Probability Distributions: Getting Started with Probability Distributions

Python 3.7    |    Beginner
  • 13 videos | 1h 25m 10s
  • Includes Assessment
  • Earns a Badge
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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.


  • 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


  • 1m 47s
  • 6m 12s
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    3.  Populations and Samples
    5m 37s
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    4.  Types of Probability Distributions
    8m 48s
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    5.  Statistical Terminology
    7m 56s
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    6.  Installing Python Libraries to Analyze Data
    4m 52s
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    7.  Visualizing Data with Box Plots
    7m 42s
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    8.  Exploring Distributions with Charts
    8m 42s
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    9.  Generating Confidence Intervals
    9m 38s
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    10.  Measuring Parameters with Confidence Intervals
    6m 7s
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    11.  Understanding Skewness and Kurtosis
    7m 46s
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    12.  Computing Skewness and Kurtosis
    8m 15s
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    13.  Course Summary
    1m 49s


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