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`.

**Task:**

Calculate the probability of getting `1000` positive answers about the flat purchase among `20 000` people. We know that the probability of having a positive response about buying real estate is `20%`. 

 ``` 
Input:
from scipy.stats import binom
# Calculate the probability
experiment = binom.pmf(k = 1000, n = 20000, p=0.20)
print(experiment)
Output:
0.0
```


**Explanation:**

1. As usual, we are importing objects from `scipy.stats` with this code `from scipy.stats import binom`.
2. `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.

It's time to dive deeper into the probability theory. Here we will learn basic formulas. The main idea of the section is to help us to understand the term probability using real-life examples. 

Here, we will get acquainted with the rules in probability theory. This section will be more complicated than the previous one. It will be attractive because we are going to work with the real challenges. But it should be noted that we learn some formulas.

Doing experiments is always funny, but here we will combine exciting tasks with programming learning. In this section, we will work with the binom object to conduct experiments in Python.

Distribution is a key to probability learning, but don't be petrified by this term. This section provides you with real-life explanations and relevant formulas. 

The normal distribution is the most commonly used one. Here we will work with the distribution with the help of real-life challenges.