![Python Certification Training https://www.edureka.co/python 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](https://image.slidesharecdn.com/statisticsandprobwithpython-190120204810/75/Statistics-Using-Python-Statistics-Python-Tutorial-Python-Certification-Training-Edureka-11-2048.jpg)
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Statistics Using Python | Statistics Python Tutorial | Python Certification Training | Edureka
The document outlines a Python certification training agenda, emphasizing the use of Python in statistics and probability. It covers foundational concepts, including probability theory, normal distribution, and practical examples such as coin toss simulations and poker hand predictions. The training is designed to illustrate how Python can simplify statistical analysis and enhance data interpretation.
In this document
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Introduction to Python certification training for statistics, covering agenda and practical use cases.
Discusses Python's advantages over R for statistical analysis, including versatility in data analysis.
Defines probability, focusing on the chance of events occurring and importance of sample space.
Explores the outcomes of a coin toss, simulating trials to generate data, and understanding statistical probability.
Discusses types of data distribution, particularly normal distribution and its significance in statistics.
Introduces Central Limit Theorem, Three Sigma Rule, and Z-Score in relation to normal distribution.
Utilizes poker to illustrate probability, prediction outcomes, card distribution, and analyzing events.
Summarizes the training discussions and the overall importance of Python in statistics and probability.
Statistics Using Python | Statistics Python Tutorial | Python Certification Training | Edureka
- 1. Python Certification Traininghttps://www.edureka.co/python Agenda
- 2. Python Certification Traininghttps://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 Traininghttps://www.edureka.co/python Why Python For Statistics?
- 4. Python Certification Traininghttps://www.edureka.co/python 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!
- 5. Python Certification Traininghttps://www.edureka.co/python What is Probability?
- 6. Python Certification Traininghttps://www.edureka.co/python 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!
- 7. Python Certification Traininghttps://www.edureka.co/python 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!
- 8. Python Certification Traininghttps://www.edureka.co/python 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!
- 9. Python Certification Traininghttps://www.edureka.co/python The Coin Toss – Data Generation
- 10. Python Certification Traininghttps://www.edureka.co/python Too early for code?
- 11. Python Certification Traininghttps://www.edureka.co/python 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
- 12. Python Certification Traininghttps://www.edureka.co/python The Coin Toss – The Theory Given enough data, statistics enables us to calculate probabilities using real-world observations
- 13. Python Certification Traininghttps://www.edureka.co/python 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!
- 14. Python Certification Traininghttps://www.edureka.co/python Data And Distribution
- 15. Python Certification Traininghttps://www.edureka.co/python 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.
- 16. Python Certification Traininghttps://www.edureka.co/python Data And Distribution The high point in a normal distribution represents the event with the highest probability of occurring!
- 17. Python Certification Traininghttps://www.edureka.co/python Revisiting The Normal
- 18. Python Certification Traininghttps://www.edureka.co/python 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
- 19. Python Certification Traininghttps://www.edureka.co/python Z-Score Learning Python
- 20. Python Certification Traininghttps://www.edureka.co/python Use-Case: Poker Probability
- 21. Python Certification Traininghttps://www.edureka.co/python Use-Case: Poker Prediction Can we predict the outcome of probability of occurrence of a poker hand?
- 22. Python Certification Traininghttps://www.edureka.co/python 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
- 23. Python Certification Traininghttps://www.edureka.co/python 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
- 24. Python Certification Traininghttps://www.edureka.co/python Use-Case: Poker Prediction Dependent Events: Flush Draw Your Hand Community Cards
- 25. Python Certification Traininghttps://www.edureka.co/python Use-Case: Poker Prediction Dependent Events: Open-Ended Straight Draw Your Hand Community Cards
- 26. Python Certification Traininghttps://www.edureka.co/python Use-Case: Poker Prediction Involve Opponents now! Your Hand Community Cards Opponent’s Hand Total Pot = $60 Opponents Bet = $20
- 27. Python Certification Traininghttps://www.edureka.co/python Conclusion
- 28. Python Certification Traininghttps://www.edureka.co/python Conclusion