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 three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.
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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 three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.
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 three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.
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. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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What's inside
Three courses
Modern Regression Analysis in R
Modern Regression Analysis in R
ANOVA and Experimental Design
ANOVA and Experimental Design
Generalized Linear Models and Nonparametric Regression
Generalized Linear Models and Nonparametric Regression
Learning objectives
- 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 wide range of real-world relationships.
- 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 wide range of real-world relationships.
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