Accounting Data Analytics with Python

Accounting Data Analytics with Python

This course is part of Accounting Data Analytics Specialization

Instructors: Ronald Guymon

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14,976 already enrolled

8 modules

Gain insight into a topic and learn the fundamentals.

112 reviews

Intermediate level

Some related experience required

Flexible schedule

4 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

8 modules

Gain insight into a topic and learn the fundamentals.

112 reviews

Intermediate level

Some related experience required

Flexible schedule

4 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

What you'll learn

Know how to operate software that will help you create and run Python code.
Execute Python code for wrangling data from different structures into a Pandas dataframe structure.
Run and interpret fundamental data analytic tasks in Python including descriptive statistics, data visualizations, and regression.
Use relational databases and know how to manipulate such databases directly through the command line, and indirectly through a Python script.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

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This course is part of the Accounting Data Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 8 modules in this course

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.

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. 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.

What's included

19 videos9 readings1 assignment1 programming assignment2 discussion prompts3 ungraded labs1 plugin

19 videos Total 102 minutes

Course Introduction 5 minutes
About Ronald Guymon 4 minutes
About Linden Lu 4 minutes
Module 1 Introduction 3 minutes
1.1 Introduction to Data Analytics 2 minutes
1.2 Jupyter Notebook 5 minutes
Python and Integrated Development Environments (IDEs) 7 minutes
Installing Python Using JupyterLab Desktop (Recommended for Windows and Mac Users) 6 minutes
Installing Python Using Homebrew and Pyenv (For advanced Mac users) 7 minutes
Installing Python from Python.org for Windows (For advanced Windows users) 5 minutes
Navigating Jupyter Notebook 6 minutes
Navigating JupyterLab 6 minutes
Using Notebook Files 9 minutes
Navigating Spyder 8 minutes
Comparison of Jupyter Notebook, JupyterLab, and Spyder 4 minutes
Refreshing Folders 3 minutes
1.3 Introduction to Markdown 3 minutes
Markdown Basics 10 minutes
Module 1 Review 7 minutes

9 readings Total 93 minutes

Syllabus 10 minutes
Glossary 10 minutes
About the Discussion Forums 10 minutes
Online Education at Gies College of Business 10 minutes
Update Your Profile 10 minutes
Module 1 Overview 3 minutes
Module 1 Readings 10 minutes
Lesson 1.1 Readings 20 minutes
Installing Python and JupyterLab: Start Here 10 minutes

1 assignment Total 20 minutes

Module 1 Quiz 20 minutes

1 programming assignment Total 3 minutes

Module 1 Programming Assignment Score 3 minutes

2 discussion prompts Total 13 minutes

Get to Know Your Fellow Learners 10 minutes
Make Connections to Topic 3 minutes

3 ungraded labs Total 85 minutes

Introduction to Jupyter Notebook 45 minutes
Introduction to Markdown 30 minutes
Module 1 Programming Assignment 10 minutes

1 plugin Total 15 minutes

Demographics Survey 15 minutes

This module focuses on the basic features in the Python programming language that underlie most data analytics programs (or scripts). First, you will read about why accounting students should learn to write computer programs. In the first lesson, you will also learn the basic concepts of the Python programming language, including how to create variables, basic data types, and mathematical operators, and how to document your programs with comments. Next, you will learn about Boolean and logical operators in Python and how they can be used to control the flow of a Python program by using conditional statements. Finally, you will learn about functions and how they can simplify developing and maintaining programs. You will also learn how to create and call functions in Python.

What's included

13 videos2 readings1 assignment1 programming assignment4 ungraded labs

13 videos Total 68 minutes

Module 2 Introduction 8 minutes
2.1 Introduction to Python 4 minutes
Python Code Basics 6 minutes
Variables, Data Types, and Operators 8 minutes
2.2 Introduction to Python Functions 3 minutes
Built-In Functions 6 minutes
User-Defined Functions 10 minutes
Functions vs Methods 4 minutes
Refreshing Folders 3 minutes
2.3 Conditional Statements in Python 3 minutes
Comparison and Logical Operators 3 minutes
Working With Conditional Statements 5 minutes
Module 2 Review 5 minutes

2 readings Total 25 minutes

Module 2 Overview 15 minutes
Module 2 Readings 10 minutes

1 assignment Total 15 minutes

Module 2 Quiz 15 minutes

1 programming assignment Total 1 minute

Module 2 Programming Assignment Score 1 minute

4 ungraded labs Total 150 minutes

Introduction to Python 45 minutes
Introduction to Python Functions 45 minutes
Python Conditional Statements 30 minutes
Module 2 Programming Assignment 30 minutes

In this module you will learn about working with fundamental data structures in Python: strings, tuples, lists, and dictionaries. You will also learn about how to write loops for performing repetitive tasks.

