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COSC 498 - Capstone Project
Credits: 3
This course is the final course of the computer science program. This capstone course provides an opportunity to apply the knowledge and skills gained from the program to solve real-world problems. Taken as the last course of the program.
Prerequisite: Senior Status and Computing Core
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CPAR 101 - Roles and Responsibilities of the Community Paramedic
Credits: Two (2)
This course will introduce the student to the role and responsibilities of the Community Paramedic. The Community Paramedic’s specific role and responsibilities as a part of the healthcare team and community will be addressed. Interdisciplinary work will be explored and defined as it relates to population-focused care and the Community Paramedic.
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CPAR 120 - Community Assessment
Credits: Three (3)
This course will introduce the role of the Community Paramedic in community assessment. Effects of social, cultural, ecological, political, and economic factors are studied as they impact families and communities.
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CPAR 200 - Management of Chronic Diseases
Credits: Four (4)
This course will describe the care and management of the patient with chronic diseases. Patient assessment, medications, and additional therapies are emphasized in this course. Course content includes the Community Paramedic’s responsibilities for documentation during patient encounters.
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CPAR 220 - Community Paramedic Practicum
Credits: Three (3)
Clinical activities are concentrated in the community and encourage students to practice community paramedicine with families and populations.
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CRIM 102 - Introduction to Criminal Justice
Credits: Three (3)
This course introduces the fundamental concepts and frameworks used in the criminal justice studies. It provides a survey of the various agencies making up the U.S. criminal justice system, primarily the criminal court, law enforcement, and corrections.
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CRIM 119 - Murder to Trial
Credits: Four
This course introduces students to the interrelationship between the investigation of a crime, the process of forensic evidence, and the use of that evidence at trial. Students will be taken to a mock crime scene where they will learn through experience as they collect evidence they will process in the lab and conclude with a mocktrial.
Cross-listed: FRSC 119
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CRIM 201 - Police Management
Credits: Three (3)
This course examines contemporary approaches in police management at the local, state, and federal levels. Attention is paid to the fiscal and personnel management techniques utilized by law enforcement agencies, plus the recruitment and maintenance of officer staffing.
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CRIM 203H - Sexual Violence and Aggression
Credits: Four (4)
Even though there have been considerable monetary and time resources spent on violent crimes by local, state, and federal agencies, law enforcement have had a difficult time prosecuting the sexually violent criminal because of societal views on personal freedoms. This course is a systematic introduction to the causation of violent offending and extreme aggressive behavior and the treatment and incarceration of this type of criminal. The readings and course framework will stress a criminological approach to sexual violence and aggression.
Cross-listed: WGS-203H
Prerequisite: Membership in Bascom Honors Program
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CRIM 210 - Multicultural Policing
Credits: Three (3)
This course focuses on the challenges present in policing multicultural settings. Issues pertaining to understanding diverse cultures and effective communication across cultures from the standpoint of law enforcement are explored in detail.
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CRIM 211 - Introduction to Criminal Investigations
Credits: Three (3)
This course is a comprehensive approach to the examination and treatment of physical evidence as it relates to the criminal justice system. Students will be involved in an interactive learning experience tied to the securing and preserving of crime scenes and maintaining the integrity of trace evidence for future court proceedings.
Cross-listed: FRSC-211
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CRIM 220 - Corrections in Society
Credits: Three (3)
This course explores the evolution of correctional practices in the United States. It also examines and assesses the variety of correctional options utilized within the criminal justice system.
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CRIM 250 - Experiential Policing
Credits: Three (3)
This course introduces students to the dynamics of police recruit training. Student are familiarized with a myriad of law enforcement issues such as criminal and traffic law, juvenile justice, report writing, and investigative patrol tactics. In addition, this course introduces students to the physical rigor required for successful acceptance to St. Louis County Municipal Police Academy.
Prerequisite: CRIM-102
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CRIM 297 - Special Studies
Credits: One (1) to Four (4)
These courses are offered periodically based on the interests of our students and faculty.
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CRIM 305 - Family Violence Through the Lifespan
Credits: Three (3)
Family Violence across the Lifespan explores the etiology, prevalence, treatment, and prevention of family violence. A broad coverage of viewpoints and theories behind family violence are covered.
