Master Complete Statistics For Computer Science – I
Salepage : Master Complete Statistics For Computer Science – I
Archive : Master Complete Statistics For Computer Science – I Digital Download
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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:
Random Discrete Variables
Variables with Continuous Randomness
Function of Cumulative Distribution
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
Kurtosis and Skewness
Solved Examples of Statistical Averages
Two-Dimensional Random Variables Expected Values
Coefficient of Correlation
Correlation Coefficient Properties
Coefficient of Rank Correlation
Lines of Regression Equations
Estimation standard error of Y on X and X on Y
Moment Generating Function and Characteristic Function
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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