This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
課程信息
Calculus, linear algebra, and probability theory.
您將獲得的技能
- Linear Model
- R Programming
- Statistical Model
- regression
Calculus, linear algebra, and probability theory.
提供方

科罗拉多大学波德分校
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授課大綱 - 您將從這門課程中學到什麼
Introduction to Statistical Models
In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.
Linear Regression Parameter Estimation
In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.
Inference in Linear Regression
In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
Prediction and Explanation in Linear Regression Analysis
In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.
關於 Statistical Modeling for Data Science Applications 專項課程
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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