University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
University of Illinois at Urbana-Champaign

Accounting Data Analytics with Python

This course is part of Accounting Data Analytics Specialization

Taught in English

Some content may not be translated

Ronald Guymon
Linden Lu

Instructors: Ronald Guymon

12,238 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.3

(84 reviews)

Intermediate level
Some related experience required
42 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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.

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.3

(84 reviews)

Intermediate level
Some related experience required
42 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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This course is part of the Accounting Data Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 9 modules in this 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.

What's included

3 videos5 readings1 discussion prompt1 plugin

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

15 videos3 readings1 quiz1 programming assignment1 discussion prompt3 ungraded labs

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 quiz1 programming assignment4 ungraded labs

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 quiz1 programming assignment4 ungraded labs

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 quiz1 programming assignment4 ungraded labs

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 quiz1 programming assignment4 ungraded labs

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 quiz1 programming assignment4 ungraded labs

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 quiz1 programming assignment4 ungraded labs

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

What's included

16 videos4 readings1 quiz1 programming assignment4 ungraded labs1 plugin

Instructors

Instructor ratings
4.2 (26 ratings)
Ronald Guymon
University of Illinois at Urbana-Champaign
5 Courses68,778 learners
Linden Lu
University of Illinois at Urbana-Champaign
3 Courses18,029 learners

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