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.

About the Author

Karen Yang has been a data engineer, an author, and a passionate computer science self-learner for 7 years. She has 6 years' experience in Python programming and big data processing. Her recent interests include cloud computing.

She holds a PhD in Political Science from Ohio State University and loves working with data to gather meaningful information by performing analysis and research. This interest led her to publish data analysis research papers on Inferential Data Analysis on Tooth Growth and Predicting Activity for Samsung SensorData. She is also a published author of the 'Apache Spark in 7 Days' course.