Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Data Analytics Foundations for Accountancy II certification course is 70 hours one that is designed by the University of Illinois at Urbana-Champaign, and Coursera jointly. The university is a world-class researcher of teaching and has carefully designed this course for those who want an online mode where they can start learning instantly after submitting the fee. The course is best for people wanting to know about data analytics foundational concepts that are needed in accountancy.
The Data Analytics Foundations for Accountancy II training is a self-paced programme where the students shall be able to review the materials that are covered each week, and also get a glimpse of the assignments that one will need to pass in order to successfully pass, and complete the course. The students also have an instructor Robert Brunner who will be there to assist in case the students face issues.
The Highlights
- Online course
- Shareable certificate
- 70 hours for completion
- English course title available
Programme Offerings
- Flexible Deadlines
- Short Programme
- Many Subtitles
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 4926 | yes | Coursera |
The Data Analytics Foundations for Accountancy II certification fee is Rs. 4,926 The enrolled students shall have to submit the direct fee
Data Analytics Foundations for Accountancy II Fee Details
Description | Amount in INR |
Course Fee, With Certificate | Rs. 4,926 |
Audit-Only | Free |
Eligibility Criteria
Certification Qualifying Details
The Data Analytics Foundations for Accountancy II certification by Coursera is offered when all the assignments, as well as the quizzes, are completed by the participants.
What you will learn
The Data Analytics Foundations for Accountancy II certification syllabus shall help everyone learn basic concepts in data analysis that are required for accountancy, and the organization.
Who it is for
People like data analysts, and accountants will be ideal for the Data Analytics Foundations for Accountancy II course.
Admission Details
To get admission to the Data Analytics Foundations for Accountancy II classes, the students can follow these steps:
Step 1: Follow the official URL: https://www.coursera.org/learn/data-analytics-accountancy-2.
Step 2: Next, the ‘Enrol Now’ button has to be seen, and located so that can be clicked upon.
Step 3: Then, the certification has to be brought to get admission.
The Syllabus
Videos
- Welcome to Data Analytics Foundations for Accountancy II
- Meet Professor Brunner
- Learn on Your Terms
Readings
- Syllabus
- About the Discussion Forums
- Learn More About Flexible Learning Paths
- Updating Your Profile
- Social Media
Quiz
- Orientation Quiz
Discussion Prompt
- Getting to Know Your Classmates
Videos
- Introduction to Module 1
- Introduction to Machine Learning
- Introduction to Linear Regression
- Introduction to k-nn
Readings
- Module 1 Overview
- Lesson 1-1 Readings
- Lesson 1-2 Readings
Practice Exercise
- Module 1 Graded Quiz
Videos
- Introduction to Module 2
- Introduction to Fundamental Algorithms
- Introduction to Logistics Regression
- Introduction to Decision Trees
- Introduction to Support Vector Machine
Readings
- Module 2 Overview
- Lesson 2-1 Readings
- Lesson 2-3 Readings
- Lesson 2-4 Readings
Practice Exercise
- Module 2 Graded Quiz
Programming assignment
- Module 2 Programming Assignment
Ungraded labs
- Introduction to Logistic Regression Notebook
- Introduction to Decision Trees Notebook
- Introduction to Support Vector Machine Notebook
- Module 2 Programming Assignment Notebook
Videos
- Introduction to Module 3
- Introduction to Modeling Success
- Introduction to Bagging
- Introduction to Boosting
- Introduction to ML Pipelines
Readings
- Module 3 Overview
- Lesson 3-1 Readings
- Lesson 3-2 Readings
Quiz
- Module 3 Graded Quiz
Programming assignment
- Module 3 Programming Assignment
Ungraded labs
- Introduction to Bagging Notebook
- Introduction to Boosting Notebook
- Practical Concerns in Machine Learning
- Module 3 Programming Assignment Notebook
Videos
- Introduction to Module 4
- Introduction to Overfitting
- Introduction to Cross-Validation
- Introduction to Model-Selection
- Introduction to Regularization
Readings
- Module 4 Overview
- Lesson 4-1 Readings
- Lesson 4-2 Readings
- Lesson 4-3 Readings
Practice Exercise
- Module 4 Graded Quiz
Programming assignment
- Module 4 Programming Assignment
Ungraded labs
- Introduction to Cross-Validation Notebook
- Introduction to Model-Selection Notebook
- Introduction to Regularization Notebook
- Module 4 Programming Assignment Notebook
Videos
- Introduction to Module 5
- Introduction to Practical Machine Learning
- Introduction to Naive Bayes
- Introduction to Gaussian Processes
Readings
- Module 5 Overview
- Lesson 5-1 Readings
- Lesson 5-2 Readings
- Lesson 5-3 Readings
Quiz
- Module 5 Graded Quiz
Programming assignment
- Module 5 Programming Assignment
Ungraded labs
- Introduction to Naive Bayes Notebook
- Introduction to Gaussian Processes Notebook
- Module 5 Programming Assignment Notebook
Videos
- Introduction to Module 6
- Practical Concerns with Machine Learning
- Introduction to Feature Selection
- Introduction to Dimension Reduction
- Introduction to Manifold Learning
Readings
- Module 6 Overview
- Lesson 6-1 Readings
- Lesson 6-3 Readings
- Lesson 6-4 Readings
Quiz
- Module 6 Graded Quiz
Programming assignment
- Module 6 Programming Assignment
Ungraded labs
- Introduction to Feature Selection Notebook
- Introduction to Dimension Reduction Notebook
- Introduction to Manifold Learning Notebook
- Module 6 Programming Assignment Notebook
Videos
- Introduction to Module 7
- Introduction to Clustering
- Introduction to Spatial Clustering
- Introduction to Density-Based Clustering
- Introduction to Mixture Models
Readings
- Module 7 Overview
- Lesson 7-1 Readings
- Lesson 7-2 Readings
- Lesson 7-3 Readings
- Lesson 7-4 Readings
Quiz
- Module 7 Graded Quiz
Programming assignment
- Module 7 Programming Assignment
Ungraded labs
- Introduction to Spatial Clustering Notebook
- Introduction to Density-Based Clustering Notebook
- Introduction to Mixture Models Notebook
- Module 7 Programming Assignment Notebook
Videos
- Introduction to Module 8
- Introduction to Anomaly Detection
- Statistical Anomaly Detection
- Machine Learning and Anomaly Detection
- Gies Online Programs
Readings
- Module 8 Overview
- Lesson 8-1 Readings
- Congratulations!
- Get Your Course Certificate
Quiz
- Module 8 Graded Quiz
Programming assignment
- Module 8 Programming Assignment
Ungraded labs
- Statistical Anomaly Detection Notebook
- Machine Learning and Anomaly Detection Notebook
- Module 8 Programming Assignment Notebook
Plugin
- How was the course
Instructors
University of Illinois, Urbana Champaign Frequently Asked Questions (FAQ's)
This online course is a Part of the Master of Science in Accountancy (iMSA) degree programme.
The approximate number of hours is 70.
Already more than 3500 students have signed up.
Robert Brunner is the tutor of the programme.
The University of Illinois at Urbana-Champaign is the supporting institution.
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