Course Content

Probability Theory

1. Learn Basic Rules

Random Variable Bernoulli Trial 1/2 Bernoulli Trial 2/2 Binomial Probability 1/2 Binomial probability 2/2

2. Probabilities of Several Events

Add Probabilities Calculate Connected Probabilities Challenge Multiply probabilities Dependent probabilities Law of Total Probability Bayes Theorem Challenge

3. Conducting Fascinating Experiments

The First Experiment The Second Experiment The Third Experiment Combine Your Knowledge

4. Discrete Distributions

Discrete Uniform Distribution Calculate Fascinating Probability Bernoulli Distribution Binomial Distribution Challenge Poisson Distribution Challenge

5. Normal Distribution

Normal Distribution Challenge 1 Сhallenge 2

The First Experiment

To be a data analyst, it is crucial to understand that we are going to conduct a lot of experiments. Here, we are going to get acquainted with several gripping functions!

Real-life example:

Imagine that we are working for a real estate agency, and we need to know how many positive answers we will get from all interviewees.

In this course, we will learn such interesting functions programming-wise.

Look at the example and everything will be clear:

General formula:

In this experiment, we will work with the binom.pmf(k, n, p) function. This function helps calculate the probability of receiving exactly k successes among n trials with the probability of success for each experiment p.


              1234
            
from scipy.stats import binom
# Calculate the probability
experiment = binom.pmf(k = 1000, n = 20000, p=0.20)
print(experiment)

Explanation:

As usual, we are importing objects from scipy.stats with this code from scipy.stats import binom.
binom.pmf(k = 1000, n = 20000, p=0.20) the probability of getting 1000 successes amoung 20 000 trials with the probability of success 20%.

Interesting fact:

The result of our code is zero, but we worked with an enormous sample; in the task, we will receive a more understandable result.

Task

Imagine that our task is to do some calculations for a charity organization just for practice.

Your task here is to calculate the probability that exactly 5 kittens will find a home; there are 12 kittens in the shelter. In this city, kittens are taken from a shelter with a probability of 75%. Import relevant library to do it.

Import binom object from scipy.stats.
Calculate the probability that exactly 5 kittens out of 12 will find a home with the probability of success 75%.

The output here is going to be less hopeful.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 1

The First Experiment

To be a data analyst, it is crucial to understand that we are going to conduct a lot of experiments. Here, we are going to get acquainted with several gripping functions!

Real-life example:

Imagine that we are working for a real estate agency, and we need to know how many positive answers we will get from all interviewees.

In this course, we will learn such interesting functions programming-wise.

Look at the example and everything will be clear:

General formula:


              1234
            
from scipy.stats import binom
# Calculate the probability
experiment = binom.pmf(k = 1000, n = 20000, p=0.20)
print(experiment)

Explanation:

As usual, we are importing objects from scipy.stats with this code from scipy.stats import binom.
binom.pmf(k = 1000, n = 20000, p=0.20) the probability of getting 1000 successes amoung 20 000 trials with the probability of success 20%.

Interesting fact:

The result of our code is zero, but we worked with an enormous sample; in the task, we will receive a more understandable result.

Task

Imagine that our task is to do some calculations for a charity organization just for practice.

Import binom object from scipy.stats.
Calculate the probability that exactly 5 kittens out of 12 will find a home with the probability of success 75%.

The output here is going to be less hopeful.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 1

The First Experiment

To be a data analyst, it is crucial to understand that we are going to conduct a lot of experiments. Here, we are going to get acquainted with several gripping functions!

Real-life example:

Imagine that we are working for a real estate agency, and we need to know how many positive answers we will get from all interviewees.

In this course, we will learn such interesting functions programming-wise.

Look at the example and everything will be clear:

General formula:


              1234
            
from scipy.stats import binom
# Calculate the probability
experiment = binom.pmf(k = 1000, n = 20000, p=0.20)
print(experiment)

Explanation:

As usual, we are importing objects from scipy.stats with this code from scipy.stats import binom.
binom.pmf(k = 1000, n = 20000, p=0.20) the probability of getting 1000 successes amoung 20 000 trials with the probability of success 20%.

Interesting fact:

The result of our code is zero, but we worked with an enormous sample; in the task, we will receive a more understandable result.

Task

Imagine that our task is to do some calculations for a charity organization just for practice.

Import binom object from scipy.stats.
Calculate the probability that exactly 5 kittens out of 12 will find a home with the probability of success 75%.

The output here is going to be less hopeful.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

To be a data analyst, it is crucial to understand that we are going to conduct a lot of experiments. Here, we are going to get acquainted with several gripping functions!

Real-life example:

Imagine that we are working for a real estate agency, and we need to know how many positive answers we will get from all interviewees.

In this course, we will learn such interesting functions programming-wise.

Look at the example and everything will be clear:

General formula:


              1234
            
from scipy.stats import binom
# Calculate the probability
experiment = binom.pmf(k = 1000, n = 20000, p=0.20)
print(experiment)

Explanation:

As usual, we are importing objects from scipy.stats with this code from scipy.stats import binom.
binom.pmf(k = 1000, n = 20000, p=0.20) the probability of getting 1000 successes amoung 20 000 trials with the probability of success 20%.

Interesting fact:

The result of our code is zero, but we worked with an enormous sample; in the task, we will receive a more understandable result.

Task

Imagine that our task is to do some calculations for a charity organization just for practice.

Import binom object from scipy.stats.
Calculate the probability that exactly 5 kittens out of 12 will find a home with the probability of success 75%.

The output here is going to be less hopeful.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below