What's included

16 videos2 readings1 assignment1 programming assignment4 ungraded labs

16 videos Total 87 minutes

Module 3 Introduction 2 minutes
3.1 Introduction to Python Data Structures 4 minutes
Introduction to Strings 6 minutes
Introduction to Lists 5 minutes
Introduction to Dictionaries, Tuples, and Unpacking 7 minutes
Common Sequence Operations 8 minutes
Refreshing Folders 3 minutes
3.2 Working With Python Data Structure 3 minutes
Working With Strings 9 minutes
Working With Lists and Tuples 8 minutes
Working With Dictionaries 4 minutes
3.3 Introduction to Python Loops 3 minutes
The For Loop 13 minutes
The While Loop 3 minutes
Comprehensions 4 minutes
Module 3 Review 5 minutes

2 readings Total 13 minutes

Module 3 Overview 3 minutes
Module 3 Readings 10 minutes

1 assignment Total 10 minutes

Module 3 Quiz 10 minutes

1 programming assignment Total 1 minute

Module 3 Programming Assignment Score 1 minute

4 ungraded labs Total 210 minutes

Introduction to Python Data Structures 60 minutes
Working With Python Data Structures 60 minutes
Introduction to Python Loops 60 minutes
Module 3 Programming Assignment 30 minutes

In this module you will learn about creating and using modules, which is a group of functions. You will then learn about two of the most important modules for data analytics: NumPy and Pandas. NumPy performs numerical calculations on large data arrays. Pandas simplifies procedures for working with panel data, also known as dataframes.

What's included

13 videos2 readings1 assignment1 programming assignment4 ungraded labs

13 videos Total 77 minutes

Module 4 Introduction 6 minutes
4.1 Writing Python Programs 3 minutes
Python Modules 8 minutes
Errors and Exceptions 9 minutes
4.2 Introduction to NumPy 2 minutes
NumPy Array 6 minutes
NumPy Basic Functions 8 minutes
Refreshing Folders 3 minutes
4.3 Introduction to Pandas 2 minutes
Introduction to Dataframes 8 minutes
Data Selection With Dataframes 10 minutes
Missing Values and Copies With Dataframes 9 minutes
Module 4 Review 3 minutes

2 readings Total 13 minutes

Module 4 Overview 3 minutes
Module 4 Readings 10 minutes

1 assignment Total 20 minutes

Module 4 Quiz 20 minutes

1 programming assignment Total 1 minute

Module 4 Programming Assignment Score 1 minute

4 ungraded labs Total 195 minutes

Writing Python Programs 45 minutes
Introduction to NumPy 60 minutes
Introduction to Pandas 60 minutes
Module 4 Programming Assignment 30 minutes

This module focuses on using the Pandas dataframe to do some fundamental dataframe tasks including saving and reading dataframes, pivot table functions, filtering functions, and calculating descriptive statistics.

What's included

15 videos2 readings1 assignment1 programming assignment4 ungraded labs

15 videos Total 96 minutes

Module 5 Introduction 3 minutes
5.1 Python File IO 3 minutes
Reading and Writing Files With Base Python 9 minutes
Reading and Writing Files With Pandas 9 minutes
Preserving Data Types With Pickling 6 minutes
Refreshing Folders 3 minutes
5.2 Working With the Pandas DataFrame 2 minutes
Exploring Dataframes 9 minutes
Copying and Sorting Dataframes 8 minutes
Changing Column and Row Names of Dataframes 6 minutes
Grouping and Aggregating With Dataframes 6 minutes
Stacking and Pivoting Dataframes 11 minutes
5.3 Introduction to Descriptive Statistics 2 minutes
Descriptive Statistics for Dataframes 13 minutes
Module 5 Review 7 minutes

2 readings Total 13 minutes

Module 5 Overview 3 minutes
Module 5 Readings 10 minutes

1 assignment Total 30 minutes

Module 5 Quiz 30 minutes

1 programming assignment Total 1 minute

Module 5 Programming Assignment Score 1 minute

4 ungraded labs Total 195 minutes

Python File Input/Output 45 minutes
Working With the Pandas DataFrame 60 minutes
Introduction to Descriptive Statistics 45 minutes
Module 5 Programming Assignment 45 minutes

In this module you will learn some basic elements of creating data visualizations in Python. You will then learn how to use the Matplotlib and Seaborn modules to help create some of the most commonly used one- and two-dimensional data visualizations.

