Data Analytics Foundations for Accountancy I

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Beschreibung

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About this course: Welcome to Data Analytics Foundations for Accountancy I! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems…

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Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Data Analytics, C/C++, SQL & MySQL, Python und Big Data.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: Welcome to Data Analytics Foundations for Accountancy I! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

Created by:  University of Illinois at Urbana-Champaign
  • Taught by:  Robert Brunner, Professor

    Accountancy
Language English How To Pass Pass all graded assignments to complete the course. Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

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University of Illinois at Urbana-Champaign 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.

Syllabus


WEEK 1


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 videos, 4 readings, 1 practice quiz expand


  1. Video: Welcome to Data Analytics Foundations for Accountancy I
  2. Video: Meet Professor Brunner
  3. Reading: Syllabus
  4. Reading: About the Discussion Forums
  5. Practice Quiz: Orientation Quiz
  6. Reading: Updating Your Profile
  7. Discussion Prompt: Getting to Know Your Classmates
  8. Reading: Social Media


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 videos, 2 readings expand


  1. Reading: Module 1 Overview
  2. Video: Introduction to Module 1
  3. Video: The Importance of Data Analytics in Modern Accountancy
  4. Reading: Lesson 1-1 Readings
  5. Video: Introduction to the Course JupyterHub Server
  6. Notebook: Introduction to Jupyter Notebook
  7. Video: Introduction to Markdown
  8. Notebook: Introduction to Markdown Notebook
  9. Video: Introduction to Python
  10. Notebook: Introduction to Python Notebook
  11. Notebook: Module 1 Programming Assignment Notebook

Graded: Module 1 Graded Quiz
Graded: Module 1 Programming Assignment

WEEK 2


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 videos, 2 readings expand


  1. Reading: Module 2 Overview
  2. Video: Introduction to Module 2
  3. Video: Why Accounting Students Should Learn to Code
  4. Reading: Lesson 2-1 Readings
  5. Video: Python Data Structures
  6. Notebook: Python Data Structures Notebook
  7. Video: Introduction to Python Functions
  8. Notebook: Python Functions Notebook
  9. Video: Python Programming Concepts
  10. Notebook: Python Programming Concepts
  11. Notebook: Module 2 Programming Assignment Notebook

Graded: Module 2 Graded Quiz
Graded: Module 2 Programming Assignment

WEEK 3


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 videos, 2 readings expand


  1. Reading: Module 3 Overview
  2. Video: Introduction to Module 3
  3. Video: Why Use Python Instead of Excel?
  4. Reading: Lesson 3-1 Readings
  5. Video: Introduction to Unix
  6. Notebook: Introduction to Unix Notebook
  7. Video: Python File I/O
  8. Notebook: Python File I/O
  9. Video: Introduction to Pandas
  10. Notebook: Introduction to Pandas Notebook
  11. Notebook: Module 3 Programming Assignment Notebook

Graded: Module 3 Graded Quiz
Graded: Module 3 Programming Assignment

WEEK 4


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 videos, 2 readings expand


  1. Reading: Module 4 Overview
  2. Video: Introduction to Module 4
  3. Video: How the Pandas Module Can Support Standard Business Analytics
  4. Reading: Lesson 4-1 Readings
  5. Video: Introduction to Numpy
  6. Notebook: Introduction to Numpy Notebook
  7. Video: Introduction to Descriptive Statistics
  8. Notebook: Introduction to Descriptive Statistics Notebook
  9. Video: Advanced Pandas
  10. Notebook: Advanced Pandas Notebook
  11. Notebook: Module 4 Programming Assignment Notebook

Graded: Module 4 Graded Quiz
Graded: Module 4 Programming Assignment

WEEK 5


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 videos, 4 readings expand


  1. Reading: Module 5 Overview
  2. Video: Introduction to Module 5
  3. Video: Creating Clear and Powerful Visualizations
  4. Reading: Lesson 5-1 Readings and Resources
  5. Video: Visualization of Quantitative Data
  6. Reading: Lesson 5-2 Readings and Resources
  7. Video: Introduction to Plotting
  8. Notebook: Introduction to Plotting Notebook
  9. Video: Introduction to Data Visualization
  10. Reading: Lesson 5-4 Reading
  11. Notebook: Introduction to Data Visualization Notebook
  12. Notebook: Module 5 Programming Assignment Notebook

Graded: Module 5 Graded Quiz
Graded: Module 5 Programming Assignment

WEEK 6


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 videos, 5 readings expand


  1. Reading: Module 6 Overview
  2. Video: Introduction to Module 6
  3. Video: Introduction to Probability
  4. Reading: Lesson 6-1 Readings
  5. Video: Introduction to Bayes Theorem
  6. Reading: Lesson 6-2 Readings
  7. Video: Calculating Probabilities in Python
  8. Reading: Lesson 6-3 Readings
  9. Notebook: Introduction to Probability Notebook
  10. Video: Introduction to Distributions
  11. Reading: Lesson 6-4 Readings
  12. Notebook: Introduction to Distributions Notebook
  13. Notebook: Module 6 Programming Assignment Notebook

Graded: Module 6 Graded Quiz
Graded: Module 6 Programming Assignment

WEEK 7


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 videos, 3 readings expand


  1. Reading: Module 7 Overview
  2. Video: Introduction to Module 7
  3. Video: Introduction to Scatter Plots
  4. Notebook: Python Two-Dimensional Plotting Notebook
  5. Video: Introduction to Numpy Matrices
  6. Notebook: Advanced Numpy Notebook
  7. Video: Statistical Issues When Exploring Multi-Dimensional Data
  8. Reading: Lesson 7-3 Readings and Resources
  9. Video: Introduction to Ordinary Linear Regression
  10. Reading: Lesson 7-4 Readings
  11. Notebook: Introduction to Ordinary Linear Regression Notebook
  12. Notebook: Module 7 Programming Assignment Notebook

Graded: Module 7 Graded Quiz
Graded: Module 7 Programming Assignment

WEEK 8


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 videos, 2 readings expand


  1. Reading: Module 8 Overview
  2. Video: Introduction to Module 8
  3. Video: Why Do Accounting Students Need Data Analytics Skills?
  4. Reading: Lesson 8-1 Readings
  5. Video: Introduction to Density Estimation
  6. Notebook: Introduction to Density Estimation Notebook
  7. Video: Advanced Density Estimation
  8. Notebook: Advanced Density Estimation Notebook
  9. Notebook: Module 8 Programming Assignment Notebook

Graded: Module 8 Graded Quiz
Graded: Module 8 Programming Assignment

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