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Program Courses

Information Systems

Program Courses

CIS 435-0 : Data Science/Machine Learning


Description

This course provides an overview of machine learning concepts, techniques, and tools with a practical emphasis on understanding large, complex datasets and building intelligent systems. Insights gleaned from data mining and machine learning can be used to optimize operational processes, identify new business opportunities, and support evidence-based decision making and digital marketing with applications in industries such as finance, retail, and healthcare. Students will develop skills to decompose 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 and students are exposed to a variety of machine learning algorithms. These include regression, classification, regularization, decision trees, clustering, Bayesian, ensemble, dimensionality reduction, and neural networks.

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.


Spring 2019
Start/End DatesDay(s)TimeBuildingSection
04/01/19 - 06/09/19Optional Sync Tu
7 – 9:30 p.m. 55
InstructorCourse FormatStatusCAESAR Course ID
Kakade, Sunil
Online
Open
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