Cross-listed: SOC-305
Prerequisite: SOC-101 or CRIM-102; Minimum grade C-
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CRIM 311 - Criminal Law and Procedure
Credits: Three (3)
This course will examine substantive criminal law and the elements of specific crimes, including crimes against persons and property. Students also will be introduced to the sources of criminal procedural law and the steps involved in a prosecution. Defenses and immunities to crimes will be discussed also.
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CRIM 321 - Cultural Relativism and Social Deviance
Credits: Three (3)
This course studies the understanding of normative violations within society, specifically, how actions and/or actors come to be defined as deviant and what functions deviance serves in society.
Cross-listed: SOC-321
Prerequisite: SOC-101
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CRIM 322 - Criminological Theory
Credits: Three (3)
This course offers an introduction to criminology and a survey of the major theoretical traditions within criminology over the past three centuries.
Cross-listed: SOC-322
Prerequisite: SOC-101, SOC-202H, or CRIM-102
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CRIM 323 - Juvenile Delinquency
Credits: Three (3)
This course surveys the criminal and deviant conduct of youths. Theoretical and treatment patterns are also considered.
Cross-listed: SOC-323
Prerequisite: SOC-101 or SOC-202H
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CRIM 326 - Criminal Behavior
Credits: Three (3)
The purpose of this course is to understand the dynamics of criminal behavior. Using a multidisciplinary approach, the course will explore the major sociological, criminological, and psychological theories attempting to explain criminal activity.
Cross-listed: PSYC/SOC-326
Prerequisite: SOC-101, SOC-202H, PSYC-101, PSYC-202H, or CRIM-102
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CRIM 397 - Special Studies
Credits: One (1) to Four (4)
These courses are offered periodically based on the interests of our students and faculty.
Prerequisite: SOC-101, SOC-202H, or CRIM-102; Minimum grade C-
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CRIM 405 - Domestic and International Terrorism
Credits: Three (3)
This course surveys perspectives pertaining to the etiology of terrorism at both the domestic and international level. Study of the various tactics used by identified terrorist groups is explored. In addition, the techniques of counter terrorism are assessed.
Cross-listed: PSCI/SOC 405
Prerequisite: PSCI-110, SOC-101, or SOC-202H
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CRIM 454 - Gender and Crime
Credits: Three (3)
This course explores variation in offending and victimization across the genders. Specific attention will be paid to theories that attempt to explain why such variation exists and such patterns changed over time.
Cross-listed: SOC/WGS-454
Prerequisite: SOC-101 or SOC-202H; Minimum Grade C-
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CRIM 477 - Police Psychology
Credits: 3
This course explores psychological principles as applied to aspects of police officer’s career. Some of the topics to be examined are: the unique psychological stresses of police work, the effects of that stress on both the officer and his or her family; identification and management of the problem police officer; psychology of crowds; riots and their effective control; and the application of psychological principles to detective work. The interpersonal dynamics of the police with civilian complainants, victims, and violent, aggressive individuals will also be covered.
Cross-listed: PSYC-477
Prerequisite: PSYC-101, PSYC-202H, or CRIM-102
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CRIM 485 - Race, Ethnicity and Crime
Credits: Three (3)
This course examines the interrelationship of race/ethnicity and criminal victimization/offending. In-depth consideration will be given to the issue of hate-crimes, racial profiling, wrongful convictions and disparities in sentencing.
Cross-listed: SOC-485
Prerequisite: SOC-101 or SOC-202H
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CRIM 494 - Police Academy Training Program
Credits: 12
This course is part of a programmatic partnership with the St. Louis County and Municipal Police Academy. To participate in this program, students must meet the entrance standards for the Academy. To earn credit from this program, students must successfully complete Academy training, and pass the POST exam.
Prerequisite: Permission of Criminal Justice/Criminology Program Director
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CRIM 495 - Internship
Credits: Three (3)
This course is designed as a culminating experience to integrate theory and practice in the context of an approved field-based experience under the supervision of the course instructor. Practicum requires completion of 135 clock hours on site along with coursework relevant to the student’s field experience.
