Enterprise Database Systems
Probability Distributions
Probability Distributions: Getting Started with Probability Distributions
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Probability Distributions: Understanding Normal Distributions
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Probability Distributions: Uniform, Binomial, & Poisson Distributions
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Probability Distributions: Getting Started with Probability Distributions
Lesson Objectives
Probability Distributions: Getting Started with 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
Overview/Description
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.
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.
Target
Prerequisites: none
Probability Distributions: Understanding Normal Distributions
Lesson Objectives
Probability Distributions: Understanding Normal Distributions
- discover the key concepts covered in this course
- describe normal distributions and their characteristics
- use the cumulative distribution function (CDF) of a normal distribution and recognize how the mean and standard deviation (SD) influence it
- visualize the cumulative distribution function (CDF) for different standard deviations
- recall the symmetrical features of normal distributions
- explain the law of large numbers programmatically
- recall the central limit theorem and recognize its applications
- summarize the key concepts covered in this course
Overview/Description
This course dives deep into normal distributions, also known as Gaussian distributions, while also introducing you to the law of large numbers and the Central Limit Theorem. You will begin by using Python's SciPy library to generate a normal distribution and examine the use of several available functions that allow you to make estimations on normally distributed data. This course will also help you understand and visualize the law of large numbers and explore the Central Limit theorem by generating multiple samples and analyzing them. After you are done with this course, you'll have the skills and knowledge to analyze data and build your own models.
This course dives deep into normal distributions, also known as Gaussian distributions, while also introducing you to the law of large numbers and the Central Limit Theorem. You will begin by using Python's SciPy library to generate a normal distribution and examine the use of several available functions that allow you to make estimations on normally distributed data. This course will also help you understand and visualize the law of large numbers and explore the Central Limit theorem by generating multiple samples and analyzing them. After you are done with this course, you'll have the skills and knowledge to analyze data and build your own models.
Target
Prerequisites: none
Probability Distributions: Uniform, Binomial, & Poisson Distributions
Lesson Objectives
Probability Distributions: Uniform, Binomial, & Poisson Distributions
- 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
Overview/Description
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.
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.
Target
Prerequisites: none
