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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/statistical-modeling-for-data-science-applications
课程评论：没有评论
课程名称：数据科学应用的统计建模 课程概述：本课程旨在教授如何正确分析和应用回归分析工具，以建模变量之间的关系，并在给定一组输入变量时进行预测。学员将成功地根据实验设计的最佳实践进行实验，利用高级统计建模技术，如广义线性模型和加性模型，来建模各种真实世界的关系。 学习内容： - 学习线性模型 - R编程技能 - 统计模型的应用 - 回归分析 - 微积分 - 概率论 - 线性代数 课程背景：统计建模是数据科学的核心。精心设计的统计模型使数据科学家能够从有限的数据中得出结论。该专业化课程包括中级和高级统计建模技术，特别关注线性回归分析、方差分析（ANOVA）和实验设计，以及广义线性和加性模型的理论与实践。强调使用R编程语言分析真实数据。 该专业化课程亦可作为科罗拉多大学博尔德分校数据科学硕士学位（MS-DS）的一部分进行学分学习，MS-DS项目结合了应用数学、计算机科学、信息科学等多个学科的教师资源，适合具有广泛专业背景的学习者。 应用学习项目：学员将通过自动评分和同伴评审的Jupyter Notebook作业掌握统计模型的应用与实施，利用真实数据和高级统计建模技术回答重要的科学和商业问题。 证书：完成课程后可获得分享证书，所有课程均为100%在线，学习时间灵活，推荐每周学习9小时，课程预计需时约4个月完成。课程要求具备微积分、线性代数和概率论的基础知识。 课程链接： 1. [现代回归分析](https://www.coursera.org/learn/modern-regression-analysis-in-r) 2. [方差分析与实验设计](https://www.coursera.org/learn/anova-and-experimental-design) 3. [广义线性模型与非参数回归](https://www.coursera.org/learn/generalized-linear-models-and-nonparametric-regression) 此课程为中级课程，提供英语与字幕，鼓励学习者尽快注册参与。
Course Link: https://www.coursera.org/learn/modern-regression-analysis-in-r
Name:Modern Regression Analysis in R
Description:Offered by University of Colorado Boulder. This course will provide a set of foundational statistical modeling tools for data science. In ... Enroll for free.
Course Link: https://www.coursera.org/learn/anova-and-experimental-design
Name:ANOVA and Experimental Design
Description:Offered by University of Colorado Boulder. This second course in statistical modeling will introduce students to the study of the analysis ... Enroll for free.
Course Link: https://www.coursera.org/learn/generalized-linear-models-and-nonparametric-regression
Name:Generalized Linear Models and Nonparametric Regression
Description:Offered by University of Colorado Boulder. In the final course of the statistical modeling for data science program, learners will study a ... Enroll for free.
What you will learn
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.
Skills you will gain
Linear Model
R Programming
Statistical Model
regression
Calculus
and probability theory.
Linear Algebra
About this Specialization
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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
Applied Learning Project
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.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Calculus, linear algebra, and probability theory.
Hours to complete
Approximately 4 months to complete
Suggested pace of 9 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Calculus, linear algebra, and probability theory.
Hours to complete
Approximately 4 months to complete
Suggested pace of 9 hours/week
Available languages
English
Subtitles: English
