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COMM 605 - Digital Media Campaigns
Credits: 3
Designing and creating innovative digital media campaigns is a powerful process in contemporary society and an essential tool for helping organizations craft a public narrative about their value in society. In this course, students will learn to evaluate organizational digital media efforts, create and design digital media campaigns for a variety of purposes, and provide recommendations to organizations about how to improve their digital media efforts. Additionally, students in this course will be able to articulate, find, and digest the latest academic research that lies at the intersection of how technology is impacting organizations.
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COMM 605 - Digital Media Campaigns
Credits: 3
Designing and creating innovative digital media campaigns is a powerful process in contemporary society and an essential tool for helping organizations craft a public narrative about their value in society. In this course, students will learn to evaluate organizational digital media efforts, create and design digital media campaigns for a variety of purposes, and provide recommendations to organizations about how to improve their digital media efforts. Additionally, students in this course will be able to articulate, find, and digest the latest academic research that lies at the intersection of how technology is impacting organizations.
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COMM 610 - Digital Mkt Metrics & Analytics
Credits: 3
This course provides an intensive examination of a variety of digital media metrics and analytics necessary to measure and evaluate marketing communication campaigns. Furthermore, it provides an evidence based approach to digital marketing that can measure an organization’s return on their marketing investments. Graduate students will learn practical, up to date tools that will help in analyzing, creating, and measuring effective digital marketing campaigns.
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COMM 620 - Digital Innovation
Credits: 3
This course lives in the intersection of emerging technologies and social media platforms to inform digital transformation. Topics covered in this course include blockchain, artificial intelligence, Web3, augmented reality, the metaverse, and more. Students will critically evaluate new technologies’ role in social media through the lens of cultural, economic, philosophical, psychological, and more. This will be accomplished through critical thinking exercises as well as hands-on applications with emerging technology. Finally, students will complete a comprehensive research report on the emerging technology of their choice and its relationship with social media.
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COMM 630 - Digital Visual Communication
Credits: 3
The objective of this course is to educate students on how to use and understand photography as a tool in communication. Students will learn how to create professional photographs with their iPad or Digital Camera and grasp an understanding on how to read those photographs from an objective lens. There will be a survey of topics ranging from photojournalism ethics to fashion model objectification that will widen one’s appreciation of how photography changes society for the good and bad. The course will be deep rooted in photography theory and critical thinking. Students will graduate with the basic skills of an entry level photographer and have a portfolio of images to illustrate their competency. The experience will accumulate in a final photo-thesis where the student will argue with media theory (Punctum and Studum Theory) why the images they created are powerful.
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COMM 673 - Social Network and Search Engine Optimization
Credits: 3
Develop knowledge, understanding and concepts related to direct and web marketing techniques, tactics, and strategies that enable a manager to make sound business decisions. Topics include web media and databases, target audiences, forecasting and cost control, research, and testing of creative strategies.
Cross-listed: MKT-673
Prerequisite: COMM-501 or MKT-660
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COMM 685 - Capstone: Comprehensive Examination
Credits: 3
Comprehensive examination must be taken in the last semester of coursework before graduation.
Note: This course is only offered Fall and Spring semesters.
Prerequisite: Students cannot take this class until they have complete the other seven core classes (501, 502, 503, 522, 520, 525, and 523).
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COMM 697 - Special Topics
Credits: 3
These courses are offered periodically based on the interests of our students and faculty. More information can be requested from the department.
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COMM 698 - Strategic Communication Capstone
Credits: 3
Students will choose between a thesis and a project. Thesis: Students completing a thesis will identify a topic on which to conduct original research with the goal of adding to the theoretical body of knowledge in strategic communication. Project: Students completing a project will use applied research and theory to construct a comprehensive plan to address a problem/opportunity in a selected setting, such as the student’s place of work or an organization or cause the student is interested in.
Prerequisite: Program Director Approval Required
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COSC 130 - Introduction to Programming
Credits: 3
This course covers data types, statements, expressions, control flow, functions, and object oriented programming. It emphasizes principals of software development, debugging, and testing. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
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COSC 140 - Introduction to Blockchain
Credits: 3
This course provides a foundational understanding of blockchain to students. The idea of what blockchain is, why it is needed, and the problems it solves is covered. An overview of how blockchain technology works and is developed is covered as well as the structure of these technologies. The potential as well as the limitations of blockchain is reviewed as well as how these limitations can be overcome.
