Linear ModelR ProgrammingStatistical ModelregressionCalculusand probability theory.Linear Algebra
Statistical Modeling for Data Science Applications 专项课程
Build Your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science
您将学到的内容有
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.
您将获得的技能
关于此 专项课程
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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.
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
应用的学习项目
Learners will master the application and implementation of statistical models through auto-graded and peer reviewed Jupyter Notebook assignments. In these assignments, learners will use real-world data and advanced statistical modeling techniques to answer important scientific and business questions.
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
Calculus, linear algebra, and probability theory.
完成课程大约需要 4 个月
建议进度：9 小时/周
英语（English）
字幕：英语（English）
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
Calculus, linear algebra, and probability theory.
完成课程大约需要 4 个月
建议进度：9 小时/周
英语（English）
字幕：英语（English）
专项课程的运作方式
加入课程
Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。
实践项目
每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。
获得证书
在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 3 门课程
Modern Regression Analysis in R
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.
ANOVA and Experimental Design
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation.
Generalized Linear Models and Nonparametric Regression
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.
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 专项课程 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。
常见问题
完成专项课程后我会获得大学学分吗？
Can I just enroll in a single course?
我可以只注册一门课程吗？
有助学金吗？
我可以免费学习课程吗？
此课程是 100% 在线学习吗？是否需要现场参加课程？
完成专项课程需要多长时间？
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
完成专项课程后我会获得大学学分吗？
还有其他问题吗？请访问 学生帮助中心。