What's included

17 videos2 readings1 assignment1 programming assignment4 ungraded labs

17 videos Total 90 minutes

Module 6 Introduction 3 minutes
6.1 Introduction to Plotting With Python 2 minutes
Introduction to Plotting With Pandas 10 minutes
More on Plotting With Pandas 5 minutes
Introduction to matplotlib 9 minutes
More on Plotting With matplotlib 6 minutes
Introduction to Plotting With Seaborn 5 minutes
Refreshing Folders 3 minutes
6.2 Introduction to One-Dimensional Data Visualization 2 minutes
Introduction to Seaborn Histograms 6 minutes
Introduction to Seaborn Box Plots 6 minutes
Introduction to Seaborn Bar Plots 5 minutes
6.3 Introduction to Two-Dimensional Data 3 minutes
Introduction to Scatter Plots 7 minutes
Introduction to Pair Plots 5 minutes
Introduction to Joint Plots 6 minutes
Module 6 Review 8 minutes

2 readings Total 13 minutes

Module 6 Overview 3 minutes
Module 6 Readings 10 minutes

1 assignment Total 15 minutes

Module 6 Quiz 15 minutes

1 programming assignment Total 1 minute

Module 6 Programming Assignment Score 1 minute

4 ungraded labs Total 240 minutes

Introduction to Plotting With Python 60 minutes
Introduction to One-Dimensional Data Visualizations 60 minutes
Introduction to Two-Dimensional Data Visualizations 60 minutes
Module 6 Programming Assignment 60 minutes

In this module you'll learn about the CRISP decision making framework to approach real-world problems. You'll also learn how to use linear regression to find and quantify relationships.

What's included

17 videos2 readings1 assignment1 programming assignment4 ungraded labs

17 videos Total 86 minutes

Module 7 Introduction 4 minutes
7.1 Introduction to CRISP-DM 3 minutes
7.2 Introduction to Data Preparation Techniques 3 minutes
Pandas Functions to Load Data 7 minutes
Fill in Missing Values With Conditional Means 8 minutes
Manipulating String Columns of a Dataframe 8 minutes
Creating Datetime Values 5 minutes
Split, Apply Combine and More on Datetimes 6 minutes
Lambda Functions 6 minutes
Refreshing Folders 3 minutes
7.3 Linear Regression in Python 3 minutes
Setting up Data for Regression 5 minutes
Creating a Simple Regression Model 8 minutes
Predicting with a Regression Model 5 minutes
Multiple Regression Model 7 minutes
Categorical Variables in Regression 3 minutes
Module 7 Review 3 minutes

2 readings Total 13 minutes

Module 7 Overview 3 minutes
Module 7 Readings 10 minutes

1 assignment Total 30 minutes

Module 7 Quiz 30 minutes

1 programming assignment Total 1 minute

Module 7 Programming Assignment Score 1 minute

4 ungraded labs Total 155 minutes

Introduction to CRISP-DM 20 minutes
Introduction to Data Preparation Techniques 45 minutes
Introduction to Linear Regression 60 minutes
Module 7 Programming Assignment 30 minutes

This module focuses on relational database management systems (RDBMS) and how to interact with those using Python.

What's included

16 videos4 readings1 assignment1 programming assignment4 ungraded labs1 plugin

16 videos Total 85 minutes

Module 8 Introduction 4 minutes
8.1 Introduction to Data Persistence 5 minutes
Introduction to Terminal 8 minutes
Creating a SQLite Database From Terminal 8 minutes
Creating a SQLite Table From a CSV File 5 minutes
Using Dump and Reading in Files to Create Tables 8 minutes
Altering Existing SQLite Tables 5 minutes
Refreshing Folders 3 minutes
8.2 Advanced Concepts 2 minutes
Querying Tables with SQL 7 minutes
SQL Join Queries 6 minutes
8.3 Python Database Programming 3 minutes
Querying Relational Database With Python and SQL 7 minutes
Exploring Databases and Adding Rows to Tables With Python 9 minutes
Module 8 Review 4 minutes
Learn on Your Terms 1 minute

4 readings Total 33 minutes

Module 8 Overview 3 minutes
Module 8 Readings 10 minutes
Congratulations on completing the course! 10 minutes
Get Your Course Certificate 10 minutes

1 assignment Total 30 minutes

Module 8 Quiz 30 minutes

1 programming assignment Total 1 minute

Module 8 Programming Assignment Score 1 minute

4 ungraded labs Total 230 minutes

Introduction to Data Persistence 60 minutes
SQL: Advanced Concepts 50 minutes
Python Database Programming 60 minutes
Module 8 Programming Assignment 60 minutes

1 plugin Total 15 minutes

Course-End Survey 15 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