Fundamentals of Statistics and Visualization in Python

Fundamentals of Statistics and Visualization in Python

Learn to display your data using Python's visualization tools

Bestseller
Created By: Karen Yang
15.87 9.52

About This Course

Statistics and visualization in Python can be applied to a wide variety of areas; having these skills is crucial for data scientists. In this course, we explore several core statistical concepts to utilize data; construct confidence intervals in Python and assess the results; discover correlations; and update your beliefs using Bayesian Inference.

In this tutorial, you will discover how to use the Statsmodels, Matplotlib, pandas, and Seaborn Python libraries for statistical data visualization. Follow along with author—Dr. Karen Yang, a seasoned data scientist and data engineer—to explore, learn, and strengthen your skills in fundamental statistics and visualization. This course utilizes the Jupyter Notebook environment to execute tasks.

By the end of this learning journey, you'll have developed a solid understanding of fundamental statistics and visualization concepts and will be confident enough to apply them to your data analysis projects.

Please note that prior knowledge of Python programming and some familiarity with pandas and NumPy are needed in order to get the best out of this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Fundamentals-of-Statistics-and-Visualization-in-Python

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Basic concepts in statistics and data visualization
  • Use Python data visualization tools to perform data visualization
  • Apply probability to statistics with the use of Bayesian Inference, a powerful alternative to classical statistics
  • Calculate and build confidence intervals in Python
  • Run basic regressions focused on linear and multilinear data
  • Run hypothesis tests and perform Bayesian inference for effective analysis and visualization
  • Apply probability to statistics by updating beliefs

Are There Any Course Requirements Or Prerequisites?

Prior knowledge of Python programming and some familiarity with pandas and NumPy are needed in order to get the best out of this course.
 

Who Are Your Target Students?

This course is for Python programmers who want to master essential statistics and visualization concepts using the Python programming language and are keen to learn to perform visualization effectively in conjunction with multiple visualization tools.

Course Content

  • 22 lectures
  • 03:14:59
  • The Course Overview
    00:05:23
  • Installing Anaconda for Python
    00:06:53
  • Understanding the Key Aspects of Statistics and Visualization
    00:09:23
  • Getting Data and Performing Operations
    00:09:43
  • Working with Summary Statistics
    00:08:27
  • Grouping Data
    00:05:42
  • Performing Normal Distribution
    00:10:56
  • Confidence Intervals
    00:09:57
  • Correlational Relationship
    00:07:44
  • Linear Regression: The Big Picture
    00:09:42
  • Multivariate Linear Regression
    00:10:26
  • Logistic Regression: The Big Picture
    00:10:41
  • Multivariate Logistic Regression
    00:10:31
  • Handling Missing Data
    00:08:59
  • Visualizing Summary Statistics with Pandas
    00:10:47
  • How to Work with Matplotlib
    00:08:15
  • Using Seaborn for Data Visualization
    00:09:35
  • Handling Outliers
    00:07:51
  • Understanding Bayes‚Äô Theorem
    00:09:00
  • How to Perform Statistical Hypothesis Testing
    00:07:30
  • Bayesian Statistics with Linear Regression
    00:07:19
  • Bayesian Statistics with Logistic Regression
    00:10:15
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Packt Publication

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  • 16 Students
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