課程信息
4.7
6 個評分
1 個審閱

開始攻讀學位

嘗試觀看 Master of Science in Accountancy (iMSA) 學位的課程視頻、閱讀課程以及完成自主學習作業

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為56 小時

建議:7 hours/week...

英語(English)

字幕:英語(English)

開始攻讀學位

嘗試觀看 Master of Science in Accountancy (iMSA) 學位的課程視頻、閱讀課程以及完成自主學習作業

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為56 小時

建議:7 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

Course Orientation

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....
2 個視頻 (總計 8 分鐘), 4 個閱讀材料, 1 個測驗
2 個視頻
Meet Professor Brunner4分鐘
4 個閱讀材料
Syllabus10分鐘
About the Discussion Forums10分鐘
Updating Your Profile10分鐘
Social Media10分鐘
1 個練習
Orientation Quiz10分鐘
完成時間為 8 小時

Module 1: Foundations

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....
5 個視頻 (總計 29 分鐘), 2 個閱讀材料, 2 個測驗
5 個視頻
The Importance of Data Analytics in Modern Accountancy3分鐘
Introduction to the Course JupyterHub Server7分鐘
Introduction to Markdown5分鐘
Introduction to Python8分鐘
2 個閱讀材料
Module 1 Overview10分鐘
Lesson 1-1 Readings10分鐘
1 個練習
Module 1 Graded Quiz20分鐘
2
完成時間為 8 小時

Module 2: Introduction to Python

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....
5 個視頻 (總計 29 分鐘), 2 個閱讀材料, 2 個測驗
5 個視頻
Why Accounting Students Should Learn to Code4分鐘
Python Data Structures7分鐘
Introduction to Python Functions5分鐘
Python Programming Concepts6分鐘
2 個閱讀材料
Module 2 Overview10分鐘
Lesson 2-1 Readings10分鐘
1 個練習
Module 2 Graded Quiz20分鐘
3
完成時間為 8 小時

Module 3: Introduction to Data Analysis

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....
5 個視頻 (總計 29 分鐘), 2 個閱讀材料, 2 個測驗
5 個視頻
Why Use Python Instead of Excel?3分鐘
Introduction to Unix6分鐘
Python File I/O7分鐘
Introduction to Pandas6分鐘
2 個閱讀材料
Module 3 Overview10分鐘
Lesson 3-1 Readings10分鐘
1 個練習
Module 3 Graded Quiz20分鐘
4
完成時間為 8 小時

Module 4: Statistical Data Analysis

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....
5 個視頻 (總計 33 分鐘), 2 個閱讀材料, 2 個測驗
5 個視頻
How the Pandas Module Can Support Standard Business Analytics2分鐘
Introduction to Numpy8分鐘
Introduction to Descriptive Statistics10分鐘
Advanced Pandas8分鐘
2 個閱讀材料
Module 4 Overview10分鐘
Lesson 4-1 Readings10分鐘
1 個練習
Module 4 Graded Quiz20分鐘
5
完成時間為 7 小時

Module 5: Introduction to Visualization

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....
5 個視頻 (總計 29 分鐘), 4 個閱讀材料, 2 個測驗
5 個視頻
Creating Clear and Powerful Visualizations5分鐘
Visualization of Quantitative Data2分鐘
Introduction to Plotting8分鐘
Introduction to Data Visualization8分鐘
4 個閱讀材料
Module 5 Overview10分鐘
Lesson 5-1 Readings and Resources10分鐘
Lesson 5-2 Readings and Resources10分鐘
Lesson 5-4 Reading10分鐘
1 個練習
Module 5 Graded Quiz20分鐘
6
完成時間為 8 小時

Module 6: Introduction to Probability

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....
5 個視頻 (總計 26 分鐘), 5 個閱讀材料, 2 個測驗
5 個視頻
Introduction to Probability2分鐘
Introduction to Bayes Theorem3分鐘
Calculating Probabilities in Python8分鐘
Introduction to Distributions7分鐘
5 個閱讀材料
Module 6 Overview10分鐘
Lesson 6-1 Readings10分鐘
Lesson 6-2 Readings10分鐘
Lesson 6-3 Readings10分鐘
Lesson 6-4 Readings10分鐘
1 個練習
Module 6 Graded Quiz20分鐘
7
完成時間為 8 小時

Module 7: Exploring Two-Dimensional Data

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....
5 個視頻 (總計 32 分鐘), 3 個閱讀材料, 2 個測驗
5 個視頻
Introduction to Scatter Plots7分鐘
Introduction to Numpy Matrices7分鐘
Statistical Issues When Exploring Multi-Dimensional Data5分鐘
Introduction to Ordinary Linear Regression7分鐘
3 個閱讀材料
Module 7 Overview10分鐘
Lesson 7-3 Readings and Resources10分鐘
Lesson 7-4 Readings10分鐘
1 個練習
Module 7 Graded Quiz20分鐘
8
完成時間為 7 小時

Module 8: Introduction to Density Estimation

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....
4 個視頻 (總計 22 分鐘), 2 個閱讀材料, 2 個測驗
4 個視頻
Why Do Accounting Students Need Data Analytics Skills?2分鐘
Introduction to Density Estimation6分鐘
Advanced Density Estimation8分鐘
2 個閱讀材料
Module 8 Overview10分鐘
Lesson 8-1 Readings10分鐘
1 個練習
Module 8 Graded Quiz20分鐘

講師

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Robert Brunner

Professor
Accountancy

領先獲取學位

此 課程 隸屬於 伊利诺伊大学香槟分校 提供的 100% 在線 Master of Science in Accountancy (iMSA)。立即開始學習開放課程或專項課程,觀看 iMBA 教師的課程並完成自主學習作業。 完成每門課程後,您將獲得一個證書,您可以添加到 LinkedIn 和簡歷中。 如果申請並被錄取參加全部課程,您的課程將計入您的學位學習進程。

關於 伊利诺伊大学香槟分校

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

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