2021–2022 CATALOG AND STUDENT HANDBOOK COURSE DESCRIPTIONS QMB4000 Data Elements 60 hours, 4 credits This course reviews the concepts, standards, and functions used to identify data elements necessary for an efficient data preparation process. Prerequisite: QMB3200 Introduction to Scripting QMB4100 Applied Business Intelligence 60 hours, 4 credits This course allows students to apply skills and techniques for analyzing existing business performance data to provide support for business planning. It places focus on planning an end-to-end business intelligence process, platform, database, and analytical tool usage. Students will learn about processing and analyzing data, quality assurance and regulatory adherence, and preparing data for consumption. Students will create visualizations to help guide business decision-making. Prerequisite: CTS3265C Introduction to Business Intelligence QMB4200 Advanced Analytics Platforms, Environments, and Software 60 hours, 4 credits This course is for the student of advanced analytics. It places focus on developing and deployed Extract Transform Load (ETL) jobs for large data sets. Topics will include how to configure the environment to run the advanced analytic job. It places focus on real-time analytics as well. This course will prepare students for developing advanced analytics and ETL jobs. It also prepares students about how to deploy the advanced analytics in the enterprise environment. Prerequisite: QMB3100 Foundations of Analytics Platforms, Environments, and Software QMB4300 Data Quality in Analytics 60 hours, 4 credits Quality data allows for quality analysis. In this course, students will learn how to identify common types of data quality issues including missing data, incorrect data, outliers, normalization, and duplication. This course will prepare students to prepare data for analytics projects. Prerequisite: None QMB4400 Data Analysis and Optimization 60 hours, 4 credits This course will allow students to run data extracts and scripts to demonstrate a complete data analysis process, while requiring the identification and application of data element requirements, scripting modifications, and preparation techniques that could improve analysis results. Prerequisites: QMB4000 Data Elements; QMB4300 Data Quality in Analytics QMB4500 Data Visualization Implementation and Communication 60 hours, 4 credits This course focuses on the study of data sets which relate to meeting client needs. It includes methods used to evaluate data such as benchmarking, scoring, and ranking. Students learn the difference between correlation and causation. Students will explore techniques for visualizing both quantitative and qualitative data. This course will prepare students with the skills to derive business insights and make meaningful inferences from data sets. Prerequisite: QMB3300 Introduction to Data Visualization QMB4900 Data Analytics Capstone 60 hours, 3 credits This course allows students to demonstrate their skills and techniques for analyzing generalized business data to provide support for business planning. It places focus on planning an end-to-end business analytics process; platform, database, and analytical tool usage; processing and analyzing data; quality assurance and regulatory adherence; preparing data for consumption; and visualization creation to help guide business decision-making. Prerequisite: Expected to be the final upper-level core course completed QMB5000 Foundations of Data Science 40 hours, 4 credits This course introduces students to the core concepts, processes, and tools of data science, while exploring the basics of common techniques in the data science field. In this course, students will develop the skills needed to apply the early aspects of the life cycle of analytics. Students will review the different types of data sources and explore various data models and algorithms. Students will also use basic tools to complete an analysis and collaborate within teams to evaluate case studies and explore ways in which stakeholder’s needs are met through data science. Prerequisite: Expected to be completed in the student’s first quarter QMB5100C Data Science Languages 60 hours, 4 credits In this course, students will improve their knowledge of the most current programming languages in data science including relevant data structures, functions, and methods of invoking application programming interfaces (APIs), and techniques that support the construction of large-scale data science applications. Prerequisite: Expected to be completed in the student’s first quarter QMB5200C Advanced Database Management 60 hours, 4 credits 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 QMB5300C 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 QMB5400C 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 QMB5500C 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 QMB6000C 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, Principal components analysis, and Monte Carlo Simulation. This course builds on an existing foundation of basic probability and distributions. Prerequisites: QMB5300C Statistical Methods; QMB5500C Risk Assessment Modeling QMB6100C 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: QMB5400C Fundamental Classification Techniques QMB6200C 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: QMB5400C Fundamental Classification Techniques QMB6300C 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 on how to approach development of big data applications as well as how to tune and optimize applications in this environment. Prerequisite: None QMB6400C 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: QMB5200C Advanced Database Management QMB6900L Data Science Capstone 80 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. Prerequisite: Expected to be the final upper-level course completed REL3308 Contemporary World Religions 40 hours, 4 credits An investigation of the historical and theological development of world religions from earliest times until the present. The course will cover the lives of the major religious founders and leaders in history, as well as the scriptures and religious text of world religions. The development of religious rituals will also be dealt with. The relationship between world religions and secular governments will be investigated, as well as the role and status of women in world religions. Prerequisite: None RMI4020 Risk Management 40 hours, 4 credits This upper-level business course explores the elements of risk management and insurance essential to the business environment. This course will develop the rationale for risk-management systems and examine the environments in which they operate. Students will learn, analyze, and evaluate approaches to measuring and managing risks in various business environments. Prerequisite: None RTE1000 Introduction to Radiology and Patient Care 80 hours, 5 credits This course provides an overview of radiology and its role in the healthcare system. Principles, practices, and policies of healthcare organizations are explored. The legal, ethical, and professional standards related to radiology are examined. This course will include the basics of patient-care skills in the radiology department. Prerequisite: None RTE1100 Radiology Physics 70 hours, 5 credits This course is the study of radiographic physics. It places focus on the process in which the X-ray circuit creates electrons and the interactions that occur inside and outside the X-ray tube. Topics covered will be the X-ray circuit, X-ray production, and photon interactions with matter. This course will prepare students for operation of the X-ray control panel and X-ray tube. Prerequisites: Introduction to Radiology and Patient Care; Algebra ALL CONTENT IS SUBJECT TO CHANGE BY ADDENDUM 123 R 1 R TE 1 T 0 0 0 0 E 0L L 0 L ae b (ct 6 u re0 h ( o2 u0 r hs o u r es, d 2 t c sre ) i d , 3 c r i ts ) R 1 R TE 1 T 1 0 E 0 0L L 0 L ae b (ct 4 1 u re0 h ( o3 u0 r hs o u r es, d 3 t c sre ) i d , 2 c r i ts )