Prerequisite: PSYC-101,PSYC-101F, PSYC-202H, SOC-101, SOC-101F or SOC-202H; Minimum grade C-
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CRIM 496 - Independent Study
Credits: One (1) to Four (4)
Junior or senior level students may design and conduct an independent study project in a field of their interest under the direction of a faculty advisor.
Prerequisite: One 200 level or higher CRIM course
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CRIM 497 - Special Studies
Credits: One (1) to Four (4)
These courses are offered periodically based on the interests of our students and faculty.
Prerequisite: CRIM-102
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DSCI 200 - Foundations of Data Science
Credits: Three (3)
The course develops the core concepts and skills in statistical inference and computational techniques through working on real-world data. The course is intended to introduce the foundation of data science to entry-level students who have not previously taken statistics or computer science courses.
Prerequisite: MATH-117
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DSCI 201 - Math Modeling-Excel
Credits: Three (3)
Students receive basic training in Microsoft Excel. A variety of real-life math models will provide the context for developing spreadsheet proficiency, including functions and formulas, pivotal pivot tables, statistical analysis, numerical solutions, optimization, and graphical output. Other areas to be covered include database applications and basic application programming techniques.
Prerequisite: MATH-117
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DSCI 297 - Special Studies
Credits: Variable
Prerequisite: Permission of Program Director
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DSCI 301 - Math Modeling-VBA
Credits: Three (3)
The content focuses on business applications including finance, statistics, and mathematical modeling. The applications provide the context for developing programming skills, using the Visual Basic Applications language as the programming vehicle.
Prerequisite: MATH-117
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DSCI 302 - Introduction to R
Credits: Three (3)
This course covers practical issues in data analysis and graphics such as programming in R, debugging R code, Jupyter Notebook, cloud computing, data exploration, and data visualization. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: MATH-117
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DSCI 303 - Introduction to Python
Credits: Three (3)
This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-503
Prerequisite: MATH-117
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DSCI 304 - Introduction to SQL
Credits: Three (3)
This course is for students who want to enhance their SQL skills through exploring real-world examples. Topics covered include but are not limited to pattern-matching using regular expressions, analytical functions, and common table expressions. Students are expected to be able to construct advanced SQL queries to retrieve desired information from the database and solve real-world problems.
Cross-listed: DSCI-504
Prerequisite: MATH-117
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DSCI 307 - SAS Programming
Credits: Three (3)
This class is an introduction to the SAS programming language. Topics include reading, exporting, sorting, printing, and summarizing data; modifying and combining data sets; writing flexible code with the SAS macro facility; visualizing data; and performing descriptive and basic statistical analyses such as Chi-square tests, T-Tests, ANOVA, and regression. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-507
Prerequisite: MATH-117
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DSCI 314 - Natural Language Processing
Credits: 3
This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails, and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-614
Prerequisite: DSCI-303
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DSCI 318 - Experimental Design
Credits: Three (3)
This course covers principles of experiments and basic statistics using SAS. Topics include analysis of variance, experimental designs, analysis of covariance, mixed model, categorical data analysis, survey data analysis, sample size and power analysis, and model comparison. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-618
Prerequisite: DSCI-307
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DSCI 324 - Data Visualization
This course is intended for students with introductory experience in SQL, R, and Excel. In this course students will learn how to connect SQL Server to tools like Excel, R, and Tableau, and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating report-quality graphs and presentations. Students will learn to use R Markdown so that their analysis follows the reproducible research paradigm. Finally, students will learn to build reporting and analytics dashboards in Shiny and Tableau. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
Cross-listed: DSCI-624
Prerequisite: DSCI-302 and DSCI-304
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DSCI 340 - Sports Analysis
Credits: 3
In this course the students will partner with specific Maryville Athletics teams to investigate questions of interest to athletics coaches. Students will collect, analyze, and report on data intended to resolve the coaches’ questions. Additionally, students will be responsible for recording and archiving their methods. Rationale is to provide students the real-world experience in sports analytics for job opportunities in this area in the future.
