Master Complete Statistics For Computer Science – I

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Probability and statistics themes are heavily emphasized in today’s engineering curriculum, as statistical approaches are extremely useful in evaluating data and interpreting conclusions.

When a prospective engineering student embarks on a project or does research, statistical approaches come in helpful.

As a result, including a well-structured probability and statistics course in the curriculum will assist students comprehend the idea in depth, in addition to preparing for tests such as those for normal courses or entry-level exams for postgraduate courses.

This course’s material is well intended to meet the demands of engineering students. All of the components in this course are properly arranged and given in an order that progresses from the basics to a higher level of statistics.

Since a consequence, this course is actually student-friendly, as I attempted to explain all topics with appropriate examples before solving problems.

This 150-plus lecture course includes video explanations of topics such as Random Variables, Probability Distribution, Statistical Averages, Correlation, Regression, Characteristic Function, Moment Generating Function, and Bounds on Probability, as well as more than 90 examples (with detailed solutions) to help you test your understanding along the way. The parts of “Master Complete Statistics For Computer Science – I” are as follows:

Introduction

Random Discrete Variables

Variables with Continuous Randomness

Function of Cumulative Distribution

Distribution Exceptional

Random Variables in Two Dimensions

Vectors at Random

One Random Variable Function

One Function Consisting of Two Random Variables

Two Random Variables’ Functions

Central Tendency Measures

Moments and Mathematical Expectations

Dispersion Measures

Kurtosis and Skewness

Solved Examples of Statistical Averages

Two-Dimensional Random Variables Expected Values

Correlation Linear

Coefficient of Correlation

Correlation Coefficient Properties

Coefficient of Rank Correlation

Regression Linear

Lines of Regression Equations

Estimation standard error of Y on X and X on Y

Moment Generating Function and Characteristic Function

Probabilistic Bounds

Who should take this course:

Students of Probability and Statistics nowadays

Statistics is a required subject for Machine Learning, Artificial Intelligence, Data Science, Computer Science, and Electrical Engineering students.

Anyone who wishes to study statistics for leisure after taking a break from school.

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