Probability Distributions: Getting Started with Probability Distributions
Python 3.7
| Beginner
- 13 Videos | 1h 25m 10s
- Includes Assessment
- Earns a Badge
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
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discover the key concepts covered in this coursedefine descriptive and inferential statisticsrecognize the difference between samples and populationsdescribe different types of probability distributions and where they occuridentify what different statistical terms representinstall Python libraries needed for data analysis and generate and work with probability distributionsanalyze and visualize data using box plots
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recognize how data is distributed using histograms and violin plotscalculate and visualize confidence intervals using Pythonestimate a population's mean with confidence intervalsdescribe and compare skewness and kurtosiscalculate skewness and kurtosis on real datasummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 47sUP NEXT
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2.Getting Familiar with Statistics6m 12s
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3.Populations and Samples5m 37s
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4.Types of Probability Distributions8m 48s
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5.Statistical Terminology7m 56s
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6.Installing Python Libraries to Analyze Data4m 52s
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7.Visualizing Data with Box Plots7m 42s
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8.Exploring Distributions with Charts8m 42s
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9.Generating Confidence Intervals9m 38s
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10.Measuring Parameters with Confidence Intervals6m 7s
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11.Understanding Skewness and Kurtosis7m 46s
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12.Computing Skewness and Kurtosis8m 15s
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13.Course Summary1m 49s
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