Data Analytics Foundations for Accountancy II
Master advanced data analytics for accountancy. Learn machine learning, algorithms & Python programming.
Master advanced data analytics for accountancy. Learn machine learning, algorithms & Python programming.
This comprehensive course delves into advanced data analytics techniques for accountancy professionals. Students will explore machine learning concepts, fundamental algorithms, and practical applications using Python and scikit-learn. The curriculum covers a wide range of topics including linear regression, logistic regression, decision trees, support vector machines, ensemble methods, regularization techniques, and clustering algorithms. Participants will also learn about feature engineering, anomaly detection, and ethical considerations in machine learning. Through hands-on programming assignments and quizzes, students will gain practical experience in implementing these techniques and understanding their applications in real-world accounting scenarios.
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What you'll learn
Understand fundamental concepts of machine learning and their applications in accountancy
Master the use of Python and scikit-learn for implementing machine learning algorithms
Learn various classification and regression techniques including logistic regression, decision trees, and support vector machines
Explore ensemble methods such as bagging and boosting for improving model performance
Understand regularization techniques to prevent overfitting in machine learning models
Gain proficiency in feature selection and dimension reduction methods
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
9 quizzes, 8 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 9 modules in this course
This course provides a comprehensive foundation in advanced data analytics techniques for accountancy professionals. Students will learn about machine learning concepts, fundamental algorithms, and their practical applications using Python and the scikit-learn library. The curriculum covers a wide range of topics, starting with basic machine learning concepts and progressing to more complex algorithms and techniques. Key areas of study include linear regression, logistic regression, decision trees, support vector machines, ensemble methods like bagging and boosting, regularization techniques, clustering algorithms, and anomaly detection. The course also addresses important practical aspects such as feature engineering, cross-validation, and ethical considerations in machine learning. Through a combination of video lectures, readings, quizzes, and hands-on programming assignments, students will gain both theoretical knowledge and practical skills in applying these techniques to real-world accounting and business scenarios.
Course Orientation
Module 1 · 1 Hours to complete
Introduction to Machine Learning
Module 2 · 8 Hours to complete
Fundamental Algorithms
Module 3 · 9 Hours to complete
Practical Concepts in Machine Learning
Module 4 · 8 Hours to complete
Overfitting & Regularization
Module 5 · 8 Hours to complete
Fundamental Probabilistic Algorithms
Module 6 · 7 Hours to complete
Feature Engineering
Module 7 · 8 Hours to complete
Introduction to Clustering
Module 8 · 8 Hours to complete
Introduction to Anomaly Detection
Module 9 · 7 Hours to complete
Fee Structure
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Instructor
Associate Dean for Innovation and Chief Disruption Officer at the University of Illinois Urbana-Champaign
Robert J. Brunner serves as the Associate Dean for Innovation and Chief Disruption Officer at Gies College of Business, University of Illinois Urbana-Champaign. He is also a Professor of Accountancy and an Arthur Andersen Faculty Fellow. Brunner has an extensive academic background with appointments across various departments, including Computer Science and Astronomy, and holds degrees in physics and astrophysics from Purdue University and Johns Hopkins University. His work focuses on the intersection of technology and business, particularly in areas such as data analytics and emerging technologies.
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4.4 course rating
10 ratings
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