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COSC 150 - Introduction to Java Programming
Credits: 3
This course introduces students to fundamental features of Java programming language. Topics include data types, control flow and loops, objects, classes, encapsulation, inheritance, and polymorphism.
Note: No previous programming experience is required.
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COSC 151 - Computer Science I
Credits: 3
This is an introduction to computer programming in C++ language. The course covers structural programming concepts, simple data types and algorithms in addition to basic C++ syntax, operators, control structures, arrays, pointers, function parameter passing, and object programming. Projects are required for coding techniques, program design, and debugging.
Prerequisite: COSC-130, COSC-150, or DSCI-303
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COSC 152 - Computer Science II (C++OOP)
Credits: 3
This course covers the concepts of encapsulation and Object Oriented Programming. Topics include the OOP fundamentals such as inheritance, polymorphism, and, in addition, overloading and exception handling. OOP programming environment in JAVA may be presented at the later part of the course.
Note: Lab assignments are required at the intermediate level of OOP design.
Prerequisite: COSC-151
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COSC 160 - Cloud Computing Foundations
Credits: 1
This course provides students with an introduction to general concepts relating to cloud computing. Topics include cloud-computing principles and best practices, user and role management, security, cloud architecture, networking, and pricing models. Students will receive hands-on experience with using AWS to create and manage cloud-based services.
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COSC 220 - Database Applications
Credits: 3
This course covers the practical application and usage of database systems. An emphasis is placed on relational databases, but non-relational databases are introduced as well. Topics include database design and architecture, the SQL language, data storage, database programming, and NoSQL databases.
Prerequisite: COSC-130, COSC-150, or DSCI-303
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COSC 231 - Project Management
Credits: 3
This course covers the project management methodologies. Topics covered include: project planning, quality management, time and cost management, agile, waterfall, and risk management.
Prerequisite: COSC-151 and COSC-220
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COSC 290 - Blockchain Networks
Credits: 3
This course presents students an understanding of the different types of blockchain networks including, but not limited, public, private, consortium, and permissioned networks. The user of blockchain networks and the value that can be brought to businesses, industry sectors, and society are explored.
Prerequisite: COSC-140
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COSC 299 - Internship
Credits: 3
Internship course is designed for mathematics, actuarial science, computer science and data science students to integrate the academic to the appropriate science profession through internship experiences. Students will work on internship projects under the employer supervisor and research projects under the guidance of faculty in mathematics and computing sciences.
Cross-listed: ACSC-299
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COSC 305 - Blockchain Cryptocurrencies
Credits: 3
This course explores the basic properties of various cryptocurrencies (e.g bitcoin, ethereum, ether). Cryptography techniques will be explored to gain a fundamental understanding of the mechanics behind cryptocurrencies.
Prerequisite: COSC-290
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COSC 315 - Ethics and Legal Aspects of Blockchain
Credits: 3
This course explores the ethical and legal hurdles and consequences of implementing blockchain technologies. Analyzing historical and contemporary case studies, this course allows for an exploration of blockchain across industries for the implementation and scaling of blockchain technology. Current events involving blockchain in the legal system, corporate world, and the public sphere will be addressed and examined.
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COSC 350 - Data Structures and Algorithms
Credits: 3
This course studies the design and implementation of data structure and algorithms. Topics include applications of data structures such as stacks, queues and linked-lists, analysis of algorithms, and algorithmic tools and techniques, including sorting and searching methods. Requires substantial object programming projects to solve real world problems using these data structure and algorithms.
Prerequisite: MATH-117 or higher (course must be algebra based) & COSC-151
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COSC 360 - Operating Systems
Credits: 3
Exposure to Windows, Linux, and Unix Operating systems. The course covers the theoretical aspects of operating systems including system structures, scheduling, threads and concurrent processes, deadlock detection and prevention, storage and file management, virtual memory, system protection, and security.
Prerequisite: MATH-117 or higher (course must be algebra based), COSC-151
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COSC 375 - Applications of Blockchain Technology
Credits: 3
This course allows students to put into practice blockchain principles with the development of a blockchain application. This course also covers the different uses of blockchain across industries including cryptocurrencies, smart contracts, and ledgers.
