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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/intro-accounting-data-analytics-visual
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课程名称：会计数据分析与可视化导论 课程简介：会计一直以来都与分析思维密切相关。自卢卡·帕乔利时代以来，数学和秩序在商业交易分析中的重要性就被强调。随着时间的推移，会计所需的技能从纸笔演变到打字机和计算器，随后是电子表格和会计软件。而如今，处理大数据分析的技能变得日益重要。本课程旨在帮助会计学生培养分析思维，同时准备他们学习数据分析编程语言，如Python和R。 课程分为三个主要部分： 第一部分：将会计与分析连接起来。我们讨论会计五大子领域（财务、管理、审计、税务和系统）中的任务如何需要分析思维，并探讨如何通过大数据分析更高效地完成这些任务。我们提出了一个大数据分析的FACT框架：框定问题、组装数据、计算数据、以及向他人展示结果。 第二部分：强调数据组装的重要性。我们使用财务报表数据，解释有效计算和可视化所需的理想数据及数据集特征。 第三部分也是最大的部分：演示和探讨如何使用Excel和Tableau来分析大数据。我们介绍视觉感知原则，并应用这些原则创建有效的可视化。接着，我们在销售点和贷款数据的背景下，探讨回归、线性规划（使用Excel Solver）和聚类等基本数据分析工具。最后，我们展示数据分析编程语言在数据的组装、可视化和分析中的强大能力，介绍了作为编程语言示例的Visual Basic for Applications及其开发环境。 课程大纲包含以下模块： - 课程介绍及模块一：会计分析概论 - 模块一：会计分析的演变与数据分析的影响 - 模块二：会计分析与分析思维 - 模块三：数据及其特性 - 模块四：数据可视化基础 - 模块五：使用Tableau进行数据可视化 - 模块六：Excel中的分析工具1 - 模块七：Excel中的分析工具2 - 模块八：Excel中的自动化 通过本课程，学员将获得深厚的数据分析基础，掌握使用Excel与Tableau进行数据处理和可视化的能力。
Name:Course Introduction and Module 1: Introduction to Accountancy Analytics
Description:In this module, you will become familiar with the course, your instructor and your classmates, and our learning environment. This orientation module will also help you obtain the technical skills required to navigate and be successful in this course.
Name:Module 1: Introduction to Accountancy Analytics
Description:In this module, you will learn how the accounting profession has evolved. You will recognize how data analytics has influenced the accounting profession and how accountants have the ability to impact how data analytics is used in the profession, as well as in an organization. Finally, you will learn how data analytics is influencing the different subdomains within accounting.
Name:Module 2: Accounting Analysis and an Analytics Mindset
Description:In this module, you will learn to recognize the importance of making room for empirical enquiry in decision making. You will explore characteristics of an analytical mindset in business and accounting contexts, and link those to your core courses. You will then evaluate a framework for making data-driven decisions using big data.
Name:Module 3: Data and its Properties
Description:This module looks at specific characteristics of data that make it useful for decision making.
Name:Module 4: Data Visualization 1
Description:In this module, you will learn fundamental principles that underlie data visualizations. Using those principles, you will identify use cases for different charts and learn how to build those charts in Excel. You will then use your knowledge of different charts to identify alternative charts that are better suited for directing attention.
Name:Module 5: Data Visualization 2
Description:In this module, you’ll learn how to use Tableau to do with data what spies do when observing their surroundings: get an overview of the data, narrow in on certain aspects of the data that seem abnormal, and then analyze the data. Tableau is a great tool for facilitating the overview, zoom, then filter details-on-demand approach. Tableau is a lot like a more powerful version of Excel's pivot table and pivot chart functionality.
Name:Module 6: Analytic Tools in Excel 1
Description:In this module, you'll be guided through a mini-case study that will illustrate the first three parts of the FACT model, with a focus on the C, or calculations part of the FACT model. First, you will perform a correlation analysis to identify two-way relationships, and analyze correlations using a correlation matrix and scatter plots. You will then build on your knowledge of correlations and learn how to perform regression analysis in Excel. Finally, you will learn how to interpret and evaluate the diagnostic metrics and plots of a regression analysis.
Name:Module 7: Analytic Tools in Excel 2
Description:In this module, you’ll learn how the regression algorithm can be applied to fit a wide variety of relationships among data. Specifically, you’ll learn how to set up the data and run a regression to estimate the parameters of nonlinear relationships, categorical independent variables. You’ll also investigate if the effect of an independent variable depends on the level of another independent variable by including interaction terms in the multiple regression model. Another aspect of this module is learning how to evaluate models, regression or otherwise, to find the most favorable levels of the independent variables. For models that explain revenue, the most favorable levels of the independent variables will maximize revenue. In contrast, if you have a model that describes costs, like a budget, then the most favorable levels of the independent variables will minimize costs. Optimizing models can be difficult because there are so many inputs and constraints that need to be managed. In this module, you’ll learn how to use the Solver Add-In to find the optimal level of inputs. For some models, the dependent variable is a binary variable that has only two values, such as true/false, win/lose, or invest/not invest. In these situations, a special type of regression, called logistic regression, is used to predict how each observation should be classified. You’ll learn about the logit transformation that’s used to convert a binary outcome to a linear relationship with the independent variables. Excel doesn’t have a built-in logistic regression tool, so you’ll learn how to manually design a logistic regression model, and then optimize the parameters using the Solver Add-In tool.
Name:Module 8: Automation in Excel
Description:The lessons in this module are organized around several useful tasks, including stacking multiple dataframes together into one dataframe, creating multiple histograms to accompany the descriptive statistics, and learning how to perform k-means clustering. After going through this module, you’ll not only gain a foundation to help you understand coding, but you’ll also learn more about analyzing financial data. Along the way, I hope that you’ll also pick up on a few other useful Excel functions.
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
