# Probability Distributions: Uniform, Binomial, & Poisson Distributions

Python 3.7    |    Intermediate
• 11 videos | 1h 28m 46s
• Includes Assessment
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Python libraries, such as NumPy and SciPy, are used for mathematical and numerical analysis. Through this course, learn how to generate uniform, binomial, and Poisson distributions using these libraries. Begin by exploring uniform distributions and delve into continuous and discrete distributions. You will then explore binomial distributions in-depth, including real-life situations where they can be applied. This course will also help you learn more about Poisson distributions and recognize their use cases. While examining these distributions, you will use functions, such as the probability density or probability mass functions and cumulative distributions functions, among others, to make estimations from your data. Upon completion of this course, you'll have the skills and knowledge to implement and visualize uniform, binomial, and Poisson distributions in Python.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course use SciPy to generate uniformly distributed samples analyze a uniform distribution by using cumulative distribution and probability density functions generate discrete uniform data using NumPy and SciPy and evaluate the distributions describe binomial distributions and generate one using SciPy simulate trials with binomial distributions in SciPy
• explore cumulative distribution, probability mass, and survival functions with binomial data describe the Poisson distribution and its applications invoke functions available in SciPy to work with Poisson distributions apply Poisson distributions to make estimates in real-life situations summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Exploring the CDF, PDF, and PPF Functions
• 4.  Generating and Sampling Uniform Data
• 5.  Generating Binomial Distributions
• 6.  Using Binomial Distributions
• 7.  Performing Computations on Binomial Distributions
• 8.  Using Poisson Distribution
• 9.  Exploring Functions for Poisson Distributions
• 10.  Applying Poisson Distributions
• 11.  Course Summary

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