Prerequisite: COSC-290
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COSC 399 - Internship
Credits: 3
Internship course is designed for mathematics, actuarial science, computer science and data science students to integrate the academic to the appropriate science profession through internship experiences. Students will work on internship projects under the employer supervisor and research projects under the guidance of faculty in mathematics and computing sciences.
Cross-listed: ACSC-399
Prerequisite: ACSC-299 or COSC-299
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COSC 405 - Scripting for Games and Apps
Credits: 3
Students will create working models of video games / apps within industry standard (special-case programming) scripting environments. They will apply fundamental programming vocabulary, logic, flow, and principles in the pursuit of understanding a key element of game design. This course is targeted towards students who are not experienced programmers.
Cross-listed: ADIN-405
Prerequisite: ADIN-300
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COSC 420 - Database Design
Credits: 3
This course continues the study of database systems began in COSC 220. It explores more advanced and theoretical aspects of database design. Topics covered in the course include: relational algebra, design theory and normalization, SQL programming techniques and web database programming using PHP, semi-structured data, NoSQL Database and big data storage systems, triggers, views, and transaction processing.
Prerequisite: COSC-220
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COSC 421 - Introduction to Robotics
Credits: 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: COSC-130, COSC-150, or DSCI-303
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COSC 423 - Computer Vision
Credits: 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 or DSCI-303
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COSC 425 - Applications in Game Design
Credits: 3
Students will produce playable game applications as they pursue advanced understanding of the tools, principles, and processes of game design. Students will demonstrate skill and knowledge in utilizing game engines, scripting principles, assessing program performance, and balancing gameplay.
Cross-listed: ADIN-425
Prerequisite: ADIN-405 or COSC-405
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COSC 427 - Advanced Game Development
Credits: 3
Students will produce playable game scenarios as they pursue an advanced understanding of game development tools, principles, and processes. Utilizing an industry-standard game engine, students seek to build skill and knowledge in developing cutting-edge programming scenarios focusing on programming structure, application performance, and gameplay innovation.
Prerequisite: ADIN-425
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COSC 435 - Introduction to Reinforcement Learning
Credits: 3
This course provides an introduction to the field of Reinforcement Learning, with an emphasis on Deep Reinforcement Learning. Topics cover may include, but are not limited to: Markov decision processes, value-based methods, Deep Q-networks, policy-gradient methods, actor-critic algorithms, and multi-agent problems.
Prerequisite: DSCI-408
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COSC 440 - Artificial Intelligence I
Credits: 3
This course provides an introduction to the field of Artificial Intelligence. Topics covered may include, but are not limited to: History of Artificial Intelligence, logic, game theory, search algorithms, knowledge representation, and automated planning
Prerequisite: DSCI-303
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COSC 441 - Artificial Intelligence II
Credits: 3
This course continue the study of the field of Artificial Intelligence. Topics covered may include, but are not limited to: Markov decision processes, evolutionary algorithms, probabilistic reasoning, multi-agent problem-solving, natural language processing, computer vision, and robotics.
Prerequisite: COSC-440
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COSC 443 - Ethics of Artificial Intelligence
Credits: 3
This course introduces students to a range of potential ethical issues related to the current and future use of artificial intelligence. Topics discussed will include the role of artificial intelligence in society, as well as the use of artificial intelligence in areas such as manufacturing, finance, healthcare, government, and law enforcement.
Prerequisite: COSC-440
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COSC 445 - Applications of Artificial Intelligence
Credits: 3
This course is a survey of some of the current and possible future applications of artificial intelligence. The course will explore applications in fields such as business, transportation, manufacturing, healthcare, cybersecurity, and geospatial analysis.
Prerequisite: COSC-440
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COSC 497 - Special Topics in Advanced and Emerging Topics in Blockchain
Credits: 3
This course explores the ethical and legal hurdles and consequences of implementing blockchain technologies. Analyzing historical and contemporary case studies, this course allows for an exploration of blockchain across industries for the implementation and scaling of blockchain technology. Current events involving blockchain in the legal system, corporate world, and the public sphere will be addressed and examined.
