Master Complete Statistics For Computer Science – I


Summary, Description

Probability and statistics subjects play a significant role in today’s engineering education, as predictive approaches are very useful in evaluating the evidence and understanding the findings.

Statistical approaches become very useful as an inexperienced engineering student takes up a project or research job.

Therefore, in addition to studying for tests such as for standard courses or entry-level exams for postgraduate courses, the use of a well-structured course on probability and statistics in the curriculum can help students understand the topic in depth.

The content of this course is well structured to appeal to the needs of engineering students. In this course, as the contents advance from basics to higher statistical stages, all sections are well arranged and presented in sequence.

As a consequence, this course is, in effect, student oriented, so before solving problems, I have tried to illustrate all the topics with relevant examples.
This 150+ lecture course contains video descriptions of everything from random variables, distribution of probability, statistical averages, correlation, regression, characteristic function, moment generating function and probability limits, and more than 90+ samples are included (with detailed solutions)

  • 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

For whom this course is intended:

  • Students of current Probability and Statistics
  • Machine learning students, artificial intelligence, data science, computer science, electrical engineering, as the prerequisite course for machine learning, data science, computer science and electrical engineering is statistics.
  • Anyone after being away from school for a while who wants to study statistics for fun.

Course Link