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Accounting Data Analytics with Python
  • Level Professional
  • المدة 43 ساعات hours
  • الطبع بواسطة University of Illinois Urbana-Champaign
  • Offered by

عن

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.

الوحدات

About the Course

    • 3 Videos
    • 4 Readings
    • 1 Plugin
Show info about module content

3 Videos

  • Course Introduction
  • About Ronald Guymon
  • About Linden Lu

4 Readings

  • Syllabus
  • Glossary
  • About the Discussion Forums
  • Online Education at Gies College of Business

About Your Classmates

    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Get to Know Your Fellow Learners

1 Readings

  • Update Your Profile

Module 1 Information

    • 1 Videos
    • 2 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Make Connections to Topic

1 Videos

  • Module 1 Introduction

2 Readings

  • Module 1 Overview
  • Module 1 Readings

Lesson 1.1: Introduction to Data Analytics

    • 1 Videos
    • 1 Readings
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1 Videos

  • 1.1 Introduction to Data Analytics

1 Readings

  • Lesson 1.1 Readings

Lesson 1.2: Jupyter Notebook

    • 10 Videos
    • 1 Lab
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1 Labs

  • Introduction to Jupyter Notebook

10 Videos

  • 1.2 Jupyter Notebook
  • Python and Integrated Development Environments (IDEs)
  • Installing and Running Python Using the Anaconda Distribution
  • Installing and Running Python Without Anaconda
  • Navigating Jupyter Notebook
  • Navigating JupyterLab
  • Using Notebook Files
  • Navigating Spyder
  • Comparison of Jupyter Notebook, JupyterLab, and Spyder
  • Refreshing Folders

Lesson 1.3: Introduction to Markdown

    • 2 Videos
    • 1 Lab
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1 Labs

  • Introduction to Markdown

2 Videos

  • 1.3 Introduction to Markdown
  • Markdown Basics

Module 1 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 1 Programming Assignment Score

1 Labs

  • Module 1 Programming Assignment

1 Videos

  • Module 1 Review

1 Quiz

  • Module 1 Quiz

Module 2 Information

    • 1 Videos
    • 2 Readings
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1 Videos

  • Module 2 Introduction

2 Readings

  • Module 2 Overview
  • Module 2 Readings

Lesson 2.1: Introduction to Python

    • 3 Videos
    • 1 Lab
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1 Labs

  • Introduction to Python

3 Videos

  • 2.1 Introduction to Python
  • Python Code Basics
  • Variables, Data Types, and Operators

Lesson 2.2: Introduction to Python Functions

    • 5 Videos
    • 1 Lab
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1 Labs

  • Introduction to Python Functions

5 Videos

  • 2.2 Introduction to Python Functions
  • Built-In Functions
  • User-Defined Functions
  • Functions vs Methods
  • Refreshing Folders

Lesson 2.3: Conditional Statements in Python

    • 3 Videos
    • 1 Lab
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1 Labs

  • Python Conditional Statements

3 Videos

  • 2.3 Conditional Statements in Python
  • Comparison and Logical Operators
  • Working With Conditional Statements

Module 2 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 2 Programming Assignment Score

1 Labs

  • Module 2 Programming Assignment

1 Videos

  • Module 2 Review

1 Quiz

  • Module 2 Quiz

Module 3 Information

    • 1 Videos
    • 2 Readings
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1 Videos

  • Module 3 Introduction

2 Readings

  • Module 3 Overview
  • Module 3 Readings

Lesson 3.1: Introduction to Python Data Structures

    • 6 Videos
    • 1 Lab
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1 Labs

  • Introduction to Python Data Structures

6 Videos

  • 3.1 Introduction to Python Data Structures
  • Introduction to Strings
  • Introduction to Lists
  • Introduction to Dictionaries, Tuples, and Unpacking
  • Common Sequence Operations
  • Refreshing Folders

Lesson 3.2: Working with Python Data Structures

    • 4 Videos
    • 1 Lab
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1 Labs

  • Working With Python Data Structures

4 Videos

  • 3.2 Working With Python Data Structure
  • Working With Strings
  • Working With Lists and Tuples
  • Working With Dictionaries

