Master Complete Statistics For Computer Science – I

Master Complete Statistics For Computer Science - I

Master Complete Statistics For Computer Science – I

Course In Probability & Statistics Important For Machine Learning, Artificial Intelligence, Data Science, Neural Network

Language: english

Note: 3.6/5 (190 notes) 52,892 students

Instructor(s): Shilank Singh

Last update: 2022-08-01

What you’ll learn

  • Random Variables
  • Discrete Random Variables and its Probability Mass Function
  • Continuous Random Variables and its Probability Density Function
  • Cumulative Distribution Function and its properties and application
  • Special Distribution
  • Two – Dimensional Random Variables
  • Marginal Probability Distribution
  • Conditional Probability Distribution
  • Independent Random Variables
  • Function of One Random Variable
  • One Function of Two Random Variables
  • Two Functions of Two Random Variables
  • Statistical Averages
  • Measures of Central Tendency (Mean, Median, Mode, Geometric Mean and Harmonic Mean)
  • Mathematical Expectations and Moments
  • Measures of Dispersion (Quartile Deviation, Mean Deviation, Standard Deviation and Variance)
  • Skewness and Kurtosis
  • Expected Values of Two-Dimensional Random Variables
  • Linear Correlation
  • Correlation Coefficient and its properties
  • Rank Correlation Coefficient
  • Linear Regression
  • Equations of the Lines of Regression
  • Standard Error of Estimate of Y on X and of X on Y
  • Characteristic Function and Moment Generating Function
  • Bounds on Probabilities

 

Requirements

  • Knowledge of Applied Probability
  • Knowledge of Calculus

 

Description

In today’s engineering curriculum, topics on probability and statistics play a major role, as the statistical methods are very helpful in analyzing the data and interpreting the results.

When an aspiring engineering student takes up a project or research work, statistical methods become very handy.

Hence, the use of a well-structured course on probability and statistics in the curriculum will help students understand the concept in depth, in addition to preparing for examinations such as for regular courses or entry-level exams for postgraduate courses.

In order to cater the needs of the engineering students, content of this course, are well designed. In this course, all the sections are well organized and presented in an order as the contents progress from basics to higher level of statistics.

As a result, this course is, in fact, student friendly, as I have tried to explain all the concepts with suitable examples before solving problems.

This 150+ lecture course includes video explanations of everything from Random Variables, Probability Distribution, Statistical Averages, Correlation, Regression, Characteristic Function, Moment Generating Function and Bounds on Probability, and it includes more than 90+ examples (with detailed solutions) to help you test your understanding along the way. “Master Complete Statistics For Computer Science – I” is organized into the following sections:

  • Introduction

  • Discrete Random Variables

  • Continuous Random Variables

  • Cumulative Distribution Function

  • Special Distribution

  • Two – Dimensional Random Variables

  • Random Vectors

  • Function of One Random Variable

  • One Function of Two Random Variables

  • Two Functions of Two Random Variables

  • Measures of Central Tendency

  • Mathematical Expectations and Moments

  • Measures of Dispersion

  • Skewness and Kurtosis

  • Statistical Averages – Solved Examples

  • Expected Values of a Two-Dimensional Random Variables

  • Linear Correlation

  • Correlation Coefficient

  • Properties of Correlation Coefficient

  • Rank Correlation Coefficient

  • Linear Regression

  • Equations of the Lines of Regression

  • Standard Error of Estimate of Y on X and of X on Y

  • Characteristic Function and Moment Generating Function

  • Bounds on Probabilities

 

Who this course is for

  • Current Probability and Statistics students
  • Students of Machine Learning, Artificial Intelligence, Data Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering
  • Anyone who wants to study Statistics for fun after being away from school for a while.

 

