This course is part of the Accounting Data Analytics Specialization

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75 ratings

Ronald Guymon +1 more instructor

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Part of the Master of Science in Accountancy (iMSA) degree
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Accounting Data Analytics SpecializationUniversity of Illinois at Urbana-Champaign

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Course 2 of 4 in the

Accounting Data Analytics Specialization

Intermediate Level

Approx. 41 hours to complete

English

What you will 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 will gain

Python Programming
Data Visualization (DataViz)
Linear Regression
SQL
Data Preparation

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

Accounting Data Analytics Specialization

Intermediate Level

Approx. 41 hours to complete

English

Instructors

Instructor rating

4.15/5 (24 Ratings)

Ronald Guymon

Senior Lecturer of Accountancy

Accountancy

52,702 Learners

5 Courses

Linden Lu

Instructor

Department of Accountancy

15,637 Learners

3 Courses

Offered by

University of Illinois at Urbana-Champaign

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Accountancy (iMSA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

1 hour to complete

INTRODUCTION TO THE COURSE

1 hour to complete

4 videos (Total 12 min), 5 readings

4 hours to complete

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.

4 hours to complete

14 videos (Total 84 min), 2 readings, 2 quizzes

14 videos

Module 1 Introduction2m1.1 Introduction to Data Analytics2m1.2 Jupyter Notebook4mPython and Integrated Development Environments (IDEs)6mInstalling and Running Python Using the Anaconda Distribution4mInstalling and Running Python Without Anaconda9mNavigating Jupyter Notebook5mNavigating JupyterLab5mUsing Notebook Files9mNavigating Spyder8mComparison of Jupyter Notebook, JupyterLab, and Spyder3m1.3 Introduction to Markdown3mMarkdown Basics9mModule 1 Review6m

2 readings

Module 1 Overview and Resources3mLesson 1.1 Readings20m

1 practice exercise

Module 1 Quiz20m

Week2

Week 2

4 hours to complete

MODULE 2: INTRODUCTION TO PYTHON

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.

4 hours to complete

12 videos (Total 66 min), 1 reading, 2 quizzes

12 videos

Module 2 Introduction7m2.1 Introduction to Python3mPython Code Basics6mVariables, Data Types, and Operators8m2.2 Introduction to Python Functions2mBuilt-In Functions6mUser-Defined Functions9mFunctions vs Methods4m2.3 Conditional Statements in Python2mComparison and Logical Operators3mWorking with Conditional Statements5mModule 2 Review4m

1 reading

Module 2 Overview and Resources15m

1 practice exercise

Module 2 Quiz15m

Week3

Week 3

5 hours to complete

MODULE 3: INTRODUCTION TO PYTHON PROGRAMMING

5 hours to complete

15 videos (Total 84 min), 1 reading, 2 quizzes

15 videos

Module 3 Introduction2m3.1 Introduction to Python Data Structures4mIntroduction to Strings6mIntroduction to Lists5mIntroduction to Dictionaries, Tuples, and Unpacking7mCommon Sequence Operations7m3.2 Working with Python Data Structure2mWorking with Strings9mWorking with Lists and Tuples7mWorking with Dictionaries3m3.3 Introduction to Python Loops2mThe For Loop13mThe While Loop2mComprehensions3mModule 3 Review4m

1 reading

Module 3 Overview and Resources3m

1 practice exercise

Module 3 Quiz10m

Week4

Week 4

5 hours to complete

MODULE 4: PYTHON PROGRAMMING

5 hours to complete

12 videos (Total 74 min), 1 reading, 2 quizzes

Week5

Week 5

5 hours to complete

MODULE 5: DATA ANALYSIS WITH PYTHON

5 hours to complete

14 videos (Total 93 min), 1 reading, 2 quizzes

14 videos

Module 5 Introduction3m5.1 Python File IO3mReading and Writing Files with Base Python9mReading and Writing Files with Pandas8mPreserving Data Types with Pickling5m5.2 Working with the Pandas DataFrame1mExploring Dataframes8mCopying and Sorting Dataframes7mChanging Column and Row Names of Dataframes6mGrouping and Aggregating with Dataframes6mStacking and Pivoting Dataframes10m5.3 Introduction to Descriptive Statistics1mDescriptive Statistics for Dataframes13mModule 5 Review7m