Lesson 3.3: Introduction to Python Loops

    • 4 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Introduction to Python Loops

4 Videos

  • 3.3 Introduction to Python Loops
  • The For Loop
  • The While Loop
  • Comprehensions

Module 3 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 3 Programming Assignment Score

1 Labs

  • Module 3 Programming Assignment

1 Videos

  • Module 3 Review

1 Quiz

  • Module 3 Quiz

Module 4 Information

    • 1 Videos
    • 2 Readings
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1 Videos

  • Module 4 Introduction

2 Readings

  • Module 4 Overview
  • Module 4 Readings

Lesson 4.1: Writing Python Programs

    • 3 Videos
    • 1 Lab
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1 Labs

  • Writing Python Programs

3 Videos

  • 4.1 Writing Python Programs
  • Python Modules
  • Errors and Exceptions

Lesson 4.2: Introduction to NumPy

    • 4 Videos
    • 1 Lab
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1 Labs

  • Introduction to NumPy

4 Videos

  • 4.2 Introduction to NumPy
  • NumPy Array
  • NumPy Basic Functions
  • Refreshing Folders

Lesson 4.3: Introduction to Pandas

    • 4 Videos
    • 1 Lab
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1 Labs

  • Introduction to Pandas

4 Videos

  • 4.3 Introduction to Pandas
  • Introduction to Dataframes
  • Data Selection With Dataframes
  • Missing Values and Copies With Dataframes

Module 4 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 4 Programming Assignment Score

1 Labs

  • Module 4 Programming Assignment

1 Videos

  • Module 4 Review

1 Quiz

  • Module 4 Quiz

Module 5 Information

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Module 5 Introduction

2 Readings

  • Module 5 Overview
  • Module 5 Readings

Lesson 5.1: Python File I/O

    • 5 Videos
    • 1 Lab
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1 Labs

  • Python File Input/Output

5 Videos

  • 5.1 Python File IO
  • Reading and Writing Files With Base Python
  • Reading and Writing Files With Pandas
  • Preserving Data Types With Pickling
  • Refreshing Folders

Lesson 5.2: Working With the Pandas Dataframe

    • 6 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Working With the Pandas DataFrame

6 Videos

  • 5.2 Working With the Pandas DataFrame
  • Exploring Dataframes
  • Copying and Sorting Dataframes
  • Changing Column and Row Names of Dataframes
  • Grouping and Aggregating With Dataframes
  • Stacking and Pivoting Dataframes

Lesson 5.3: Introduction to Descriptive Statistics

    • 2 Videos
    • 1 Lab
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1 Labs

  • Introduction to Descriptive Statistics

2 Videos

  • 5.3 Introduction to Descriptive Statistics
  • Descriptive Statistics for Dataframes

Module 5 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 5 Programming Assignment Score

1 Labs

  • Module 5 Programming Assignment

1 Videos

  • Module 5 Review

1 Quiz

  • Module 5 Quiz

Module 6 Information

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Module 6 Introduction

2 Readings

  • Module 6 Overview
  • Module 6 Readings

Lesson 6.1: Introduction to Plotting With Python

    • 7 Videos
    • 1 Lab
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1 Labs

  • Introduction to Plotting With Python

7 Videos

  • 6.1 Introduction to Plotting With Python
  • Introduction to Plotting With Pandas
  • More on Plotting With Pandas
  • Introduction to matplotlib
  • More on Plotting With matplotlib
  • Introduction to Plotting With Seaborn
  • Refreshing Folders

Lesson 6.2: Introduction to One-Dimensional Data Visualizations

    • 4 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Introduction to One-Dimensional Data Visualizations

4 Videos

  • 6.2 Introduction to One-Dimensional Data Visualization
  • Introduction to Seaborn Histograms
  • Introduction to Seaborn Box Plots
  • Introduction to Seaborn Bar Plots

Lesson 6.3: Introduction to Two-Dimensional Data

    • 4 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Introduction to Two-Dimensional Data Visualizations

4 Videos

  • 6.3 Introduction to Two-Dimensional Data
  • Introduction to Scatter Plots
  • Introduction to Pair Plots
  • Introduction to Joint Plots

Module 6 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 6 Programming Assignment Score