Prerequisite: COSC-290
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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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COSC 499 - Internship
Credits: 3
Internship course is designed for mathematics, actuarial science, computer science and data science students to integrate the academic to the appropriate science profession through internship experiences. Students will work on internship projects under the employer supervisor and research projects under the guidance of faculty in mathematics and computing sciences.
Cross-listed: ASCS-499
Prerequisite: COSC-399 or ACSC-399
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COSC 521 - Robotics
Credits: 3
This course provides an 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-508
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COSC 523 - Image Processing
Credits: 3
This course covers methods that allow a machine to analyze, learn from, and make images from image and video data. It covers a range of image processing techniques and applications, such as image sampling, noise reduction, transformation, feature extraction, image classification, image segmentation, and keypoint detection.
Prerequisite: DSCI-503
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COSC 550 - Data Structures
Credits: 3
This course studies the design and implementation of data structure. Topics include object-oriented programming, basic C++ syntax, pointers, arrays, linked-lists, stacks, queues, hash tables, binary trees, graphs, and searching algorthms.
Prerequisite: DSCI-503
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COSC 599 - Internship
Credits: 3
Internship course is designed for mathematics, actuarial science, computer science and data science students to integrate the academic to the appropriate science profession through internship experiences. Students will work on internship projects under the employer supervisor and research projects under the guidance of faculty in mathematics and computing sciences.
Cross-listed: ACSC-599; DSCI-599
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COSC 635 - Reinforcement Learning
Credits: 3
This course provides an introduction to the field of Reinforcement Learning, with an emphasis on Deep Reinforcement Learning. Topics cover may include, but are not limited to: Markov decision processes, value-based methods, Deep Q-networks, policy-gradient methods, actor-critic algorithms, and multi-agent problems.
Prerequisite: DSCI-508
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COSC 640 - Fundamentals of Artificial Intelligence
Credits: 3
This course provides an introduction to the field of Artificial Intelligence. Topics covered may include, but are not limited to: History of Artificial Intelligence, logic, game theory, search algorithms, knowledge representation, and automated planning.
Prerequisite: DSCI-503
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COSC 641 - Advanced Artificial Intelligence
Credits: 3
This course continue the study of the field of Artificial Intelligence. Topics covered may include, but are not limited to: Markov decision processes, evolutionary algorithms, probabilistic reasoning, multi-agent problem-solving, natural language processing, computer vision, and robotics.
Prerequisite: COSC-640
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COSC 643 - Ethics of Artificial Intelligence
Credits: 3
This course introduces students to a range of potential ethical issues related to the current and future use of artificial intelligence. Topics discussed will include the role of artificial intelligence in society, as well as the use of artificial intelligence in areas such as manufacturing, finance, healthcare, government, and law enforcement.
Prerequisite: COSC-640
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COSC 645 - Applications of Artificial Intelligence
Credits: 3
This course is a survey of some of the current and possible future applications of artificial intelligence. The course will explore applications in fields such as business, transportation, manufacturing, healthcare, cybersecurity, and geospatial analysis.
Prerequisite: COSC-640
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COSC 699 - Internship
Credits: 3
Internship course is designed for mathematics, actuarial science, computer science and data science students to integrate the academic to the appropriate science profession through internship experiences. Students will work on internship projects under the employer supervisor and research projects under the guidance of faculty in mathematics and computing sciences.
Cross-listed: ACSC-699, DSCI-699
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CRIM 102 - Introduction to Criminal Justice
Credits: 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 203H - Sexual Violence and Aggression
Credits: 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.
General Education Area: Social Science
Cross-listed: WGS-203H
Prerequisite: Membership in Bascom Honors Program
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CRIM 210 - Multicultural Policing
Credits: 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: 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: 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 224H - Detective Fiction
Credits: 4
The seminar will study the origin of detective fiction and the cultural and scientific circumstances which made the genre possible. Secondly, the course will explore the reasons behind the popularity of this kind of fiction for the last two centuries. The course will also examine the development of forensic science and detecting by studying the evolution of police investigation procedures from the 19th century to then present day. Students will read a variety of detective stories beginning with Edgar Allan Poe and Arthur Conan Doyle ending this study in the 21s century.
