About this Course
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100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 40 hours to complete

Suggested: 6 hours/week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 40 hours to complete

Suggested: 6 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
1 hour to complete

Course Orientation

2 videos (Total 9 min), 4 readings, 1 quiz
2 videos
Meet Professor Brunner4m
4 readings
Syllabus10m
About the Discussion Forums10m
Updating Your Profile10m
Social Media10m
1 practice exercise
Orientation Quiz10m
9 hours to complete

Module 1: Introduction to Machine Learning

4 videos (Total 47 min), 3 readings, 2 quizzes
4 videos
Introduction to Machine Learning14m
Introduction to Linear Regression14m
Introduction to k-nn12m
3 readings
Module 1 Overview10m
Lesson 1-1 Readings10m
Lesson 1-2 Readings10m
1 practice exercise
Module 1 Graded Quiz20m
Week
2
9 hours to complete

Module 2: Fundamental Algorithms

5 videos (Total 52 min), 4 readings, 2 quizzes
5 videos
Introduction to Fundamental Algorithms3m
Introduction to Logistics Regression14m
Introduction to Decision Trees15m
Introduction to Support Vector Machine13m
4 readings
Module 2 Overview10m
Lesson 2-1 Readings10m
Lesson 2-3 Readings10m
Lesson 2-4 Readings10m
1 practice exercise
Module 2 Graded Quiz20m
Week
3
8 hours to complete

Module 3: Practical Concepts in Machine Learning

5 videos (Total 40 min), 3 readings, 2 quizzes
5 videos
Introduction to Modeling Success6m
Introduction to Bagging11m
Introduction to Boosting9m
Introduction to ML Pipelines8m
3 readings
Module 3 Overview10m
Lesson 3-1 Readings10m
Lesson 3-2 Readings10m
1 practice exercise
Module 3 Graded Quiz20m
Week
4
9 hours to complete

Module 4: Overfitting & Regularization

5 videos (Total 48 min), 4 readings, 2 quizzes
5 videos
Introduction to Overfitting4m
Introduction to Cross-Validation13m
Introduction to Model-Selection16m
Introduction to Regularization8m
4 readings
Module 4 Overview10m
Lesson 4-1 Readings10m
Lesson 4-2 Readings10m
Lesson 4-3 Readings10m
1 practice exercise
Module 4 Graded Quiz20m
4.7
1 ReviewsChevron Right

Top reviews from Data Analytics Foundations for Accountancy II

By ADJun 23rd 2019

I like this course. Because it is very useful to accounting and auditing .

Instructor

Avatar

Robert Brunner

Professor
Accountancy

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Accountancy (iMSA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

About University of Illinois at Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.