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
TAKE AS PART OF CERTIFICATE OR ON ITS OWN
Part of the Certificate in Data Science, this course is sometimes available to take on its own. To enroll, you must meet all admissions requirements.
Those who successfully complete this course can use it to gain entry to the Certificate in Machine Learning; you’ll be automatically accepted after you submit your application and fee.