# Probability Distributions: Getting Started with Probability Distributions

Python 3.7    |    Beginner
• 13 videos | 1h 25m 10s
• Includes Assessment
Likes 3
Probability distributions are statistical models that show the possible outcomes and statistical likelihood of any given event and are often useful for making business decisions. Get familiar with the theoretical concepts around statistics and probability distributions through this course and delve into applying statistical concepts to analyze your data using Python. Start by exploring statistical concepts and terminology that will help you understand the data you want to use for estimations on a population. You'll then examine probability distributions - the different forms of distributions, the types of events they model, and the various functions available to analyze distributions. Finally, you'll learn how to use Python to calculate and visualize confidence intervals, as well as the skewness and kurtosis of a distribution. After completing this course, you'll have a foundational understanding of statistical analysis and probability distributions.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course define descriptive and inferential statistics recognize the difference between samples and populations describe different types of probability distributions and where they occur identify what different statistical terms represent install Python libraries needed for data analysis and generate and work with probability distributions analyze and visualize data using box plots
• recognize how data is distributed using histograms and violin plots calculate and visualize confidence intervals using Python estimate a population's mean with confidence intervals describe and compare skewness and kurtosis calculate skewness and kurtosis on real data summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Populations and Samples
• 4.  Types of Probability Distributions
• 5.  Statistical Terminology
• 6.  Installing Python Libraries to Analyze Data
• 7.  Visualizing Data with Box Plots
• 8.  Exploring Distributions with Charts
• 9.  Generating Confidence Intervals
• 10.  Measuring Parameters with Confidence Intervals
• 11.  Understanding Skewness and Kurtosis
• 12.  Computing Skewness and Kurtosis
• 13.  Course Summary

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