ADDENDUM to the Rasmussen College Catalog 2018-2019 June 27, 2019 In this course, students will improve their database design skills while obtaining more experience writing complicated SQL queries for relational databases. Students will also gain a strong exposure to technologies and software that work with databases to support higher levels of data integration. In addition, students will be exposed to alternatives to relational databases and understand the advantages and disadvantages of each, most notably, document databases and graph-based databases. Prerequisite: None QMB 5300C Statistical Methods 60 hours, 4 credits In this course, students will learn basic statistical methods through the use of linear model theory and regression. Students will learn how to apply statistical techniques to improve the performance of data analysis systems. Although R or Python programming is necessary to carry out assignments, this course does not offer programming instructions. Students are expected to have basic R or Python skills and to improve upon them throughout the course. Prerequisite: None QMB 5400C Fundamental Classification Techniques 60 hours, 4 credits In this course, students will focus on techniques, concepts, methods, and skills for developing classification models, analysis databases, and data warehouses. Students will develop analytical thinking to identify appropriate business strategies. This course focuses on the programmatic interface between databases and analytical tools, the classification foundation of data science, dimensional modeling, and the extraction-transformation- loading staging of a database and data warehouse. Prerequisite: None QMB 5500C Risk Assessment and Modeling Methods 60 hours, 4 credits This course covers the fundamental concepts of risk and exposure as well as the existing techniques in insurance, health management, and financial industries. Students will assess, map, and minimize potential risks using the available data analytics techniques. Prerequisite: None QMB 6000C Advanced Statistical Techniques 60 hours, 4 credits This course expands upon basic statistics in order to support the means of determining solutions to problems that require several levels of decision-making or that may approach an intractable level. This course introduces techniques including Markov Process Models, Principle components analysis, and Monte Carlo Simulation. This course builds on an existing foundation of basic probability and distributions. Prerequisite: QMB 5300C Statistical Methods; QMB 5500C Risk Assessment Modeling QMB 6100C Advanced Machine Learning 60 hours, 4 credits This course addresses the application of neural nets, deep learning method, and cross-learning technique for classification and verification. It also covers techniques including the application of support vector machines (SVM), genetic algorithms, and genetic programming. Prerequisite: QMB 5400C Fundamental Classification Techniques QMB 6200C Text Mining 60 hours, 4 credits This course covers theoretical aspects that are relied upon in text mining techniques, as well as the application of text mining tools. Use of these tools supports the development of complete software pipelines, which in turn, support the means of extracting hidden patterns and information from large collections of unstructured data. Students will gain a solid understanding of how to interpret large collections of textual data and apply several techniques of learning against them. Prerequisite: QMB 5400C Fundamental Classification Techniques QMB 6300C Big Data Technologies 60 hours, 4 credits This course will introduce the student to working within the world of Big Data, by explaining its purpose, major tools, programming paradigms as well as data structures and programming techniques. IT will also inform the student of how to approach development of Big Data applications as well as how to tune and optimize applications in this environment. Prerequisite: None QMB 6400C Data Visualization and Communication 60 hours, 4 credits In this course, students will conduct descriptive, predictive, and prescriptive data analysis, and utilize various programs to visualize the findings. Students will then articulately convey those findings using technical writing and reporting skills. Prerequisite: QMB 5200C Advanced Database Management QMB 6900L Data Science Capstone 40 hours, 4 credits In this course, students will solve and address data science problems in an industry setting, such as medicine and health, retail, engineering, or government agency. The final project synthesizes machine learning, data mining, statistical learning, decision analysis, and computational challenges involved in solving complex, real-world problems. This addendum modifies catalog content as indicated, and supersedes all previous addenda. Page 15 / 47
