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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/statistical-inference-for-data-science-applications
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课程名称：数据科学基础：统计推断 课程概述：本课程旨在教授概率在统计学和数据科学中的重要性。学习者将理解条件事件和独立事件之间的关系，计算多个随机变量的期望值和方差，并掌握“良好”估计量的特征，从而能够比较不同的估计量。通过本课程，学习者将获得推断、统计、数据科学、概率、中心极限定理、连续随机变量、贝叶斯定理和离散随机变量等技能。 关于本专业：该程序旨在为学习者提供坚实的概率理论基础，以便为更广泛的统计学习做好准备。同时，它还将介绍统计学的基本原理，并使学习者具备使用R编程语言进行基本统计分析所需的技能。该专业可以作为科罗拉多大学博尔德分校（CU Boulder）数据科学硕士学位（MS-DS）的一部分获得学术学分。该学位是一个跨学科的项目，结合了应用数学、计算机科学、信息科学等多个系的教职工，适合拥有计算机科学、信息科学、数学和统计学等广泛背景的个人。 学习项目：学习者将通过在Jupyter Notebooks中完成练习，实践新的概率技能，包括对数据集的基本统计分析。此外，学习者将通过课程中的基准测验测试他们的知识。 证书信息：完成课程后可获得分享证书。课程完全在线，学习者可以随时开始，自由安排学习进度，并设置灵活的截止日期。课程为中级水平，需要完成微积分直至微积分II（最好是多元微积分），并具备一定的R语言编程经验。预计完成该课程大约需要4个月，每周建议学习7小时。 适用语言：课程提供英文内容，附有英文字幕。 课程链接： [数据科学的概率理论基础](https://www.coursera.org/learn/probability-theory-foundation-for-data-science) [数据科学中估计的统计推断](https://www.coursera.org/learn/statistical-inference-for-estimation-in-data-science) [数据科学应用中的统计推断与假设检验](https://www.coursera.org/learn/statistical-inference-and-hypothesis-testing-in-data-science-applications)
Course Link: https://www.coursera.org/learn/probability-theory-foundation-for-data-science
Name:Probability Theory: Foundation for Data Science
Description:Offered by University of Colorado Boulder. Understand the foundations of probability and its relationship to statistics and data science. ... Enroll for free.
Course Link: https://www.coursera.org/learn/statistical-inference-for-estimation-in-data-science
Name:Statistical Inference for Estimation in Data Science
Description:Offered by University of Colorado Boulder. This course introduces statistical inference, sampling distributions, and confidence intervals. ... Enroll for free.
Course Link: https://www.coursera.org/learn/statistical-inference-and-hypothesis-testing-in-data-science-applications
Name:Statistical Inference and Hypothesis Testing in Data Science Applications
Description:Offered by University of Colorado Boulder. This course will focus on theory and implementation of hypothesis testing, especially as it ... Enroll for free.
What you will learn
Explain why probability is important to statistics and data science.
See the relationship between conditional and independent events in a statistical experiment.
Calculate the expectation and variance of several random variables and develop some intuition.
Identify characteristics of “good” estimators and be able to compare competing estimators.
Skills you will gain
Inference
Statistics
Data Science
Probability
central limit theorem
continuous random variables
Bayes' Theorem
discrete random variables
About this Specialization
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This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in 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 Christopher Burns on Unsplash.
Applied Learning Project
Learners will practice new probability skills. including fundamental statistical analysis of data sets, by completing exercises in Jupyter Notebooks. In addition, learners will test their knowledge by completing benchmark quizzes throughout the courses.
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
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Hours to complete
Approximately 4 months to complete
Suggested pace of 7 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
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Hours to complete
Approximately 4 months to complete
Suggested pace of 7 hours/week
Available languages
English
Subtitles: English
