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.
课程信息
15,582 次近期查看
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 3 门）
中级
Calculus, linear algebra, and probability theory.
完成时间大约为42 小时
英语（English）
字幕：英语（English）
您将获得的技能
- Calculus
- and probability theory.
- Linear Algebra
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 3 门）
中级
Calculus, linear algebra, and probability theory.
完成时间大约为42 小时
英语（English）
字幕：英语（English）
提供方
科罗拉多大学波德分校
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
立即开始攻读硕士学位
此 课程 隶属于 科罗拉多大学波德分校 提供的 100% 在线 Master of Science in Data Science。
如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
授课大纲 - 您将从这门课程中学到什么
完成时间为 12 小时
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.
完成时间为 12 小时
7 个视频 （总计 75 分钟）, 3 个阅读材料, 7 个测验
完成时间为 9 小时
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.
完成时间为 9 小时
7 个视频 （总计 83 分钟）
完成时间为 9 小时
Introduction to Nonparametric Regression
In this module, we will introduce the concept of a nonparametric regression model. We will contrast this notion with the parametric models that we have studied so far. Then, we’ll study particular nonparametric regression models: kernel estimators and splines. Finally, we will introduce additive models as a blending of parametric and nonparametric methods.
完成时间为 9 小时
6 个视频 （总计 66 分钟）
完成时间为 12 小时
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.
完成时间为 12 小时
6 个视频 （总计 81 分钟）, 1 个阅读材料, 4 个测验
关于 Statistical Modeling for Data Science Applications 专项课程
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.
常见问题
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