Course content

  • Introduction
    • Master Complete Statistics For Computer Science – I
    • Course Structure and Curriculum
    • Random Variables Definition
  • Discrete Random Variables
    • Discrete Random Variables – Concept
    • Discrete Random Variables – Solved Example 1 and 2
    • Discrete Random Variables – Solved Example 3
    • Discrete Random Variables – Solved Example 4
    • Discrete Random Variables – Solved Example 5
  • Continuous Random Variables
    • Continuous Random Variables – Concept
    • Continuous Random Variables – Solved Example 1 and 2
    • Continuous Random Variables – Solved Example 3
    • Continuous Random Variables – Solved Example 4
    • Continuous Random Variables – Solved Example 5
    • Continuous Random Variables – Solved Example 6
    • Continuous Random Variables – Solved Example 7
    • Continuous Random Variables – Solved Example 8
  • Cumulative Distribution Function
    • Cumulative Distribution Function – Concept
    • Cumulative Distribution Function – Solved Example 1
    • Cumulative Distribution Function – Solved Example 2
    • Cumulative Distribution Function – Solved Example 3
    • Cumulative Distribution Function – Solved Example 4
    • Cumulative Distribution Function – Solved Example 5
    • Cumulative Distribution Function – Solved Example 6
  • Special Distribution
    • Special Discrete Distribution
    • Special Continuous Distribution
    • Special Distribution – Solved Example 1
    • Special Distribution – Solved Example 2
  • Two – Dimensional Random Variables
    • Two – Dimensional Random Variables – Concept
    • Cumulative Distribution Function – Concept
    • Marginal Probability Distribution – Concept
    • Conditional Probability Distribution – Concept
    • Two – Dimensional Random Variables – Solved Example 1
    • Two – Dimensional Random Variables – Solved Example 2
    • Two – Dimensional Random Variables – Solved Example 3
    • Two – Dimensional Random Variables – Solved Example 4
    • Two – Dimensional Random Variables – Solved Example 5
    • Two – Dimensional Random Variables – Solved Example 6
    • Two – Dimensional Random Variables – Solved Example 7
    • Two – Dimensional Random Variables – Solved Example 8
    • Two – Dimensional Random Variables – Solved Example 9
    • Two – Dimensional Random Variables – Solved Example 10
    • Two – Dimensional Random Variables – Solved Example 11
  • Random Vectors
    • Random Vectors – Concept
  • Function of One Random Variable
    • Function of One Random Variable – Concept
    • Function of One Random Variable – Solved Example 1 and 2
    • Function of One Random Variable – Solved Example 3
    • Function of One Random Variable – Solved Example 4 and 5
    • Function of One Random Variable – Solved Example 6
    • Function of One Random Variable – Solved Example 7
    • Function of One Random Variable – Solved Example 8 and 9
    • Function of One Random Variable – Solved Example 10
    • Function of One Random Variable – Solved Example 11
    • Function of One Random Variable – Solved Example 12
    • Function of One Random Variable – Solved Example 13
    • Function of One Random Variable – Solved Example 14
  • One Function of Two Random Variables
    • One Function of Two Random Variables – Result 1, Solved Example 1
    • One Function of Two Random Variables – Result 1, Solved Example 2
    • One Function of Two Random Variables – Result 1, Solved Example 3
    • One Function of Two Random Variables – Result 2, Solved Example 1
    • One Function of Two Random Variables – Result 3, Solved Example 1
  • Two Functions of Two Random Variables
    • Two Functions of Two Random Variables – Concept, Solved Example 1
    • Two Functions of Two Random Variables – Solved Example 2
    • Two Functions of Two Random Variables – Solved Example 3
    • Two Functions of Two Random Variables – Solved Example 4
    • Two Functions of Two Random Variables – Solved Example 5
    • Two Functions of Two Random Variables – Solved Example 6
  • Measures of Central Tendency
    • Measures of Central Tendency – Concept
    • Measures of Central Tendency – Solved Example 1
  • Mathematical Expectations and Moments
    • Mathematical Expectations and Moments – Concept
    • Relation Between Central and Non-Central Moments – Concept
  • Measures of Dispersion
    • Measures of Dispersion (Quartile Deviation) – Concept
    • Measures of Dispersion (Quartile Deviation) – Solved Example 1
    • Measures of Dispersion (Mean Deviation) – Concept
    • Measures of Dispersion (Standard Deviation and Variance) – Concept
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 1 & 2
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 3
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 4
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 5
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 6