Prerequisite: DSCI-302
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DSCI 393 - Cooperative Education
Credits: Variable
Cooperative Education is a structured method of combining classroom-based education with practical work experience. A cooperative education experience, commonly known as a “co-op”, provides academic credit for structured job experience. Cooperative Education is taking on new importance in helping young people to make the school-to-work transition, service learning, and experiential learning initiatives.
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DSCI 397 - Special Studies
Credits: Variable
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DSCI 408 - Machine Learning
Credits: Three (3)
This is an introductory course in machine learning intended primarily for students majoring or minoring in Mathematics, Data Science, or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics, or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills, and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, k-means clustering, error calculation, optimization training, tree-based algorithms (including random forests), frequent item sets andrecommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks).
Cross-listed: DSCI-508
Prerequisite: DSCI-302 or DSCI-303
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DSCI 409 - Forecasting Principles
Credits: 3
This course is designed to provide a practical overview of forecasting practices and methodologies. The course will follow more of a “cookbook” approach rather than classical academic course. This course can benefit a wide range of audiences, from actuarial and data science students to “accidental” data analysts who are trained in the sciences, business, or engineering and then found themselves confronted with data for which they have no formal analytic training. In this course, different type of sequential information (such as financial or sales data) will be analyzed and several mathematical models that might be used to describe the processes which generate these types of data will be demonstrated. The end goal is to develop a set of skills to build a forecast model that provides intelligence about what might be expected in the future.
Cross-listed: DSCI-609
Prerequisite: DSCI-302
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DSCI 412 - Predictive Modeling
Credits: Three (3)
This course introduces students to fundamental statistical learning techniques that can be applied to real-world business problems. Topics include generalized linear models, tree-based models, clustering methods, and principal components analysis. It trains students to understand key steps and considerations in building predictive models, selecting a best model, and effectively communicating the model results. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-512
Prerequisite: DSCI-302 or DSCI-303
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DSCI 417 - Big Data Analytics
Credits: Three (3)
This course targets data scientists and data engineers. It covers programming with RDDs, tuning and debugging Spark applications, Spark SQL, Spark streaming, and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-617
Prerequisite: DSCI-303
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DSCI 419 - Deep Learning
Credits: Three (3)
This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Cross-listed: DSCI-619
Prerequisite: DSCI-408
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DSCI 421 - Introduction to Robotics
Credits: Three (3)
This course covers introduction to robotics, applications of robots, return-on-investment, abstract models, controlling robot motion, complex motion, robotic sensors, input/output, external sensors, threads, event programming, remote communication, remote sensing, behavior programming, and human/robot interfaces. Students will gain hands-on experience with emerging robot technologies, understand industrial applications of robots, and ramifications of human/robot interaction.
Prerequisite: DSCI-408
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DSCI 423 - Computer Vision
Credits: Three (3)
This course provides students with an introduction to the theory and practice of computer vision - the practice of analyzing visual images from the world. This course will cover a wide range of computer techniques and problems that address “how we make computers see”.
Prerequisite: COSC-130, DSCI-408 and MATH-316
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DSCI 498 - Capstone Project
Credits: Three (3)
The Capstone Project course is for the students to apply the knowledge acquired during the Data Science program to a company project involving actual data in a realistic setting. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem.
Cross-listed: DSCI-598
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DSCI 501 - Math Modeling
Credits: Three (3)
Students receive basic training in Microsoft Excel. A variety of real-life math models will provide the context for developing spreadsheet proficiency, including functions and formulas, pivotal tables, goal seeker, data input, data transfer, data protection, data table, and graphical out. Other areas to be covered include database applications and basic application programming techniques.
Note: This course is for graduate students only.
