Statistical Modeling for Data Science Applications Coursera

Overview

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 Statistical Modeling for Data Science Applications course offered by Coursera in partnership with University of Colorado Boulder, 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.

Key facts

• 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.

Skills you'll gain

• General Statistics

• Probability & Statistics

• Regression

• Experiment