Data Analytics Foundations for Accountancy I

所在平台: Coursera

课程主页: https://www.coursera.org/learn/data-analytics-accountancy-1

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课程简介

课程名称：会计数据分析基础 I 概述：欢迎参加会计数据分析基础 I 课程！您将与成千上万的学习者一起学习，我期待您的贡献。建议您花几分钟时间浏览课程网站，查看每周将要覆盖的内容和完成课程所需的作业。点击讨论区与其他同学讨论课程材料。 若对课程内容有疑问，请在论坛中提问以获得帮助。若有技术问题，请访问学习者帮助中心。 祝您学习顺利，希望您享受这门课程！ 课程大纲： 1. **课程介绍**：熟悉课程、同学及学习环境，并获得课程所需的技术技能。 2. **模块 1：基础知识**：介绍课程内容及 Jupyter 服务器，学习分析脚本的具体案例，掌握如何使用服务器创建、编辑和运行笔记本，并学习如何编写 Markdown 格式的文档，最后开始学习用于数据分析的 Python 编程语言。 3. **模块 2：Python 入门**：学习 Python 编程语言的基本特性，了解编程对会计学生的重要性，掌握常用数据结构、函数的编写及控制程序执行的基础知识，最终能够编写 Python 脚本进行基本的数据分析任务。 4. **模块 3：数据分析基础**：介绍数据分析的基本概念，了解会计大数据的报告，学习 Unix 文件系统以及如何在 Python 中读取和写入数据，掌握 Pandas 模块的基本使用。 5. **模块 4：统计数据分析**：学习使用 Pandas 模块进行 Excel 中的基本操作，了解 Numpy 模块的使用，掌握描述性统计以及 Pandas 的高级功能，如掩码、分组、堆叠和透视表。 6. **模块 5：可视化入门**：了解可视化在数据探索中的重要性，学习如何创建简单的可视化（基本线图），并利用 Seaborn 模块改善图形美观，探索一维数据集的可视化方法。 7. **模块 6：概率入门**：学习概率基础及其与统计数据分析的关系，包括随机变量、简单概率计算及条件概率和贝叶斯定理的应用。 8. **模块 7：二维数据探索**：扩展对二维数据的可视化和分析，学习如何绘制二维散点图，识别相关性和异常值，并了解普通线性回归的应用。 9. **模块 8：密度估计入门**：学习直方图的局限性，掌握非参数密度估计的核心技术—核密度估计（KDE），了解如何通过密度估计生成数据模型。 该课程通过实践与理论相结合的方式，帮助学员在会计领域有效应用数据分析技巧。

课程大纲

Name:Course Orientation

Description:You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

Name:Module 1: Foundations

Description:This module serves as the introduction to the course content and the course Jupyter server, where you will run your analytics scripts. First, you will read about specific examples of how analytics is being employed by Accounting firms. Next, you will learn about the capabilities of the course Jupyter server, and how to create, edit, and run notebooks on the course server. After this, you will learn how to write Markdown formatted documents, which is an easy way to quickly write formatted text, including descriptive text inside a course notebook. Finally, you will begin learning about Python, the programming language used in this course for data analytics.

Name:Module 2: Introduction to Python

Description:This module focuses on the basic features in the Python programming language that underlie most data analytics scripts. First, you will read about why accounting students should learn to write computer programs. Second, you will learn about basic data structures commonly used in Python programs. Third, you will learn how to write functions, which can be repeatedly called, in Python, and how to use them effectively in your own programs. Finally, you will learn how to control the execution process of your Python program by using conditional statements and looping constructs. At the conclusion of this module, you will be able to write Python scripts to perform basic data analytic tasks.

Name:Module 3: Introduction to Data Analysis

Description:This module introduces fundamental concepts in data analysis. First, you will read a report from the Association of Accountants and Financial Professionals in Business that explores Big Data in Accountancy. Next, you will learn about the Unix file system, which is the operating system used for most big data processing (as well as Linux and Mac OSX desktops and many mobile phones). Second, you will learn how to read and write data to a file from within a Python program. Finally, you will learn about the Pandas Python module that can simplify many challenging data analysis tasks, and includes the DataFrame, which programmatically mimics many of the features of a traditional spreadsheet.

Name:Module 4: Statistical Data Analysis

Description:This module introduces fundamental concepts in data analysis. First, you will read about how to perform many basic tasks in Excel by using the Pandas module in Python. Second, you will learn about the Numpy module, which provides support for fast numerical operations within Python. This module will focus on using Numpy with one-dimensional data (i.e., vectors or 1-D arrays), but a later module will explore using Numpy for higher-dimensional data. Third, you will learn about descriptive statistics, which can be used to characterize a data set by using a few specific measurements. Finally, you will learn about advanced functionality within the Pandas module including masking, grouping, stacking, and pivot tables.

Name:Module 5: Introduction to Visualization

Description:This module introduces visualization as an important tool for exploring and understanding data. First, the basic components of visualizations are introduced with an emphasis on how they can be used to convey information. Also, you will learn how to identify and avoid ways that a visualization can mislead or confuse a viewer. Next, you will learn more about conveying information to a user visually, including the use of form, color, and location. Third, you will learn how to actually create a simple visualization (basic line plot) in Python, which will introduce creating and displaying a visualization within a notebook, how to annotate a plot, and how to improve the visual aesthetics of a plot by using the Seaborn module. Finally, you will learn how to explore a one-dimensional data set by using rug plots, box plots, and histograms.

Name:Module 6: Introduction to Probability

Description:In this Module, you will learn the basics of probability, and how it relates to statistical data analysis. First, you will learn about the basic concepts of probability, including random variables, the calculation of simple probabilities, and several theoretical distributions that commonly occur in discussions of probability. Next, you will learn about conditional probability and Bayes theorem. Third, you will learn to calculate probabilities and to apply Bayes theorem directly by using Python. Finally, you will learn to work with both empirical and theoretical distributions in Python, and how to model an empirical data set by using a theoretical distribution.

Name:Module 7: Exploring Two-Dimensional Data

Description:This modules extends what you have learned in previous modules to the visual and analytic exploration of two-dimensional data. First, you will learn how to make two-dimensional scatter plots in Python and how they can be used to graphically identify a correlation and outlier points. Second, you will learn how to work with two-dimensional data by using the Numpy module, including a discussion on analytically quantifying correlations in data. Third, you will read about statistical issues that can impact understanding multi-dimensional data, which will allow you to avoid them in the future. Finally, you will learn about ordinary linear regression and how this technique can be used to model the relationship between two variables.

Name:Module 8: Introduction to Density Estimation

Description:Often, as part of exploratory data analysis, a histogram is used to understand how data are distributed, and in fact this technique can be used to compute a probability mass function (or PMF) from a data set as was shown in an earlier module. However, the binning approach has issues, including a dependance on the number and width of the bins used to compute the histogram. One approach to overcome these issues is to fit a function to the binned data, which is known as parametric estimation. Alternatively, we can construct an approximation to the data by employing a non-parametric density estimation. The most commonly used non-parametric technique is kernel density estimation (or KDE). In this module, you will learn about density estimation and specifically how to employ KDE. One often overlooked aspect of density estimation is the model representation that is generated for the data, which can be used to emulate new data. This concept is demonstrated by applying density estimation to images of handwritten digits, and sampling from the resulting model.

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