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DSCI 502 - R Programming
Credits: Three (3)
This course covers practical issues in data analysis and graphics such as programming in R, debugging R code, Jupyter Notebook, cloud computing, data exploration, and data visualization. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-302
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DSCI 503 - Python
Credits: Three (3)
This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib, and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-303
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DSCI 504 - SQL
Credits: Three (3)
This course covers practical issues in relational database systems that includes creating databases, updating data, retrieving data, and saving data in databases. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-304
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DSCI 507 - SAS Programming
Credits: Three (3)
This class is an introduction to the SAS programming language. Topics include reading, exporting, sorting, printing, and summarizing data; modifying and combining data sets; writing flexible code with the SAS macro facility; visualizing data; and performing descriptive and basic statistical analyses such as Chi-square tests, T-Tests, ANOVA, and regression. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-307
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DSCI 508 - Machine Learning
Credits: Three (3)
This course provides an introduction to machine learning. Topics include: supervised learning, machine learning algorithms, learning theory, reinforcement learning and adaptive control, neural networks, and applications of machine learning to data mining, autonomous navigation, and web data processing.
Note: This course is for graduate students only.
Cross-listed: DSCI-408
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DSCI 512 - Predictive Modeling
Credits: Three (3)
This course introduces students to fundamental statistical learning techniques that can be applied to real-world business problems. Topics include generalized linear models, tree-based models, clustering methods, and principal components analysis. It trains students to understand key steps and considerations in building predictive models, selecting a best model, and effectively communicating the model results. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-412
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DSCI 609 - Forecasting Principles
Credits: 3
This course is designed to provide a practical overview of forecasting practices and methodologies. The course will follow more of a “cookbook” approach rather than classical academic course. This course can benefit a wide range of audiences, from actuarial and data science students to “accidental” data analysts who are trained in the sciences, business, or engineering and then found themselves confronted with data for which they have no formal analytic training. In this course, different type of sequential information (such as financial or sales data) will be analyzed and several mathematical models that might be used to describe the processes which generate these types of data will be demonstrated. The end goal is to develop a set of skills to build a forecast model that provides intelligence about what might be expected in the future.
Cross-listed: DSCI-409
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DSCI 613 - NOSQL Database
Credits: Three (3)
This course covers no-relational database on a large scale. Topics include MongoDB, Cassandra, Redis, HBase, and Neo4j. Project based learnings is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
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DSCI 614 - Text Mining
Credits: Three (3)
This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails, and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-314
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DSCI 617 - Big Data Analytics
Credits: Three (3)
This course targets data scientists and data engineers. It covers programming with RDDs, tuning and debugging Spark applications, Spark SQL, Spark streaming, and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-417
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DSCI 618 - Experimental Design
Credits: Three (3)
This course covers principles of experiments and basic statistics using SAS. Topics include analysis of variance, experimental designs, analysis of covariance, mixed model, categorical data analysis, survey data analysis, sample size and power analysis, and model comparison. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-318
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DSCI 619 - Deep Learning
Credits: Three (3)
This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-419
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DSCI 624 - Data Visualization
Credits: Three (3)
This course is intended for students with introductory experience in SQL, R, and Excel. In this course students will learn how to connect SQL Server to tools like Excel and R and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating report-quality graphs and presentations. Students will learn how to use tools like R Notebooks so that their analysis follows the reproducible research paradigm. Finally, students will learn to create simple web applications in R Shiny to build reporting and analytics dashboards. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
Note: This course is for graduate students only.
Cross-listed: DSCI-324
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DSCI 625 - Blockchain
Credits: Three (3)
This course explores the fundamentals of the public, transparent, secure, immutable and distributed database called blockchain. Topics include applications to crypto currencies, such as keys, addresses, wallets, transactions, the blockchain mining and consensus, and network. Blockchains can be used to record and transfer any digital asset not just currency. Its potential impact on financial services, government, banking, contracting and identity management will also be discussed. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
Note: This course is for graduate students only.
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DSCI 697 - Thesis/Research
Credits: Three (3)
A master’s thesis is a piece of original scholarship written under the direction of a data science faculty advisor. Students need to write a data science academic paper in which a research question is developed and analyzed through original empirical and/or theoretical research, supplemented with a literature review. Students will do both a written final report and a presentation.
Note: This course is for graduate students only.
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DSCI 698 - Capstone Project
Credits: Three (3)
The Capstone Project course is for the students to apply the knowledge acquired during the Data Science program to a company project involving actual data in a realistic setting. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem.
Note: This course is for graduate students only.
