Leverage the power of Node.js 12 to build RESTful apps

Learn

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

About

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
Features

Discover and sharpen your skills in core statistics and visualization
Create vibrant data visualizations using Seaborn and Matplotlib
Apply what you've learned from this course to your data analysis projects
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