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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/managing-describing-analyzing-data
课程评论：没有评论
课程名称：数据管理、描述与分析 课程概述：本课程将为您提供理解所拥有数据的基础知识，并解释为什么正确分类数据是做出正确决策的第一步。您将学习如何使用描述性统计和R软件以图形和数字方式描述数据。课程中将介绍四种在数据分析中常用的概率分布，并使用适当的概率分布分析数据集。最后，您将了解抽样误差、抽样分布及决策错误的基本知识。 本课程可作为CU Boulder大学数据科学硕士（MS-DS）学位的一部分获得学术学分，课程通过Coursera平台提供。MS-DS是一个跨学科的学位项目，由CU Boulder的应用数学、计算机科学、信息科学等多个部门的教师共同授课。该项目采用基于表现的招生方式，无需申请过程，非常适合计算机科学、信息科学、数学和统计等广泛背景的本科教育和/或专业经验的个体。 课程大纲： 1. 数据与测量 - 描述：完成本模块后，学生将能够使用R及R Studio处理数据，并通过测量尺度分类数据类型。 2. 图形与数字方式描述数据 - 描述：完成本模块后，学生将能够使用R及R Studio创建数据的可视化表示，并计算描述性统计数据，以描述数据的位置、分散度和形状。 3. 概率与概率分布 - 描述：完成本模块后，学生将能够应用概率和概率分布的规则和条件，使用R及R Studio进行决策和问题解决。 4. 抽样分布、误差与估计 - 描述：完成本模块后，学生将能够使用R及R Studio表征抽样及其抽样分布、误差和与统计推断相关的估计。 5. 两组假设检验 - 描述：完成本模块后，学生将能够使用R及R Studio对独立和依赖数据的两组进行统计检验。 欲了解更多关于数据科学硕士项目的信息，请访问：https://www.coursera.org/degrees/master-of-science-data-science-boulder。
Name:Data and Measurement
Description:Upon completion of this module, students will be able to use R and R Studio to work with data and classify types of data using measurement scales.
Name:Describing Data Graphically and Numerically
Description:Upon completion of this module, students will be able to use R and RStudio to create visual representations of data, and calculate descriptive statistics to describe location, spread and shape of data.
Name:Probability and Probability Distributions
Description:Upon completion of this module, students will be able to apply the rules and conditions of probability and probability distributions to make decisions and solve problems using R and R Studio.
Name:Sampling Distributions, Error and Estimation
Description:Upon completion of this module, students will be able to use R and RStudio to characterize sampling and sampling distributions, error and estimation with respect to statistical inference.
Name:Two Sample Hypothesis Testing
Description:Upon completion of this module, students will be able to use R and RStudio to perform statistical tests for two groups with independent and dependent data.
In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making. 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.
