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Data Analytics Foundations for Accountancy II

Approx. 70 hours to complete

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Course Summary

Learn how to use data analytics to enhance accounting processes and make better business decisions. This course covers data visualization, data analysis, and data-driven decision making in accounting.

Key Learning Points

Understand the importance of data analytics in accounting
Learn to use data visualization tools to improve financial reporting
Apply data analysis techniques to make more informed business decisions

Learning Outcomes

Develop skills in data visualization and analysis
Apply data-driven decision making techniques to accounting processes
Improve financial reporting through data analytics

Prerequisites or good to have knowledge before taking this course

Basic understanding of accounting principles
Proficiency in Microsoft Excel

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Assignments and quizzes

Similar Courses

Data Analytics for Business
Data Visualization
Data Analysis and Presentation Skills: the PwC Approach

Related Education Paths

Notable People in This Field

Tom Hood
Jeffrey Thomson
Barbara O'Neill

Related Books

Description

Welcome to Data Analytics Foundations for Accountancy II! 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!

Outline

Course Orientation
Welcome to Data Analytics Foundations for Accountancy II
Meet Professor Brunner
Syllabus
About the Discussion Forums
Updating Your Profile
Social Media
Orientation Quiz

Module 1: Introduction to Machine Learning
Introduction to Module 1
Introduction to Machine Learning
Introduction to Linear Regression
Introduction to k-nn
Module 1 Overview
Lesson 1-1 Readings
Lesson 1-2 Readings
Module 1 Graded Quiz

Module 2: Fundamental Algorithms
Introduction to Module 2
Introduction to Fundamental Algorithms
Introduction to Logistics Regression
Introduction to Decision Trees
Introduction to Support Vector Machine
Module 2 Overview
Lesson 2-1 Readings
Lesson 2-3 Readings
Lesson 2-4 Readings
Module 2 Graded Quiz

Module 3: Practical Concepts in Machine Learning
Introduction to Module 3
Introduction to Modeling Success
Introduction to Bagging
Introduction to Boosting
Introduction to ML Pipelines
Module 3 Overview
Lesson 3-1 Readings
Lesson 3-2 Readings
Module 3 Graded Quiz

Module 4: Overfitting & Regularization
Introduction to Module 4
Introduction to Overfitting
Introduction to Cross-Validation
Introduction to Model-Selection
Introduction to Regularization
Module 4 Overview
Lesson 4-1 Readings
Lesson 4-2 Readings
Lesson 4-3 Readings
Module 4 Graded Quiz

Module 5: Fundamental Probabilistic Algorithms
Introduction to Module 5
Introduction to Practical Machine Learning
Introduction to Naive Bayes
Introduction to Gaussian Processes
Module 5 Overview
Lesson 5-1 Readings
Lesson 5-2 Readings
Lesson 5-3 Readings
Module 5 Graded Quiz

Module 6: Feature Engineering
Introduction to Module 6
Practical Concerns with Machine Learning
Introduction to Feature Selection
Introduction to Dimension Reduction
Introduction to Manifold Learning
Module 6 Overview
Lesson 6-1 Readings
Lesson 6-3 Readings
Lesson 6-4 Readings
Module 6 Graded Quiz

Module 7: Introduction to Clustering
Introduction to Module 7
Introduction to Clustering
Introduction to Spatial Clustering
Introduction to Density-Based Clustering
Introduction to Mixture Models
Module 7 Overview
Lesson 7-1 Readings
Lesson 7-2 Readings
Lesson 7-3 Readings
Lesson 7-4 Readings
Module 7 Graded Quiz

Module 8: Introduction to Anomaly Detection
Introduction to Module 8
Introduction to Anomaly Detection
Statistical Anomaly Detection
Machine Learning and Anomaly Detection
Gies Online Programs
Module 8 Overview
Lesson 8-1 Readings
Congratulations!
Module 8 Graded Quiz

Summary of User Reviews

Discover the world of data analytics in accountancy with this course on Coursera. Students have praised the course for its comprehensive approach to data analytics, making it easy to understand and apply in real-world scenarios.

Key Aspect Users Liked About This Course

The course is well-structured and covers all the necessary topics for understanding data analytics in accountancy.

Pros from User Reviews

Comprehensive course material that covers all necessary topics
Easy to understand and apply in real-world scenarios
Highly engaging and interactive course content
Great instructor support and feedback
Flexible learning schedule that allows students to learn at their own pace

Cons from User Reviews

Some users have complained about technical issues with the course platform
A few users have found the course material to be too basic and lacking in depth
The course may not be suitable for advanced learners looking for more challenging content
Limited opportunities for interaction with other students
The course may require a significant time commitment, especially for those new to data analytics

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