Data Analytics Foundations for Accountancy I
- 4.3
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Approx. 66 hours to complete
Course Summary
Learn the fundamentals of data analytics in accountancy, including data visualization, data modeling, and data-driven decision making.Key Learning Points
- Understand the role of data analytics in accountancy
- Learn data visualization techniques to effectively communicate insights
- Develop data modeling skills to analyze and interpret data
- Apply data-driven decision making to solve real-world accounting problems
Related Topics for further study
Learning Outcomes
- Develop proficiency in data analytics in accountancy
- Apply data-driven decision making to accounting problems
- Effectively communicate insights through data visualization
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of accounting principles
- Familiarity with Excel
Course Difficulty Level
BeginnerCourse Format
- Self-paced
- Online
- Video lectures
- Interactive exercises
Similar Courses
- Data Analytics for Finance
- Data Visualization with Tableau
Related Education Paths
Related Books
Description
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.
Outline
- Course Orientation
- Welcome to Data Analytics Foundations for Accountancy I
- Meet Professor Brunner
- Syllabus
- About the Discussion Forums
- Updating Your Profile
- Social Media
- Orientation Quiz
- Module 1: Foundations
- Introduction to Module 1
- The Importance of Data Analytics in Modern Accountancy
- Introduction to the Course JupyterHub Server
- Introduction to Markdown
- Introduction to Python
- Module 1 Overview
- Lesson 1-1 Readings
- Module 1 Graded Quiz
- Module 2: Introduction to Python
- Introduction to Module 2
- Why Accounting Students Should Learn to Code
- Python Data Structures
- Introduction to Python Functions
- Python Programming Concepts
- Module 2 Overview
- Lesson 2-1 Readings
- Module 2 Graded Quiz
- Module 3: Introduction to Data Analysis
- Introduction to Module 3
- Why Use Python Instead of Excel?
- Introduction to Unix
- Python File I/O
- Introduction to Pandas
- Module 3 Overview
- Lesson 3-1 Readings
- Module 3 Graded Quiz
- Module 4: Statistical Data Analysis
- Introduction to Module 4
- How the Pandas Module Can Support Standard Business Analytics
- Introduction to Numpy
- Introduction to Descriptive Statistics
- Advanced Pandas
- Module 4 Overview
- Lesson 4-1 Readings
- Module 4 Graded Quiz
- Module 5: Introduction to Visualization
- Introduction to Module 5
- Creating Clear and Powerful Visualizations
- Visualization of Quantitative Data
- Introduction to Plotting
- Introduction to Data Visualization
- Module 5 Overview
- Lesson 5-1 Readings and Resources
- Lesson 5-2 Readings and Resources
- Lesson 5-4 Reading
- Module 5 Graded Quiz
- Module 6: Introduction to Probability
- Introduction to Module 6
- Introduction to Probability
- Introduction to Bayes Theorem
- Calculating Probabilities in Python
- Introduction to Distributions
- Module 6 Overview
- Lesson 6-1 Readings
- Lesson 6-2 Readings
- Lesson 6-3 Readings
- Lesson 6-4 Readings
- Module 6 Graded Quiz
- Module 7: Exploring Two-Dimensional Data
- Introduction to Module 7
- Introduction to Scatter Plots
- Introduction to Numpy Matrices
- Statistical Issues When Exploring Multi-Dimensional Data
- Introduction to Ordinary Linear Regression
- Module 7 Overview
- Lesson 7-3 Readings and Resources
- Lesson 7-4 Readings
- Module 7 Graded Quiz
- Module 8: Introduction to Density Estimation
- Introduction to Module 8
- Why Do Accounting Students Need Data Analytics Skills?
- Introduction to Density Estimation
- Advanced Density Estimation
- Gies Online Programs
- Module 8 Overview
- Lesson 8-1 Readings
- Congratulations!
- Module 8 Graded Quiz
Summary of User Reviews
Data Analytics for Accountancy 1 is a comprehensive course that provides hands-on experience in data analysis for accountants. Many users appreciated the practical approach of the course.Pros from User Reviews
- Hands-on experience in data analysis
- Comprehensive course content
- Great practical examples
- Excellent instructors
- Flexible scheduling
Cons from User Reviews
- Some technical jargon may be difficult to understand
- Course pace may be too fast for beginners
- Some users reported technical difficulties with the platform
- Limited interaction with instructors
- Some users felt the course was too expensive
Robert Brunner
4.3 Raiting
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