Accounting Data Analytics with Python
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Accounting Data Analytics with Python
This course is part of Accounting Data Analytics Specialization
Instructors: Ronald Guymon
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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
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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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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
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Reviewed on Mar 27, 2022
very useful and important to computer science engineering students
Reviewed on Nov 30, 2020
It is very easy to learn and also very interesting because you can modify and try other things.
Reviewed on Jul 2, 2020
Great program for beginners in python programming
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