Accounting Data Analytics with Python from Coursera

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.

What's inside

Syllabus

Introduction to the Course

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.

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.

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.

Module 3: Introduction to Python Programming

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.

Module 4: Python Programming

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.

Module 5: Data Analysis With Python

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.

Module 6: Introduction to Visualization in Python

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.

Module 7: Production Data Analytics

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.

Module 8: Introduction to Databases in Python

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides practical applications of accounting data analytics

Teaches relevant programming tools and techniques for accounting data analytics

Strong foundation in data structuring and manipulation using Python

Covers fundamentals of Python programming and its applications in data analytics

Shows the use of Python libraries NumPy and Pandas for numerical computations and data handling

Reviews summary

Introductory python course with accounting applications

According to students, Accounting Data Analytics with Python is a beginner-friendly course that introduces learners to Python programming for data analytics. While its focus is on basic programming, it also touches on advanced concepts. Reviews praise the informative materials and helpful tutorials, especially those by Lecturer Ronald Guymon. However, some learners criticize the lack of in-depth explanations and hands-on exercises.

Suitable for learners new to Python and data analytics.

"Great beginner's course for python!"

"Very recommended to learn analytics with python"

"A great course for someone who is just starting with Python."

Course materials are well-designed and provide a solid foundation.

"The materials are well designed."

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

"Really Nice course but you will not explain all the module very indepth."

Video tutorials, especially those by Lecturer Ronald Guymon, are highly praised.

"Great tutorials by Lecturer Ronald Guymon."

"I highly recommend."

Some learners find the explanations and exercises to be insufficiently detailed.

"not near detailed enough to feel comfortable doing the exercises."

"Many of the Jupyter notebook sections were simply a lot of reading, no chances for the students to get some hands-on experience with actual coding."

Learners express a desire for more practical exercises and opportunities to apply their knowledge.

"lack of instruction plagued too many the exercises."

"This could be an outstanding course if more time was spent on going over more of the content in greater detail and building out the exercises."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Accounting Data Analytics with Python with these activities:

Review Python fundamentals

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This activity helps strengthen your foundational knowledge of Python before starting the course.

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Review Python data types, variables, and operators.
Practice writing simple Python scripts.

Install Python and its dependencies

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This activity ensures that your system is properly set up for the course.

Browse courses on Package Management

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Download and install Python and Jupyter notebook.
Install necessary packages for data analysis, such as NumPy, Pandas, and Matplotlib.

Create a Python notebook

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This activity allows you to practice Python programming, explore data, and create interactive visualizations.

Browse courses on Jupyter Notebook

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Create a Jupyter notebook on the course platform or your local machine.
Write Python code to import data, perform analysis, and generate visualizations.

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Career center

Learners who complete Accounting Data Analytics with Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst collects, analyzes, interprets, and presents data to help organizations make informed decisions. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst helps businesses improve their operations by analyzing data and identifying opportunities for improvement. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Business Analyst.

See salaries and explore the career path for Business Analyst

Market Researcher

A Market Researcher conducts surveys, analyzes data, and writes reports to help businesses understand their customers and make informed decisions. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Market Researcher.

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Financial Analyst

A Financial Analyst evaluates the performance of companies and makes recommendations on how to invest money. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Financial Analyst.

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Data Scientist

A Data Scientist uses statistical methods and machine learning to analyze data and make predictions. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Data Scientist.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Software Engineer.

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Statistician

A Statistician collects, analyzes, interprets, and presents data. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Statistician.

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Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical methods to improve the efficiency of operations. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as an Operations Research Analyst.

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Actuary

An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as an Actuary.

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Risk Analyst

A Risk Analyst evaluates the risks that a company faces and makes recommendations on how to mitigate those risks. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Risk Analyst.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical methods to analyze financial data. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Quantitative Analyst.

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Insurance Analyst

An Insurance Analyst evaluates the risks that insurance companies face and makes recommendations on how to price insurance policies. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as an Insurance Analyst.

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Healthcare Analyst

A Healthcare Analyst evaluates the performance of healthcare providers and makes recommendations on how to improve the quality of care. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as a Healthcare Analyst.

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Economist

An Economist studies the production, distribution, and consumption of goods and services. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course can help students develop the skills they need to succeed as an Economist.

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Data Engineer

A Data Engineer designs, builds, and maintains data pipelines. This course introduces students to Python, a popular programming language for data analysis. Students will learn how to use Python to automate data analytic tasks, manipulate data, and create visualizations. This course may help students develop the skills they need to succeed as a Data Engineer.

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