1 reading

Module 5 Overview and Resources3m

1 practice exercise

Module 5 Quiz30m

Week6

Week 6

6 hours to complete

MODULE 6: INTRODUCTION TO VISUALIZATION IN PYTHON

6 hours to complete

16 videos (Total 88 min), 1 reading, 2 quizzes

16 videos

Module 6 Introduction3m6.1 Introduction to Plotting with Python2mIntroduction to Plotting with Pandas9mMore on Plotting with Pandas5mIntroduction to matplotlib9mMore on Plotting with matplotlib5mIntroduction to Plotting with Seaborn5m6.2 Introduction to One-Dimensional Data Visualization2mIntroduction to Seaborn Histograms5mIntroduction to Seaborn Box Plots5mIntroduction to Seaborn Bar Plots4m6.3 Introduction to Two-Dimensional Data3mIntroduction to Scatter Plots7mIntroduction to Pair Plots5mIntroduction to Joint Plots5mModule 6 Review7m

1 reading

Module 6 Overview and Resources3m

1 practice exercise

Module 6 Quiz15m

Week7

Week 7

5 hours to complete

MODULE 7: PRODUCTION DATA ANALYTICS

5 hours to complete

16 videos (Total 84 min), 1 reading, 2 quizzes

16 videos

Module 7 Introduction3m7.1 Introduction to CRISP-DM3m7.2 Introduction to Data Preparation Techniques2mPandas Functions to Load Data7mFill in Missing Values with Conditional Means8mManipulating String Columns of a Dataframe7mCreating Datetime Values4mSplit, Apply Combine and More on Datetimes6mLambda Functions6m7.3 Linear Regression in Python2mSetting up Data for Regression5mCreating a Simple Regression Model7mPredicting with a Regression Model5mMultiple Regression Model6mCategorical Variables in Regression3mModule 7 Review2m

1 reading

Module 7 Overview and Resources3m

1 practice exercise

Module 7 Quiz30m

Week8

Week 8

6 hours to complete

MODULE 8: INTRODUCTION TO DATABASES IN PYTHON

6 hours to complete

15 videos (Total 86 min), 3 readings, 2 quizzes

15 videos

Module 8 Introduction3m8.1 Introduction to Data Persistence4mIntroduction to Terminal8mCreating a SQLite Database From Terminal8mCreating a SQLite Table From a CSV File5mUsing Dump and Reading in Files to Create Tables8mAltering Existing SQLite Tables4m8.2 Advanced Concepts2mQuerying Tables with SQL6mSQL Join Queries5m8.3 Python Database Programming3mQuerying Relational Database with Python and SQL7mExploring Databases and Adding Rows to Tables with Python8mModule 8 Review4mGies Online Programs3m

3 readings

Module 8 Overview and Resources3mCongratulations!10mGet Your Course Certificate10m

1 practice exercise

Module 8 Quiz30m

Reviews

4.3

24 reviews

5 stars
57.33%
4 stars
25.33%
3 stars
6.66%
2 stars
8%
1 star
2.66%

TOP REVIEWS FROM ACCOUNTING DATA ANALYTICS WITH PYTHON

by SAMar 27, 2022

very useful and important to computer science engineering students

by SMJun 27, 2020

Really Nice course but you will not explain all the module very indepth. All the module are basic and easy to learn. I am very happy to completed this course.

by MFMar 2, 2023

Great introductory Python course with alot of knowledge that can be applied to your every day job.

by EGNov 30, 2020

It is very easy to learn and also very interesting because you can modify and try other things.

View all reviews

About the Accounting Data Analytics Specialization

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Accounting Data Analytics with Python

About this Course

What you will learn

Skills you will gain