techinaut course
TECHINAUT

B LEVEL

4.5 (15)
NIELIT

COMPUTER BASED NUMERICAL AND STATISTICAL

TECHNIQUES B4.1-R4- B level (NIELIT)

Computer-Based Numerical and Statistical Techniques is a course that teaches students various numerical analysis methods.It includes high-speed computation, integration, differentiation, and the Test of Significance.

techinaut

10 Lessons

techinaut

120 Hours - (Theory: 48 hrs + Practical: 72 hrs)

techinaut

100+ students enrolled

Overview
Course Description

The course focuses on the methods for statistical analysis. Learn to analyze various data sets, including those from the physical sciences, social sciences, and biology. The course gives students a conceptual background for designing and analyzing programs.

What you'll learn
  • How to use the mathematics and statistical procedures fundamental to many scientific fields.
  • Numerical techniques include both direct and iterative methods.
  • The GMRES and the conjugate gradient method are examples of iterative methods.
  • Numerical and statistical techniques are used to optimize performance and minimize error.
  • They analyze astrological data, detect environmental changes, and build mathematical models of dynamical systems.
  • Learn about the methods used in market research and quality control.
Course Content
  • Errors in Numerical CalculationsLecture 1.1 Errors and their Computation

    Errors in Numerical Calculations 02:53
  • Errors in Numerical CalculationsLecture 1.2 A general error formula

    Errors in Numerical Calculations 02:53
  • Errors in Numerical CalculationsLecture 1.3 Error in Series Approximation

    Errors in Numerical Calculations 02:53
  • Algebraic and Transcendental EquationsLecture 2.1 Bisection Method, Iteration Method

    Algebraic and Transcendental Equations 02:53
  • Algebraic and Transcendental EquationsLecture 2.2 Newton – Raphson Method

    Algebraic and Transcendental Equations 02:53
  • System of linear EquationsLecture 3.1 Solution of Linear Systems

    System of linear Equations 02:53
  • System of linear EquationsLecture 3.2 Iterative Method: Gauss-Siedal Method

    System of linear Equations 02:53
  • InterpolationLecture 4.1 Finite differences, Newton Interpolation Formula

    Interpolation 02:53
  • InterpolationLecture 4.2 Lagrange’s Interpolation Formula

    Interpolation 02:53
  • Numerical differentiation & IntegrationLecture 5.1 Numerical differentiation

    Numerical differentiation & Integration 02:53
  • Numerical differentiation & IntegrationLecture 5.2 Numerical Integration: Trapezoidal rule

    Numerical differentiation & Integration 02:53
  • Probability, Conditional Probability and independenceLecture 6.1 Motivation, Probability Models, Probability Axioms

    Probability, Conditional Probability and independence 02:53
  • Probability, Conditional Probability and independenceLecture 6.2 Conditional Probability, Bayes’ formula

    Probability, Conditional Probability and independence 02:53
  • Probability, Conditional Probability and independenceLecture 6.3 Independent events

    Probability, Conditional Probability and independence 02:53
  • Random Variables (RVs) and ExpectationLecture 7.1 Introduction, Discrete RV, Distribution Function

    Random Variables (RVs) and Expectation 02:53
  • Random Variables (RVs) and ExpectationLecture 7.2 Continuous RVs, Probability Density Function, Uniform RVs

    Random Variables (RVs) and Expectation 02:53
  • Random Variables (RVs) and ExpectationLecture 7.3 correlation, Expectation of Sum of RVs, Markov

    Random Variables (RVs) and Expectation 02:53
  • SOME IMPORTANT DISTRIBUTIONSLecture 8.1 Discrete Distributions: Binomial, Poisson, Geometric

    SOME IMPORTANT DISTRIBUTIONS 02:53
  • SOME IMPORTANT DISTRIBUTIONSLecture 8.2 Continuous Distributions: Uniform, Exponential

    SOME IMPORTANT DISTRIBUTIONS 02:53
  • Statistical InferenceLecture 9.1 Parameter Estimation: Random Sample, Statistic

    Statistical Inference 02:53
  • Statistical InferenceLecture 9.2 Confidence Intervals: sampling from Normal Population

    Statistical Inference 02:53
  • Statistical InferenceLecture 9.3 Hypothesis Testing: Null Hypothesis, Alternative Hypothesis

    Statistical Inference 02:53
  • RegressionLecture 10.1 Introduction, Least squares regression curve

    Regression 02:53
  • RegressionLecture 10.2 Least Squares curve fitting, Coefficient of Determination

    Regression 02:53
  • RegressionLecture 10.3 Students should use any statistical Software like Excel, SPSS

    Regression 02:53
About Us
techinaut course
TECHINAUT

B LEVEL

4.5 Instructor Rating
techinaut course details

100+ Courses

techinaut course details

20+ Faculty

techinaut course details

Industry Expert

techinaut course details

45000+ students enrolled

UI/UX Designer, with 7+ Years Experience. Guarantee of High Quality Work.

Skills: Web Design, UI Design, UX/UI Design, Mobile Design, User Interface Design, Sketch, Photoshop, GUI, Html, Css, Grid Systems, Typography, Minimal, Template, English, Bootstrap, Responsive Web Design, Pixel Perfect, Graphic Design, Corporate, Creative, Flat, Luxury and much more.

Available for:

  • 1. Full Time Office Work
  • 2. Remote Work
  • 3. Freelance
  • 4. Contract
  • 5. Worldwide
Reviews
techinaut reviews
Nicole Brown

UX/UI Designer

4.5 Instructor Rating

“ This is the second Photoshop course I have completed with Cristian. Worth every penny and recommend it highly. To get the most out of this course, its best to to take the Beginner to Advanced course first. The sound and video quality is of a good standard. Thank you Cristian. “

Reply
Post A comment