Cross-listed: DSCI-498
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DVST 200 - Introduction to Diversity Studies
Credits: Three (3)
This course conceptualizes issues of diversity from a broad perspective. The course will examine matters related to race, ethnicity, culture, nationality, socioeconomic status, religion, sexual orientation, gender identity, and ability. Through an interdisciplinary social science lens, the course will examine several critical questions: How do we experience and understand diversity and difference? How do diversity and differences shape systems that affect individuals, families, communities, and society?
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DVST 475 - Leadership in Diversity Studies
Credits: Three (3)
This course is the culminating experience of the Diversity Studies Minor curriculum. As a capstone course, students are expected to deepen their appreciation for diverse communities and become more aware of issues facing their communities, themselves, and other various populations. Students are expected to fully engage the material in both an academic and personally meaningful manner.
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ECON 201 - Macroeconomics
Credits: Three (3)
This course studies the overall economic activity and growth of a nation. Topics include the basic model of supply and demand, national-income accounting, the determinants of national income and employment, the meaning and measurement of inflation and unemployment, business cycles, the economics of money and banking, and the role of monetary and fiscal policies in influencing economic activity.
General Education Area: Social Science
Prerequisite: ENGL-104 OR ENGL-204H, and MATH-116 or higher; Minimum grade C-
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ECON 202 - Microeconomics
Credits: Three (3)
This course studies price theory (or the laws of supply and demand)
the market system, the economics of consumer-behavior and
firm-behavior, market structures, and government regulation of
business.
General Education Area: Social Science
Prerequisite: ENGL-104 or ENGL-204H; and MATH-116 or higher; Minimum grade C-
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ECON 470 - International Trade & Finance
Credits: Three (3)
This course studies the economic principles involved in international trade and finance. It is designed to provide the student with the conceptual tools needed to analyze such international economic issues as import tariffs and quotas, import liberalization, loss of jobs to foreign countries, free-trade agreements, customs unions, monetary unions, and exchange-rate fluctuations. The general topics to be covered in this course include the pure theory of trade, the theory of trade policy (such as trade restrictions and economic integration), foreign-exchange markets and exchange rates, the international monetary system, and international economic institutions.
General Education Area: Social Science
Cross-listed: FIN-470
Prerequisite: ECON-201, ECON-202
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ECON 497 - Spec Study in Economics
Credits: One (1) to Four (4)
General Education Area: Social Science
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ECON 620 - Business Economics
Credits: Three (3)
This course studies how economic forces can affect a business. Topics from both micro and macroeconomics are included: the basic supply and demand model, market fluctuations, elasticity of demand and revenues, production costs and profits of a firm, measures of economic performance, national output and income, inflation and unemployment, fiscal policy and the governments budget, money and monetary policy, and special topics in economic policy.
General Education Area: Social Science
Prerequisite: ACCT-509
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ECON 697 - Special Studies
Credits: One (1) to Four (4)
General Education Area: Social Science
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EDHL 720 - International Perspectives in Higher Education
Credits: 3
This course will provide the opportunity to conduct in-depth analysis of international higher education institutions during a study trip to another country. Students will be introduced to the international and comparative divisions of higher education by examining how a country’s culture has affected the finances, governance, administrative structure, academic specialization and program delivery, student support/services, and enrollment underlying international higher education models.
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EDHL 730 - Dissertation Study
Credits: 0
This 16-week, zero-credit hour course providing candidates with a structured way to brainstorm and develop research ideas, as well as a collaborative way to explore and develop concepts and topics necessary for developing and writing the Dissertation in Practice research study.
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EDHL 731 - Research Design
Credits: 0
This 16-week, zero-credit hour course provides candidates with a structured way to begin synthesizing literature related to a problem in Higher Education Leadership practice and drafting sections of the Dissertation in Practice Proposal.
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EDHL 735 - Advanced Research Methods
Credits: Variable: 0.0 - 3.0
This 16-week, (0-3) credit hour course provides candidates with a structured way to continue development of the Dissertation in Practice Proposal.
