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Master Complete Statistics For Computer Science – I

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In right now’s engineering curriculum, subjects on chance and statistics play a significant position, because the statistical strategies are very useful in analyzing the info and decoding the outcomes.

When an aspiring engineering scholar takes up a mission or analysis work, statistical strategies develop into very helpful.

Therefore, the usage of a well-structured course on chance and statistics within the curriculum will assist college students perceive the idea in depth, along with getting ready for examinations equivalent to for normal programs or entry-level exams for postgraduate programs.

With the intention to cater the wants of the engineering college students, content material of this course, are properly designed. On this course, all of the sections are properly organized and offered in an order because the contents progress from fundamentals to greater stage of statistics.

Because of this, this course is, the truth is, scholar pleasant, as I have tried to elucidate all of the ideas with appropriate examples earlier than fixing issues.

This 150+ lecture course contains video explanations of every thing from Random Variables, Chance Distribution, Statistical Averages, Correlation, Regression, Attribute Perform, Second Producing Perform and Bounds on Chance, and it contains greater than 90+ examples (with detailed options) that can assist you check your understanding alongside the way in which. “Master Complete Statistics For Computer Science – I” is organized into the next sections:

  • Introduction
  • Discrete Random Variables
  • Steady Random Variables
  • Cumulative Distribution Perform
  • Particular Distribution
  • Two – Dimensional Random Variables
  • Random Vectors
  • Perform of One Random Variable
  • One Perform of Two Random Variables
  • Two Features of Two Random Variables
  • Measures of Central Tendency
  • Mathematical Expectations and Moments
  • Measures of Dispersion
  • Skewness and Kurtosis
  • Statistical Averages – Solved Examples
  • Anticipated Values of a Two-Dimensional Random Variables
  • Linear Correlation
  • Correlation Coefficient
  • Properties of Correlation Coefficient
  • Rank Correlation Coefficient
  • Linear Regression
  • Equations of the Traces of Regression
  • Commonplace Error of Estimate of Y on X and of X on Y
  • Attribute Perform and Second Producing Perform
  • Bounds on Chances

Who this course is for:

  • Present Chance and Statistics college students
  • College students of Machine Studying, Synthetic Intelligence, Knowledge Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Studying, Knowledge Science, Computer Science and Electrical Engineering
  • Anybody who needs to review Statistics for enjoyable after being away from faculty for some time.

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