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 7
    • Measures of Dispersion (Standard Deviation and Variance) – Solved Example 8 & 9
  • Skewness and Kurtosis
    • Skewness – Concept
    • Skewness – Solved Example 1
    • Kurtosis – Concept
    • Kurtosis – Solved Example 1
  • Statistical Averages – Solved Examples
    • Statistical Averages – Solved Example 1
    • Statistical Averages – Solved Example 2
    • Statistical Averages – Solved Example 3
  • Expected Values of a Two-Dimensional Random Variables
    • Expected Values of a Two-Dimensional RVs – Concept and Solved Example 1
    • Expected Values of a Two-Dimensional RVs – Properties
    • Expected Values of a Two-Dimensional RVs – Solved Example 1
    • Conditional Expected Values of a Two-Dimensional RVs – Concept
    • Conditional Expected Values of a Two-Dimensional RVs – Properties
    • Conditional Expected Values of a Two-Dimensional RVs – Solved Example 1
    • Conditional Expected Values of a Two-Dimensional RVs – Solved Example 2
  • Linear Correlation
    • Linear Correlation – Introduction
  • Correlation Coefficient
    • Correlation Coefficient – Concept
    • Correlation Coefficient – Solved Example 1
    • Correlation Coefficient – Solved Example 2
  • Properties of Correlation Coefficient
    • Properties 1 and 2 of Correlation Coefficient – Concept
    • Properties 1 and 2 of Correlation Coefficient – Solved Example 1
    • Properties 1 and 2 of Correlation Coefficient – Solved Example 2
    • Properties 1 and 2 of Correlation Coefficient – Solved Example 3
    • Properties 3 and 4 of Correlation Coefficient – Concept
    • Properties 3 and 4 of Correlation Coefficient – Solved Example 1
    • Properties 3 and 4 of Correlation Coefficient – Solved Example 2
    • Properties 3 and 4 of Correlation Coefficient – Solved Example 3
    • Properties 3 and 4 of Correlation Coefficient – Solved Example 4
  • Rank Correlation Coefficient
    • Rank Correlation Coefficient – Concept
    • Rank Correlation Coefficient – Solved Example 1
    • Rank Correlation Coefficient – Solved Example 2
  • Linear Regression
    • Linear Regression – Introduction
  • Equations of the Lines of Regression
    • Equation of the Regression Line of Y on X – Concept
    • Equation of the Regression Line of X on Y – Concept
    • Important Notes on Equations of the Regression Lines – Concept
    • Equations of the Lines of Regression – Solved Example 1
    • Equations of the Lines of Regression – Solved Example 2
    • Equations of the Lines of Regression – Solved Example 3
    • Equations of the Lines of Regression – Solved Example 4
    • Equations of the Lines of Regression – Solved Example 5
    • Equations of the Lines of Regression – Solved Example 6
    • Equations of the Lines of Regression – Solved Example 7
  • Standard Error of Estimate of Y on X and of X on Y
    • Standard Error of Estimate of Y on X and of X on Y – Concept
    • Standard Error of Estimate of Y on X and of X on Y – Solved Example 1
  • Characteristic Function and Moment Generating Function
    • Characteristic Function – Definition and Properties – Concept
    • Characteristic Function – Solved Example 1
    • Characteristic Function – Solved Example 2
    • Characteristic Function – Solved Example 3
    • Characteristic Function – Solved Example 4
    • Characteristic Function – Solved Example 5
    • Characteristic Function – Solved Example 6
    • Characteristic Function – Solved Example 7 and 8
    • Characteristic Function – Solved Example 9
    • Moment Generating Function – Definition and Properties – Concept
    • Moment Generating Function – Solved Example 1
    • Moment Generating Function – Solved Example 2
    • Moment Generating Function – Solved Example 3
    • Moment Generating Function – Solved Example 4
    • Moment Generating Function – Solved Example 5 and 6
    • Moment Generating Function – Solved Example 7
    • Moment Generating Function – Solved Example 8
    • Moment Generating Function – Solved Example 9
    • Cumulant Generating Function – Concept and Solved Example 1
    • Cumulant Generating Function – Solved Example 2
    • Joint Characteristic Function – Concept and Solved Example 1
  • Bounds on Probabilities
    • Tchebycheff Inequality – Concept
    • Tchebycheff Inequality – Solved Example 1 and 2
    • Tchebycheff Inequality – Solved Example 3
    • Tchebycheff Inequality – Solved Example 4
    • Tchebycheff Inequality – Solved Example 5
    • Tchebycheff Inequality – Solved Example 6
    • Tchebycheff Inequality – Solved Example 7
    • Tchebycheff Inequality – Solved Example 8
    • Tchebycheff Inequality – Solved Example 9 and 10
    • Bienayme’s Inequality – Concept
    • Schwartz Inequality – Concept

 

Master Complete Statistics For Computer Science - IMaster Complete Statistics For Computer Science - I

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