# Probability Distributions: Getting Started with Probability Distributions

Python 3.7    |    Beginner
• 13 Videos | 1h 30m 40s
• Includes Assessment
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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

• 1.
Course Overview
• 2.
Getting Familiar with Statistics
• 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

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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