Statistical Modeling for Data Science Applications Specialization
Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this online data science specialization, you will learn to use intermediate and advanced statistical modeling techniques, including the theory and application of linear regression analysis, ANOVA and experimental design, and generalized linear and additive models. Emphasis is placed on analyzing real data using the R programming language.
By completing this specialization, you will be able to:
- Correctly analyze and apply tools of regression analysis to model relationship between variables and make predictions given a set of input variables
- Successfully conduct experiments based on best practices in experimental design
- Use advanced statistical modeling techniques, such as generalized linear and additive models, to model a wide range of real-world relationships
Courses
- Modern Regession Analysis in R
- ANOVA & Experimental Design
- Generalized Linear Models & Nonparametric Regression
This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. Learn more about the MS-DS program.