About This Course
To make sense of data, data scientists rely on everything from statistical analysis to machine learning. This course focuses on data exploration and visualization, probability and statistical theory, and theory of linear statistical models. You’ll develop the skills to explore and display complex relationships in data, apply probabilistic and statistical methods, and understand the basis of core machine learning algorithms. Learn how to correctly apply statistical methods so you can move beyond a “cookbook” approach to data science.
What You’ll Learn
Common statistical measures and plots to describe data and results
Statistical inference using both the Bayesian and modern frequentist approaches
Common statistical pitfalls and how to avoid them
How statistical theory is applied to real-world data analysis
Get Hands-On Experience
Work with Python statistical packages
Use statistics to summarize and visualize data
Apply sampling techniques to estimation problems
Build and interpret linear models
Earn a Digital Badge
After successfully completing this course, you can claim a digital achievement badge that can be shared on LinkedIn and other social media sites. Learn more about digital badges.
