ACCT 6340 - Predictive Analytics for Accounting & Decision Making

Institution:

Utah Tech University

Subject:

Description:

This course will build a practical foundation for machine learning by teaching students basic tools and techniques that can scale to large computational systems and massive data sets. This course follows the first course in the foundations of data analytics series to teach students to draw inferences from large, complex, and diverse data. This course contains eight modules that introduce machine learning, with a focus on business applications. This course will introduce the Scikit Learn and Statsmodels Python modules, while also demonstrating new applications of the NumPy, Pandas, Matplotlib, and Seaborn modules. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Distinguish the different types of machine learning algorithms and provide examples where each type might be applied within the Accounting profession. 2. Explain the challenges in cleaning and pre-processing data. 3. Explain how to work effectively with imbalanced training classes. 4. Demonstrate proficiency performing basic descriptive and predictive analysis such as regression, k-nearest neighbor, decision tree, support vector, ensemble learning and Bayes and Gaussian process algorithms. 5. Evaluate the performance and ethical use of a machine learning classification and machine learning regression analysis. SP

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.utahtech.edu/

Phone Number:

(435) 652-7500

Regional Accreditation:

Northwest Commission on Colleges and Universities

Calendar System:

Semester

General Education

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