Random Variables and probability distributions
Hemakumar Kasala
2:12:26
Description
Random Variables - Probability distributions - Binomial, Geometric, Normal and Standard normal distributions
What You'll Learn?
- Introduction to random variables ,
- Introduction to discrete & continuous probability distributions
- Binomial & Geometric distributions
- normal & standard normal distributions
Who is this for?
What You Need to Know?
More details
DescriptionThe first section focuses on
Probability distributions Starts with identifying the difference between a variable and a random variable. Explains discrete and continuous random variables and their characteristics. Move on to explain the need for the probability distributions. explains the basics, characteristics and definitions around the discrete probability distribution and continuous probability distributions. explains the graphical representations of probability distributions involving histograms and continuous functions Every aspect is illustrated with a simple case study to appreciate the details
The second section focusses on
two important discrete probability distributions namely Binomial distribution & Geometric distribution Explains - the conditions to be met for each of these experiments - derivation of mathematical functions that describe these distributions - mean and standard deviation (variance) for each of these distributions - applications of these distributions in certain real world using examples
The third section explains
What is a Normal distribution? What is a standard Normal distribution ( z value / z curve )? How are probabilities evaluated for a standard Normal distribution and normal distribution? How to judge if a sample data is Normally distributed? What is a normal probability plot? How to transform data into a normal distribution when the sample is not? How to arrive at probabilities for a discrete probability distribution using normal approximations?
Section four is only for reference and is OPTIONAL
Added here in order to help those who do not have the pre-requisite knowledge on essential concepts on probability
Explains the basic underlying concepts and definitions on probability involving, Chance experiments, Sample Space, Events, Likelihood
Two important theorems on probability namely
- Conditional probability and
- Bayes theorem
Various properties on Probability
Who this course is for:
- This course is one among the essential concepts on Probability and Statistics that an aspiring Data Scientist .
The first section focuses on
Probability distributions Starts with identifying the difference between a variable and a random variable. Explains discrete and continuous random variables and their characteristics. Move on to explain the need for the probability distributions. explains the basics, characteristics and definitions around the discrete probability distribution and continuous probability distributions. explains the graphical representations of probability distributions involving histograms and continuous functions Every aspect is illustrated with a simple case study to appreciate the details
The second section focusses on
two important discrete probability distributions namely Binomial distribution & Geometric distribution Explains - the conditions to be met for each of these experiments - derivation of mathematical functions that describe these distributions - mean and standard deviation (variance) for each of these distributions - applications of these distributions in certain real world using examples
The third section explains
What is a Normal distribution? What is a standard Normal distribution ( z value / z curve )? How are probabilities evaluated for a standard Normal distribution and normal distribution? How to judge if a sample data is Normally distributed? What is a normal probability plot? How to transform data into a normal distribution when the sample is not? How to arrive at probabilities for a discrete probability distribution using normal approximations?
Section four is only for reference and is OPTIONAL
Added here in order to help those who do not have the pre-requisite knowledge on essential concepts on probability
Explains the basic underlying concepts and definitions on probability involving, Chance experiments, Sample Space, Events, Likelihood
Two important theorems on probability namely
- Conditional probability and
- Bayes theorem
Various properties on Probability
Who this course is for:
- This course is one among the essential concepts on Probability and Statistics that an aspiring Data Scientist .
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Hemakumar Kasala
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 7
- duration 2:12:26
- Release Date 2024/04/23
