UIUC - Data Analytics Foundations for Accountancy II
- Offered byCoursera
Data Analytics Foundations for Accountancy II at Coursera Overview
Duration | 70 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Analytics Foundations for Accountancy II at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 70 hours to complete
- English Subtitles: English
Data Analytics Foundations for Accountancy II at Coursera Course details
- 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!
Data Analytics Foundations for Accountancy II at Coursera Curriculum
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
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
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