Universidad de Illinois en Urbana-Champaign

Data Analytics Foundations for Accountancy I

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Welcome to Data Analytics Foundations for Accountancy I! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

De la lección

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. Finally, you will begin learning about Python, the programming language used in this course for data analytics.

Introduction to Module 13:10

Conoce a los instructores

Robert Brunner
Professor
Accountancy

Welcome to module one.

As you can see I'm standing here on the field of Memorial Stadium.

Right behind me is one of the end zones and particularly the south end zone.

If there was actually a game going on this would be a dangerous place to stand.

This first module provides the foundation for

both this course and all of the other courses in the data analytics sequence.

First, you will be introduced into how data analytics is being used in accounting.

The modern world of accounting is changing rapidly.

Data is everywhere and that data is constantly changing.

In many ways, the task of an accountant is to use data to answer questions.

With more data, this task becomes increasingly difficult.

The data analytics track will prepare you to use data effectively

today but also prepare you for an uncertain future.

Next, you will learn how to effectively interact with the core server.

This core server is a new technology known as Jupyter server.

By using this server you avoid having to install software on your laptop,

which for corporate clients may not even be possible.

With this server, you can analyze large data remotely via a web browser.

This means you can use a shared computer such as at a library, a tablet,

or even a smartphone to analyze data and you will analyze this data inside a notebook.

The notebook interface is powerful because it allows you to

collect everything together into a coherent story.

You also can run code inside a notebook to analyze data or to make visualizations.

The notebook allows you to document your workflow in a concise manner and it

also allows you to share data which reduces data downloads and compatibility issues.

And lastly, you'll be able to share your results with others by exporting the notebook.

You can do this either as a notebook which can be run elsewhere,

as a PDF that can be shared,

or even as a web page.

Thus, your primary task in this module is to become familiar with the server.

You need to learn how to navigate on the server

to be able to move through directories and access files.

You need to learn to open, run,

and modify, and even save notebooks.

You should learn to create new notebooks and

you should learn to write descriptive text by using markdown.

Finally, you're going to learn to use Python

which is a powerful programming language as a basic calculator.

Other modules will build on this content,

in particular, that for the Python programming language.

Be sure to stay on top of this content and ask questions as they arise.

I hope you're excited to get started.

This is the first step in your journey to becoming a data analyst. Good luck.