General Education Area: Humanities, Social Science
Cross-listed: ENGL-224H
Prerequisite: Membership in Bascom Honors Program
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CRIM 297 - Special Studies
Credits: 1-4
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: 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
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CRIM 311 - Criminal Law and Procedure
Credits: 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: 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: 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: 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: 3
This course explores the theoretical underpinnings that attempt to explain and predict criminal behavior and human aggression, including investigation of biological, cognitive, social and other explanations. Additionally, the course investigates juvenile delinquency, psychopathy, mental illness, mass murder, sex offending, terrorism, white collar crime and the unique features and profiles of each.
Cross-listed: PSYC-326, SOC-326
Prerequisite: SOC-101, SOC-202H, PSYC-101, PSYC-202H, or CRIM-102
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CRIM 397 - Special Studies
Credits: 1-4
These courses are offered periodically based on the interests of our students and faculty.
Prerequisite: SOC-101, SOC-202H, or CRIM-102
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CRIM 405 - Domestic and International Terrorism
Credits: 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-405
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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 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: 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-202H, SOC-101, or SOC-202H
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CRIM 496 - Independent Study
Credits: 1-4
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: 1-4
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: 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 or higher
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DSCI 201 - Math Modeling-Excel
Credits: 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 or higher
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DSCI 297 - Special Studies
Credits: 1-3
These courses are offered periodically based on the interests of our students and faculty. More information can be requested from the department.
Prerequisite: Permission of Program Director
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DSCI 301 - Math Modeling-VBA
Credits: 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 or higher
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DSCI 302 - Introduction to R
Credits: 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.
Related Courses: DSCI-502
Prerequisite: MATH-117 or higher
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DSCI 303 - Introduction to Python
Credits: 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.
Related Courses: DSCI-503
Prerequisite: MATH-117 or higher
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DSCI 304 - Introduction to SQL
Credits: 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.
Related Courses: DSCI-504
Prerequisite: MATH-117 or higher
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DSCI 307 - SAS Programming
Credits: 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.
Related Courses: 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.
Related Courses: DSCI-614
Prerequisite: DSCI-303
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DSCI 318 - Experimental Design
Credits: 3
This course covers principles of experiments and basic statistics using R. 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.
Related Courses: DSCI-618
Prerequisite: DSCI-302
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DSCI 324 - Data Visualization
Credits: 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, 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.
Related Courses: 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: 1-3
These courses are offered periodically based on the interests of our students and faculty. More information can be requested from the department.
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DSCI 408 - Machine Learning
Credits: 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).
Related Courses: 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.
Related Courses: DSCI-609
Prerequisite: DSCI-302
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DSCI 412 - Predictive Modeling
Credits: 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.
Related Courses: DSCI-512
Prerequisite: DSCI-302 or DSCI-303
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DSCI 417 - Big Data Analytics
Credits: 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.
Related Courses: DSCI-617
Prerequisite: DSCI-303
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DSCI 419 - Deep Learning
Credits: 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.
Related Courses: DSCI-619
Prerequisite: DSCI-408
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DSCI 498 - Capstone Project
Credits: 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.
Related Courses: DSCI-598
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DSCI 501 - Math Modeling
Credits: 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: 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.
Related Courses: DSCI-302
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DSCI 503 - Python
Credits: 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.
Related Courses: DSCI-303
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DSCI 504 - SQL
Credits: 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.
Related Courses: DSCI-304
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DSCI 507 - SAS Programming
Credits: 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.
Related Courses: DSCI-307
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DSCI 508 - Machine Learning
Credits: 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.
Related Courses: DSCI-408
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DSCI 512 - Predictive Modeling
Credits: 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.
Related Courses: DSCI-412
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DSCI 597 - Special Studies
Credits: 1-3
These courses are offered periodically based on the interests of our students and faculty. More information can be requested from the department.
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DSCI 598 - Capstone Project
Credits: 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.
Related Courses: DSCI-498
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DSCI 599 - Data Science Internship
Credits: 3
Internship course is designed for mathematics, actuarial science, computer science and data science students to integrate the academic to the appropriate science profession through internship experiences. Students will work on internship projects under the employer supervisor and research projects under the guidance of faculty in mathematics and computing sciences.
Cross-listed: COSC-599, ACSC-599
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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.
Related Courses: DSCI-409
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