questions each of 01 marks. [05 Marks]
Q 2. Attempt any 02 out of 04 questions each of 05 marks. [10 Marks]
Q 3. Attempt any 02 out of 04 questions each of 05 marks. [10 Marks]
Q 4. Attempt any 02 out of 04 questions each of 10 marks. [10 Marks]
C) Instructions Regarding Practical:
Paper-III:Mathematics Practical:
(i) Mathematics Practical, external examiner shall be appointed by
Savitribai Phule Pune University, Pune.
(ii) The minimum duration of parctical examination is 3 hours.
(iii) The semester examination is of 35 marks 15 marks are from internal
evaluation (Journal, attendence and viva-voce or internal test etc.)
(iv) The slips for the questions on programming and problem solving
using python shall be prepared and provided and these can be used
at least for 3 years. D) Standard of passing:
For Paper- I, Paper-II and Papaer -III: 14 Marks out of 35 and 06 marks out of 15
marks and total should be 20 marks for each course.
Use following link for previous year question papers of BCA
Use following link for previous year question papers of B.Sc (Computer Science)
FYBSc(Cyber and Digital Science) question paper
Notes of Digital Communication and Networking
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