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.

Traffic lights

Read about what's good

what should give you pause

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

Python for accounting data analysis

According to learners, this course offers a valuable introduction to using Python for accounting data analytics. Students appreciate the practical focus on relevant accounting tasks and the use of key libraries like Pandas and NumPy. While many find the material clear and well-structured, particularly the hands-on exercises and assignments, some note that the pace can be challenging for those entirely new to programming. The course is widely considered a strong foundational step for accounting professionals looking to gain data analytics skills, although a few reviewers mention that more advanced topics could be covered in greater depth.

Fast-paced; prior coding helps but isn't required.

"It's quite fast if you are a complete beginner to Python."

"Having some basic programming knowledge definitely helps with the pace."

"Course moves quickly, covers a lot in a short time."

Provides a solid base in Python and data tools.

"Gave me a solid foundation in using Python for data handling."

"Covers the essential libraries like NumPy and Pandas effectively."

"Excellent starting point for accounting professionals in data analytics."

Exercises and labs reinforce concepts effectively.

"The practical exercises solidified my understanding of Pandas."

"Labs are well-designed and help you apply what you learn."

"Assignments are relevant and push you to use the tools."

Applies Python directly to accounting tasks.

"I can immediately see how to use Python for real accounting problems."

"The course relates Python very well to financial and accounting data."

"Finally, a course that shows the practical use of Python in my field."

Good intro, but lacks depth in advanced topics.

"Could use more detailed examples or challenges on complex data sets."

"I was hoping for more advanced analytical methods using Python."

"Felt like it just scratched the surface on some topics like SQL."

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

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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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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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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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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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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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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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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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.

See salaries and explore the career path for Data Engineer