Explore

Accounting Data Analytics with Python

Approx. 32 hours to complete

Save Course

Go to Course

Course Summary

Learn how to use Python to analyze financial data and create financial models. This course covers topics such as financial statements, ratios, forecasting, and more.

Key Learning Points

Use Python to analyze financial data and create financial models
Learn about financial statements and ratios
Understand how to forecast financial data

Job Positions & Salaries of people who have taken this course might have

Financial Analyst
- USA: $63,000
- India: ₹473,000
- Spain: €34,000
Data Analyst
- USA: $62,000
- India: ₹437,000
- Spain: €29,000
Business Analyst
- USA: $71,000
- India: ₹652,000
- Spain: €36,000

Learning Outcomes

Ability to use Python to analyze financial data
Understanding of financial statements and ratios
Ability to create financial models and forecast financial data

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Basic understanding of financial statements and ratios

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Data Analysis and Visualization with Excel
Python for Data Science
Introduction to Financial Accounting

Notable People in This Field

Founder and CEO of Citadel LLC
CEO of Berkshire Hathaway

Related Books

Description

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.

Knowledge

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.

Outline

INTRODUCTION TO THE COURSE
Course Introduction
About Ronald Guymon
About Linden Lu
Learn on Your Terms
Syllabus
Glossary
About the Discussion Forums
Learn More About Flexible Learning Paths
Update Your Profile

MODULE 1: FOUNDATIONS
Module 1 Introduction
1.1 Introduction to Data Analytics
1.2 Jupyter Notebook
1.3 Introduction to Markdown
Module 1 Review
Module 1 Overview and Resources
Lesson 1.1 Readings
Module 1 Quiz

MODULE 2: INTRODUCTION TO PYTHON
Module 2 Introduction
2.1 Introduction to Python
2.2 Introduction to Python Functions
2.3 Conditional Statements in Python
Module 2 Review
Module 2 Overview and Resources
Module 2 Quiz

MODULE 3: INTRODUCTION TO PYTHON PROGRAMMING
Module 3 Introduction
3.1 Introduction to Python Data Structures
3.2 Working with Python Data Structure
3.3 Introduction to Python Loops
Module 3 Review
Module 3 Overview and Resources
Module 3 Quiz

MODULE 4: PYTHON PROGRAMMING
Module 4 Introduction
4.1 Writing Python Programs
4.2 Introduction to NumPy
4.3 Introduction to Pandas
Module 4 Review
Module 4 Overview and Resources
Module 4 Quiz

MODULE 5: DATA ANALYSIS WITH PYTHON
Module 5 Introduction
5.1 Python File IO
5.2 Working with the Pandas DataFrame
5.3 Introduction to Descriptive Statistics
Module 5 Review
Module 5 Overview and Resources
Module 5 Quiz

MODULE 6: INTRODUCTION TO VISUALIZATION IN PYTHON
Module 6 Introduction
6.1 Introduction to Plotting with Python
6.2 Introduction to One-Dimensional Data Visualization
6.3 Introduction to Two-Dimensional Data
Module 6 Review
Module 6 Overview and Resources
Module 6 Quiz

MODULE 7: PRODUCTION DATA ANALYTICS
Module 7 Introduction
7.1 Introduction to CRISP-DM
7.2 Introduction to Data Preparation Techniques
7.3 Linear Regression in Python
Module 7 Review
Module 7 Overview and Resources
Module 7 Quiz

MODULE 8: INTRODUCTION TO DATABASES IN PYTHON
Module 8 Introduction
8.1 Introduction to Data Persistence
8.2 Advanced Concepts
8.3 Python Database Programming
Module 8 Review
Gies Online Programs
Module 8 Overview and Resources
Congratulations!
Get Your Course Certificate
Module 8 Quiz

Summary of User Reviews

Discover the power of Python for accounting data analytics in this course on Coursera. Students praise the course for its comprehensive coverage and practical approach to teaching accounting data analytics. Overall, the course has received positive reviews from users.

Key Aspect Users Liked About This Course

Many users thought that the course was very practical and hands-on, which helped them to apply what they learned in a real-world setting.

Pros from User Reviews

The course provides a good introduction to Python programming for accounting professionals.
The instructor is knowledgeable and provides clear explanations.
The course covers a wide range of topics related to accounting data analytics, including data visualization and data cleaning.
The course provides ample opportunities for students to practice what they have learned through exercises and assignments.

Cons from User Reviews

Some users found the course material to be too basic, especially if they already had experience with Python programming.
The course may not be suitable for those who are looking for an in-depth exploration of accounting data analytics.
Some users found the course to be too theoretical and would have preferred more practical examples.
Some users experienced technical issues while taking the course, such as problems with the online platform or difficulty accessing course materials.

Recommended for you

Python: Build Machine Learning Models in 6 Hours

A complete & comprehensive course in which you will create machine learning models with ease! Python comes to the rescue to craft better solutions and process them effectively....

Save Course

Deep Learning Regression with Python

Learn deep learning regression from basic to expert level through a practical course with Python programming language.   Become a Deep Learning Regression Expert in this Practical Course with Python...

Save Course

Customer lifetime value predictive model with Python

A very useful marketing AI model course that enables you to master machine learning and application into business The method is also sometimes called the 'repurchase modeling'....

Save Course

Python for Data Science Project

This mini-course is intended for you to demonstrate foundational Python skills for working with data. Enroll to learn more, complete the course and claim your badge!...

Save Course