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EDHL 736 - Research Writing
Credits: 0
This 16-week, zero-credit hour course provides candidates with a structured way to continue development of the Dissertation in Practice. Depending on their semester of study, students are responsible for the dissertation deliverables listed in the Dissertation in Practice Handbook.
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EDHL 737 - Research Seminar
Credits: 0
In this 16-week, zero-credit hour course, students will focus on completing the collection and analysis of the data from their research. Depending on their semester of study, students are responsible for the dissertation deliverables listed in the Dissertation in Practice Handbook.
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EDHL 738 - Writing for Publication
Credits: 0 - 3
In this 16-week, (0-3) credit hour doctoral-level writing course, students continue the collection and analysis of data and focus on the development of a manuscript for publication based on their dissertation research.
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EDHL 750 - Reflective Leadership Practice and Inquiry
Credits: Three (3)
This course introduces students to historical and contemporary foundations of higher education with specific focus on ethical leadership and reflective practice. Students will be introduced to educational inquiry and build knowledge and skills in academic research and writing.
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EDHL 751 - The College Student Experience
Credits: Three (3)
Students will become familiar with higher education leadership from the perspective of the student experience through study of student development theory, student services, student engagement, and through application of theory to practice in student affairs.
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EDHL 752 - Leadership in Higher Education
Credits: Three (3)
This course provides a general overview of various aspects of leadership in higher education including theories, multiple frames approach, strategic planning, and various decision-making models.
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EDHL 753 - Educational Research Methods
Credits: Three (3)
Students will be able to identify educational research methodologies and methods and understand the importance of research design and paradigms.
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EDHL 754 - The Competitive Context of Higher Education
Credits: Three (3)
The context of higher education is an increasingly complex and increasingly competitive environment. This course discusses the contemporary and future roles of enrollment management, the nature and scope of institutions in higher education, and constituency engagement.
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EDHL 755 - Research Residency I
Credits: Three (3)
Students will be immersed in dissertation research and writing through workshops, presentations, and critique groups.
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EDHL 756 - The Academic Community
Credits: Three (3)
In this comprehensive course students will develop strategies and leadership skills that will enable them to lead in an academic environment. Topics will include academic structure, governance, faculty tenure, emerging roles of faculty, and unions. Students will identify leadership roles in academic planning and curriculum development and will identify current and emerging issues in the academic environment in higher education.
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EDHL 757 - Organizational Leadership In Higher Education
Credits: Three (3)
Understanding the context of higher education prepares future leaders to confront most any issue if they have a grasp of organizational theory in higher education, power, conflict and crisis management, and organizational behavior.
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EDHL 758 - Understanding Data and Analysis
Credits: Three (3)
This course focuses on understanding the analysis of qualitative and quantitative data. Students will have the opportunity to learn different forms of analysis but can concentrate on the most appropriate type for their own study. Writing results and conclusions for a research study will also be discussed.
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EDHL 759 - Strategic Change and Innovation
Credits: Three (3)
Higher education is constantly evolving and changing. Future leaders must be prepared to understand, manage, and implement change. This course focuses on change models, barriers and resistance to change, innovation, and the future of higher education. Students will be exposed to the concept of design thinking as a tool for innovation in higher education.
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EDHL 760 - Research Residency II
Credits: Three (3)
The second residency includes presentations and workshops for advanced research and writing.
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EDHL 761 - Performance And Accountability
Credits: Three (3)
Leaders now and in the foreseeable future are held to many types of performance and accountability measures from different external and internal forces. This course discusses the role of assessment and data for decision-making, accreditation, and the basics of financial management.
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EDHL 762 - Leading in a Complex Environment
Credits: Three (3)
This course provides an overview of the key policy and legal issues in public and private higher education. Students will learn how policy, legal, and finance intersect and are integrated to solve complex problems.
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EDHL 763 - Dissertation Research And Writing
Credits: Three (3)
In this doctoral-level writing course, students focus on refinement of a manuscript for publication based on the student’s dissertation research. Students will revise and finalize their manuscript to meet their identified journal’s publishing standards.
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EDHL 764 - Portfolio and Oral Defense
Credits: Three (3)
This final course is to review and defend the comprehensive portfolio through a professional conversation and an oral presentation.
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