** Python Certification Training: https://www.edureka.co/python ** This Edureka PPT on Python Tutorial covers all the basic knowledge of statistics and probability for Python. Why Python for Statistics? What is Probability? Data and Distribution Revisiting the Normal Poker Prediction Use-Case Check out our Python Tutorial Playlist: https://goo.gl/WsBpKe Follow us to never miss an update in the future. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

1. Python Certification Training https://www.edureka.co/python
Agenda

2. Python Certification Training https://www.edureka.co/python
Agenda
Introduction 01
Introduction to
Statistics And
Probability Getting Started 02
Concepts 03
Use Case 04
Getting Started With Python
for Probability
A practical Python use-case
to understand Python faster!
Overview of the simple
concepts that’s involved

3. Python Certification Training https://www.edureka.co/python
Why Python For Statistics?

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Why Python For Statistics?
R is a language dedicated for statistics!
Then why Python?
Building complex analysis pipelines that mix statistics
with Image Analysis, Text Mining etc..
Here, the richness of Python is an invaluable asset!

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What is Probability?

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What is Probability?
What is the chance of an event happening?
How do you answer this?
We need to consider all the other events that can occur
before coming to a conclusion!

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The Coin Toss
What are the outcomes for a coin toss?
Flipping a heads Flipping a tails
Any other outcome? NO!
We call this the
Sample Space!

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The Coin Toss
What are the outcomes for a coin toss?
A 100 Heads and 10 Tails, is this fair?
Yes, the outcome here is to gather data, use
statistics to make predictions and compare!

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The Coin Toss – Data Generation

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Too early for code?

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The Coin Toss – Code
import random
def coin_trial():
heads = 0 for i in range(100):
if random.random() <= 0.5:
heads +=1
return heads
def simulate(n):
trials = []
for i in range(n):
trials.append(coin_trial()) return(sum(trials)/n)
simulate(10)
>> 5.4
simulate(100)
>>> 4.83
simulate(1000)
>>> 5.055
simulate(1000000)
>>> 4.999781

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The Coin Toss – The Theory
Given enough data, statistics enables us to calculate
probabilities using real-world observations

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The Coin Toss – Python
What are the chances of someone
developing a disease over time?
What is probability that a critical
car component will fail when
you are driving?
Python making
our lives simpler
with this!

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Data And Distribution

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Data And Distribution
Let’s tackle “Which wine is better than average”
You need to know the nature of the data!
Normal Distribution
Normal distribution refers to a particularly important
phenomenon in the realm of probability and statistics.

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Data And Distribution
The high point in a normal distribution represents the event
with the highest probability of occurring!

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Revisiting The Normal

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Revisiting The Normal
Two major factors
Central Limit Theorem Three Sigma Rule
Central Limit Theorem dictates that the distribution of
the estimates will look like a normal distribution.
The Three Sigma rule dictates that given a normal
distribution, 68% of your observations will fall between one
standard deviation of the mean. 95% will fall within two, and
99.7% will fall within three.
Learning Python

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Z-Score
Learning Python

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Use-Case: Poker Probability

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Use-Case: Poker Prediction
Can we predict the outcome of probability of
occurrence of a poker hand?

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Use-Case: Poker Prediction
Let’s look at the basics
52 cards in a standard deck!
4 of each
shape
For an Ace - P(A) = 4/52

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Use-Case: Poker Prediction
Poker Without
Python
Poker With
Python
Texas Hold’em
Pre-Flop: Each player is dealt two cards, known as "hole cards"
Flop: Three community cards are dealt
Turn: One community card is dealt
River: Final community card is dealt

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Use-Case: Poker Prediction
Dependent Events: Flush Draw
Your Hand
Community Cards

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Use-Case: Poker Prediction
Dependent Events: Open-Ended
Straight Draw
Your Hand
Community Cards

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Use-Case: Poker Prediction
Involve Opponents now!
Your Hand
Community Cards
Opponent’s Hand
Total Pot = $60 Opponents Bet = $20

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Conclusion

28. Python Certification Training https://www.edureka.co/python
Conclusion