B. Sc (Computer Science), MSU, CCSU & All Universities

Computer Based Numerical and Statistical Techniques – Adv

Exam Preparation for Computer Based Numerical and Statistical Techniques: This model paper is designed for graduation students as per ... Show more

Instructor

Teach To India

125 Students enrolled

2 reviews

Description
Curriculum
Reviews

Model Question Paper

Computer Based Numerical and Statistical Techniques

Key Features

Unit-wise Short Notes
Each unit includes a summary in both languages, making revision faster and more effective.
Extensive MCQ Practice
1500+ MCQ Practice Questions: This comprehensive question bank includes 1500+ multiple-choice questions (MCQs). Each unit contains approximately 150 MCQs covering a wide range of cognitive levels such as remembering, understanding, application, and analysis.
Exam Practice Paper with Mock Tests
Includes three full-length mock tests for real exam practice.
Latest Syllabus as per NEP
The syllabus aligns with the latest National Education Policy (NEP) and follows the exam patterns of MSU, CCSU, and other universities following the NEP.
Designed by Experts
This question bank has been meticulously prepared by subject matter experts to ensure accuracy and relevance.

Why Choose This Model Paper?

Complete Exam Preparation: Unit-wise summaries, MCQ practice, and mock tests provide a complete study solution.
Latest NEP-Based Pattern: Ensures compliance with the latest university exam structure.

Program Class: Diploma / B.Sc. CS		Year: II		Semester: IV
Subject: B.Sc. Computer Science
Course Title: Computer Based Numerical and Statistical Techniques
Course Learning Outcomes: On completion of this course, learners will be able to: · Obtain an intuitive and working understanding of numerical methods for the basic problems of numerical analysis. · Gain experience in the implementation of numerical methods using a computer. · Trace error in these methods and need to analyze and predict it. · Provide knowledge of various significant and fundamental concepts to inculcate in the students an adequate understanding of the application of Statistical Methods. · Demonstrate the concepts of numerical methods used for different applications.
Credits: 4			Core Compulsory
Max. Marks: –25+75			Min. Passing Marks: 33
Unit	Topics
I	Introduction: Numbers and their accuracy, Computer Arithmetic, Errors and their Computation, General error formula, Error in a Series Approximation. Solution of Algebraic and Transcendental Equation: Bisection Method, Iteration method, Method of false position, Newton-Raphson method, Methods of finding complex roots, Graffe’s method, Rate of convergence of Iterative methods, Polynomial Equations.
II	Interpolation: Finite Differences, Difference tables, Newton’s forward and backward formula Central Difference Formulae: Gauss forward and backward formula, Stirling’s formula, Bessel’s formula. Laplace Everett’s formula. Interpolation with unequal intervals: Lagrange’s Interpolation, Newton Divided difference formula, Hermite’s Interpolation,
III	Numerical Integration and Differentiation: Numerical differentiation Numerical Integration: Trapezoidal rule, Simpson’s 1/3 and 3/8 rule, Boole’s rule, Weddle’s rule.
IV	Solution of ordinary differential Equations: Picard’s Method, Euler’s Method, aylor’s Method, Runge-Kutta Methods, Predictor Corrector Methods, and Stability of olution, some application-based questions on Numerical differentiation and Numerical Integration
V	Statistical Computation: Frequency chart, Curve fitting by method of least squares, fitting of straight lines, polynomials, exponential curves etc, Data fitting with Cubic splines, Regression Analysis, Linear and nonlinear Regression.