本課程是 Statistical Modeling for Data Science Applications 專項課程專項課程的一部分

Brian Zaharatos

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Statistical Modeling for Data Science Applications 專項課程科罗拉多大学波德分校

課程信息

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In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.
This course 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.

Logo adapted from photo by Vincent Ledvina on Unsplash

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第 3 門課程（共 3 門）

Statistical Modeling for Data Science Applications 專項課程

中級

Calculus, linear algebra, and probability theory.

完成時間大約為42 小時

英語（English）

您將獲得的技能

Calculus
and probability theory.
Linear Algebra

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

Coursera Labs

包括實踐學習項目。

了解更多關於 Coursera Labs 的信息

第 3 門課程（共 3 門）

Statistical Modeling for Data Science Applications 專項課程

中級

Calculus, linear algebra, and probability theory.

完成時間大約為42 小時

英語（English）

授課教師

授課教師評分

4.59/5 （5 個評分）

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Department of Applied Mathematics

6,088 個學生

3 門課程

提供方

科罗拉多大学波德分校

開始攻讀學位

該課程屬於科罗拉多大学波德分校提供的 1 個在線學位計劃。當您通過大學註冊學分課程而非學位課程並想在線完成時，它會計入 CU-Boulder 學位的學分課時。您只需通過大學申請即可。

Master of Science in Data Science

科罗拉多大学波德分校

註冊以獲取學分

授課大綱 - 您將從這門課程中學到什麼

週

週 1

完成時間為 12 小時

An Introduction to Generalized Linear Models Through Binomial Regression

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, including the most common binomial link functions; correctly interpret the binomial regression model; and consider various methods for assessing the fit and predictive power of the binomial regression model.

完成時間為 12 小時

7 個視頻（總計 75 分鐘）, 3 個閱讀材料, 7 個測驗

7 個視頻

From Linear Models to Generalized Linear Models12分鐘

The Components of a GLM6分鐘

The Exponential Family of Distributions14分鐘

Introduction to Binomial Regression9分鐘

Binomial Regression Parameter Estimation11分鐘

Interpretation of Binomial Regression7分鐘

Binomial Regression in R11分鐘

3 個閱讀材料

Welcome and Where to Find Help 10分鐘

Information on Peer Reviews10分鐘

FairML Book, Introduction10分鐘

3 個練習

Introduction to Generalized Linear Models30分鐘

Binomial Regression30分鐘

Binomial Regression Inference30分鐘

週

週 2

完成時間為 9 小時

Models for Count Data

In this module, we will consider how to model count data. When the response variable is a count of some phenomenon, and when that count is thought to depend on a set of predictors, we can use Poisson regression as a model. We will describe the Poisson regression in some detail and use Poisson regression on real data. Then, we will describe situations in which Poisson regression is not appropriate, and briefly present solutions to those situations.

完成時間為 9 小時

7 個視頻（總計 83 分鐘）

7 個視頻

Poisson Regression: A New Model for Count Data13分鐘

Poisson Regression Parameter Estimation6分鐘

Interpreting the Poisson Regression Model7分鐘

Poisson Regression on Real Data in R21分鐘

Goodness of Fit for Poisson Regression I16分鐘

Goodness of Fit for Poisson Regression II4分鐘

Overdispersion12分鐘

2 個練習

Poisson Regression Basics30分鐘

Poisson Regression Inference and Goodness of Fit30分鐘

週

週 3

完成時間為 9 小時

Introduction to Nonparametric Regression

完成時間為 9 小時

6 個視頻（總計 66 分鐘）

週

週 4

完成時間為 12 小時

Introduction to Generalized Additive Models

Some models, such as linear regression, are easily interpretable, but inflexible, in that they don't capture many real-world relationships accurately. Other models, such as neural networks, are quite flexible, but very difficult to interpret. Generalized additive models (GAMs) are a nice balance between flexibility and interpretability. In this module, we will further motivate GAMs, learn the basic mathematics of fitting GAMs, and implementing them on simulated and real data in R.

完成時間為 12 小時

6 個視頻（總計 81 分鐘）, 1 個閱讀材料, 4 個測驗

6 個視頻

Motivating Generalized Additive Models17分鐘

Generalized Additive Models in R16分鐘

Inference with Generalized Additive Models: Effective Degrees of Freedom12分鐘

Inference with Generalized Additive Models: Tests4分鐘

Generalized Additive Models in R: Inference and Interpretation13分鐘

Generalized Additive Models: A Complete Example with Real Data16分鐘

1 個閱讀材料

Required: Generalized additive models for data science10分鐘

2 個練習

Generalized Additive Models: Basics30分鐘

Generalized Additive Models: Inference and Data Analysis30分鐘

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.

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. Logo adapted from photo by Vincent Ledvina on Unsplash

Statistical Modeling for Data Science Applications

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Generalized Linear Models and Nonparametric Regression

課程信息

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