Fundamentals of Statistics and Visualization in Python

Fundamentals of Statistics and Visualization in Python

Learn to display your data using Python's visualization tools

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

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
  • Installing Anaconda for Python
  • Understanding the Key Aspects of Statistics and Visualization
  • Getting Data and Performing Operations
  • Working with Summary Statistics
  • Grouping Data
  • Performing Normal Distribution
  • Confidence Intervals
  • Correlational Relationship
  • Linear Regression: The Big Picture
  • Multivariate Linear Regression
  • Logistic Regression: The Big Picture
  • Multivariate Logistic Regression
  • Handling Missing Data
  • Visualizing Summary Statistics with Pandas
  • How to Work with Matplotlib
  • Using Seaborn for Data Visualization
  • Handling Outliers
  • Understanding Bayes‚Äô Theorem
  • How to Perform Statistical Hypothesis Testing
  • Bayesian Statistics with Linear Regression
  • Bayesian Statistics with Logistic Regression

Packt Publication

  • 4.48 (16)
  • 13 Reviews
  • 16 Students
  • 935 Courses