This is data science different topic explanation conversation series.
If you are not following below mentioned are the links for each part.

Part-1:

Data Science different explanation – An introduction
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There are a lot of engineers who have never been involved in the field of statistics or Data Science.
But in order to build data pipelines or rewrite produced code by Data Scientists to an adequate, easily maintained code many nuances and misunderstandings arise on the engineering side. For those Data/ML engineers and novice Data Scientists, I’ve made this series of posts.
I will try to explain some basic approaches in plain eng…
Part-2:

If you missed the previous post, here’s the below mentioned link.
Data Science different topic explanation – Part-2 – Events. Probabilities of events
One of the basic concepts in statistics in an event. Events are simply results of experiments. Events can be certain impossible, or random.
A certain event is an event that as a result of an experiment (the execution of certain actions with a certain set of conditions) will occur in 100% cases. For instance, tossed coin will certainly fall (on…
Part-3:

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Data Science different topic explanation – Part-3 – Event Types
Independent and Dependent events
Two random events A and B are called independent if the occurrence of one of them does not change the probability of the occurrence of the other. Otherwise, events A and B are called dependent.
Counterintuitively, knowing that the coin l…
Part-4:

This is data science different topic explanation conversation series.
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Data Science different topic explanation – Part-4 – Disjoint and overlapping events
Disjoint events cannot happen at the same time. A synonym for this term is “mutually exclusive”.
For instance, the outcome of a single coin toss cannot be head and a tail, it can be either head or tails.
The not disjoint event can happend a…
Part-5:

This is data science different topic explanation conversation series.
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Data Science different topic’s explanation – Part-5 – Types of Probabilities
Joint Probability
Joint probability is a type of probability where more than one event can occur simultaneously. The joint probability is the probability that event A will occur at the same time as event B.
For instance, from a deck o…
Part-6:

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Data Science different topic’s explanation – Part-6 – Marginal Probability and Conditional Probability
Marginal Probability
Marginal Probability – a probability of any single event occurring unconditioned on any other events.
Whenever someone asks you whether the weather is going to be rainy or sunny t…
Part-7:

Continuing the conversation series for next topic here.
Cumulative Distribution Function (CDF)
The cumulative distribution function provides an integral picture of the probability distribution. As the name cumulative suggests, it is simply the probability that a variable will take a value less than or equal to particular value. In the example above given x=3, the CDF tells us the sum probability of all random variables form 1 to 3.
Part-8:

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Part-7:
Continuous distributions
We investigate probabilistic distributions of continuous random variables, i.e. random variables whose support contains an infinite range of possible outcomes.
Uniform Distribution
A uniform distribution is a type of distribution of probabilities where all …
Part-9:

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Data Science different topic’s explanation – Part-9 – Normal Distribution
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
def normal() -> None:
fig, ax = plt.subplots(1, 1)
# calculate a few first moments
mean, var, skew, kurt = n…
Data Science different topic’s explanation – Part-10 – Discrete distributions
Bernoulli distribution (binomial distribution)
```
from scipy.stats import bernoulli
import seaborn as sb
def bernoulli_dist(): -> None:
data_bern = bernoulli.rvs(size=1000,p=0.6)
ax = sb.distplot(
data_bern,
kde=True,
color='b',
hist_kws={'alpha':1},
kde_kws={'color': 'r', 'lw': 3, 'label': 'KDE'})
ax.set(xlabel='Bernouli', ylabel='Frequency')
bernoulli_dist()
```

Not all phenomena are measured on a quantitative scale of type 1,2,3 … 100500… Not always a phenomenon can take on an infinite or a large number of different states. For instance, a person’s sex can be either a man or a woman. The shooter either hits the target or missed. You can vote either “for” or “against”, etc. Other words reflect the state of an alternative feature (the event did not come).