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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/business-analytics-r
课程评论：没有评论
课程名称：使用R进行商务分析入门 课程概述：几乎每个商业领域都受到数据分析的影响。为了使企业能够充分利用数据分析，必须有能理解商业分析工作流程的领导者。本课程旨在填补人类技能差距，提供一套基础的数据处理技能，这些技能可以应用于许多商业环境。 在本课程中，您将使用数据分析语言R，高效地为分析工具（如算法和可视化）准备商业数据。数据清理、转换、聚合和重塑是商业分析工作流程中至关重要但不太显眼的步骤。 通过学习如何使用R为分析准备数据，您将获得使用RStudio的经验，这是一种强大的集成开发环境（IDE），具有许多简化R编程的内置功能。 在学习商业分析工作流程的同时，您还将考虑商业原则与数据分析之间的相互作用。具体而言，您将探讨委托、控制和可行性如何影响数据处理方式。此外，您还将接触数据自动化和分析可以解决的商业问题的实例，以及不需要从一个平台复制粘贴到另一个平台的方法来传达数据分析结果。 课程大纲： 模块1：如何开始使用数据分析语言解决商业问题？ 在这一模块中，您将了解数据分析在商业领域中的作用，以及R和RStudio的基本概念。 模块2：如何了解我的数据并与他人分享？ 在这一模块中，您将探讨数据是否是一种资产以及如何探索数据集。 模块3：如何使用函数帮助数据准备？ 本模块将讨论为商业分析目的组装数据的重要性，并通过Tidyverse（一组有用的R包）展示数据转换。 模块4：如何对数据进行预处理？ 在这一模块中，我们将学习委托、可行性和控制如何影响数据聚合的水平。然后，我们将重点进行各种数据预处理任务，以便为可视化和算法的使用做准备。
Name:Course Overview and Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problem?
Description:In this module you will be introduced to (1) the role of data analytics in business domains, and (2) R and RStudio.
Name:Module 2: How Do I Get to Know My Data and Share It With Others?
Description:In this module you will explore whether data is an asset and how to explore a dataset.
Name:Module 3: How Can I Use Functions to Help with Data Preparation?
Description:This module starts with a discussion on the importance of assembling data for business analytic purposes, and then illustrates data transformation using Tidyverse, a group of useful R packages.
Name:Module 4: How Do I Preprocess Data?
Description:In this module, we will learn how delegation, feasibility, and control influence the level at which data is aggregated. We then focus on performing a variety of data preprocessing tasks to prepare data for use in visualizations and algorithms.
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use R to prepare data for analysis you will gain experience using RStudio, a powerful integrated development environment (IDE), that has many built-in features that simplify coding with R. As you learn about the business analytic workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