1 Labs

  • Module 6 Programming Assignment

1 Videos

  • Module 6 Review

1 Quiz

  • Module 6 Quiz

Module 7 Information

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Module 7 Introduction

2 Readings

  • Module 7 Overview
  • Module 7 Readings

Lesson 7.1: Introduction to CRISP-DM

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to CRISP-DM

1 Videos

  • 7.1 Introduction to CRISP-DM

Lesson 7.2: Introduction to Data Preparation Techniques

    • 8 Videos
    • 1 Lab
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1 Labs

  • Introduction to Data Preparation Techniques

8 Videos

  • 7.2 Introduction to Data Preparation Techniques
  • Pandas Functions to Load Data
  • Fill in Missing Values With Conditional Means
  • Manipulating String Columns of a Dataframe
  • Creating Datetime Values
  • Split, Apply Combine and More on Datetimes
  • Lambda Functions
  • Refreshing Folders

Lesson 7.3: Linear Regression in Python

    • 6 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Introduction to Linear Regression

6 Videos

  • 7.3 Linear Regression in Python
  • Setting up Data for Regression
  • Creating a Simple Regression Model
  • Predicting with a Regression Model
  • Multiple Regression Model
  • Categorical Variables in Regression

Module 7 Conclusion

    • 1 Videos
    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 7 Programming Assignment Score

1 Labs

  • Module 7 Programming Assignment

1 Videos

  • Module 7 Review

1 Quiz

  • Module 7 Quiz

Module 8 Information

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Module 8 Introduction

2 Readings

  • Module 8 Overview
  • Module 8 Readings

Lesson 8.1: Introduction to Data Persistence

    • 7 Videos
    • 1 Lab
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1 Labs

  • Introduction to Data Persistence

7 Videos

  • 8.1 Introduction to Data Persistence
  • Introduction to Terminal
  • Creating a SQLite Database From Terminal
  • Creating a SQLite Table From a CSV File
  • Using Dump and Reading in Files to Create Tables
  • Altering Existing SQLite Tables
  • Refreshing Folders

Lesson 8.2: SQL: Advanced Concepts

    • 3 Videos
    • 1 Lab
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1 Labs

  • SQL: Advanced Concepts

3 Videos

  • 8.2 Advanced Concepts
  • Querying Tables with SQL
  • SQL Join Queries

Lesson 8.3: Python Database Programming

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Python Database Programming

3 Videos

  • 8.3 Python Database Programming
  • Querying Relational Database With Python and SQL
  • Exploring Databases and Adding Rows to Tables With Python

Module 8 Conclusion

    • 2 Videos
    • 2 Readings
    • 1 Quiz
    • 1 Plugin
    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Module 8 Programming Assignment Score

1 Labs

  • Module 8 Programming Assignment

2 Videos

  • Module 8 Review
  • Learn on Your Terms

2 Readings

  • Congratulations on completing the course!
  • Get Your Course Certificate

1 Quiz

  • Module 8 Quiz

Auto Summary

Unlock the power of Python for accounting data analysis with this specialized course designed for professionals in Business & Management. Enhance your ability to assemble and analyze business data within the versatile Jupyter Notebook environment, moving beyond the basics covered in introductory courses. Dive deep into financial statement data, stock data, loan data, and more, applying your skills in real-world accounting scenarios. Led by expert instructors from Coursera, this comprehensive program spans approximately 2,580 minutes and offers a rich curriculum divided into two main sections. The first part will advance your knowledge of Python programming, focusing on automating data analytic tasks, mastering the use of mathematical operators, functions, conditional statements, and loops. The latter half transitions into preparing data for machine learning applications. Here, you'll gain proficiency in Pandas dataframes and Numpy for efficient data manipulation, create insightful visualizations, perform linear regression analysis, and interface Python with SQL databases. Flexible subscription options, including Starter and Professional plans, make this course accessible for those seeking to elevate their data analytics capabilities. Ideal for professionals aiming to integrate advanced Python skills into their accounting toolkit, this course promises to transform your approach to data-driven decision-making in business.

Ronald Guymon
Instructors

Ronald Guymon

Linden Lu
Instructors

Linden Lu