Program Courses
Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
CIS 435-DL : Practical Data Science Using Machine Learning
Description
This course provides an overview of machine learning concepts, techniques, and tools that will help you deepen your understanding of large, complex datasets and your knowledge of intelligent systems. You will learn machine learning techniques that can optimize business processes, identify new revenue models, and support evidence-based decision making in industries such as finance, retail, and health care.
You will develop skills that will help you deconstruct a business problem into actionable tasks that include exploratory data analysis and visualization, data preprocessing and dimensionality reduction, algorithm selection, and model evaluation, optimization, and deployment. Open-source development frameworks, including Python and the Scikit-Learn and TensorFlow libraries, are used to implement supervised and unsupervised learning methods. You will work with a variety of machine learning algorithms. Regression, classification, regularization, decision trees, clustering, Bayesian, ensemble, dimensionality reduction, and different neural networks will all be explored.
Students must complete CIS 417 and MSDS 430 prior to enrolling in CIS 435.
Note for students in the MSIS program: This course is required for the specializations in Analytics and Business Intelligence, Artificial Intelligence, Data Science, and Database and Internet Technologies. This course may be used as an elective towards all other specializations.
Note for students in the MHI/MMI program: This course is an elective for students pursuing the MHI/MMI degree.
Note for students in the MSDS/MSPA program: This course is an elective for students pursuing the MSDS/MSPA degree.
Note for all students: This course was formerly called Data Warehousing and Data Mining.