Data Science Course Descriptions
Scroll down to read descriptions of the data science courses offered at Carthage, or click on these links for additional resources:
Data Science I (QR)
CSC 1030 / 4 credits
This class introduces students to the foundational skills needed for data analysis: data manipulation and visualization, statistical summarization, and problem-solving using data. No prior programming experience is needed, and students will become proficient at writing code in a modern computer environment.
Offered in Fall/Spring
Data Science II
CSC 1040 / 4 credits
This class introduces students to the data structures and algorithms needed for complicated data analysis tasks. No prior programming experience is needed, and students will learn principles of computer science that will benefit them in future programming endeavors.
Prerequisite: CSC 1030 with a grade of C- or better
Offered in Spring
Calculus I (MTH)(QR)
MTH 1120 / 4 credits
This course is a study of coordinate systems, straight lines and conic sections, theory of limits, differentiations of algebraic functions, applications to slopes and curves, and maxima and minima.
Prerequisite: MTH 1070 with a grade of C- or better, or high school preparation
Offered in Fall/Spring
Linear Algebra (MTH)
MTH 2040 / 4 credits
An examination of linear equations, matrices, vector spaces, transformations, and eigensystems.
Prerequisite: MTH 1120
Offered in Fall/Spring
Mathematics of Data Science (QR)
MTH 3090 / 4 credits
This class dives deeper into the data science process by studying the mathematical foundations of common data science methods and techniques. Methods include linear regression, classification models, and clustering. Techniques include generalized study of functions, best practices for handling data, optimization, and analyzing error measures.
Prerequisite: CSC 1110 or CSC 1810
Introduction to Computing (QR)
CSC 1100 / 4 credits
An introduction to the art and science of computer programming for the student without previous programming experience. Topics covered include the historical development of computing, the basic operating principles of computers, and an introduction to problem-solving using one or more high-level computing languages, such as Python. Intended for nonmajors/nonminors. Does not count toward major or minor in CSC.
Offered in Fall/Spring
Principles of Computer Science
CSC 1810 / 4 credits
A study of the fundamentals of writing computer programs and problem-solving, using structured and object-oriented techniques. Intended for future majors and minors in Computer Science and minors in Game Development. Students are strongly encouraged to enroll in this course in the Fall term of their first year
Offered in Fall/Spring
Business Geographics and Data Visualization (QR)
BUS 2150 / 4 credits
The course focuses on the visual display of quantitative information in a business or organizational context. Students will use advanced software technology to summarize data visually for better business decision-making, increased organizational efficiency, and effective organizational planning.
Database Design and Management
CSC 2810 / 4 credits
An introduction to database methods including data models (relational, object-oriented, network, and hierarchical); database design and modeling; implementation and accessing methods; and SQL. Students will design and implement a database using a database management system.
Prerequisite: CSC 1820 with a C- or higher
Ethics in Data Science
MTH 2190 / 4 credits
This course will help students think critically about the complex ethical issues arising in technological fields. Students will learn some of the indicators of bias hidden by technology, analyze recent cases of ethical misconduct involving big data, and will learn to perform a contextual risk-benefit analysis of implementing.
Prerequisite: CSC 1030
Offered in J-Term
Business Ethics (HUM)(CL)
BUS 2110 / 4 credits
In this course, students explore major ethical issues arising in the practice of business and learn to apply various methods of ethics in solving these problems. Whistle-blowing, insider trading, employees’ rights, multinational corporations, and other topics are discussed.
Data Science Portfolio I
MTH 3350 / 1 credit
This is the first course in a two-course sequence in which students develop, propose, and refine their three-course applied sequence in data science; learn about the concept of a data science portfolio; and begin the process of planning and assembling their data science portfolio. Students will also engage in career and professional development activities.
Prerequisites: CSC 1040 and MTH 1050; MTH 3050, BUS/ECN 2340, or EXS 2330; or instructor approval
Offered in Spring
Data Science Portfolio II
MTH 4350 / 1 credit
This is the second and final course in the data science portfolio sequence. In this class, students will assemble and present their data science portfolio. In addition, students will reflect upon the learning goals of their threecourse sequence and articulate how those learning goals were accomplished. Students will participate in professional and career development activities.
Prerequisite: MTH 3350 or instructor approval
Offered in Fall
Senior Thesis Completion
MTH 4990 / 0 credits
Students should register for MTH 4990 during the semester in which they plan to complete their Senior Thesis.
Elementary Statistics (MTH)(QR)
MTH 1050 / 4 credits
Methods of determining averages, variability, hypothesis testing and correlation, and of testing the significance of the statistics, prediction, and distribution-free statistics. A student may not receive credit for this course after receiving credit for any other statistics course without approval of the Mathematics Department chair.
Offered in Fall/Spring
Statistics (MTH)
MTH 3050 / 4 credits
Data collection and analysis; continuous and discrete distributions, central limit theorem, sampling theory, confidence intervals and estimation theory, regression analysis and correlation including multiple linear regression models and hypothesis testing and confidence intervals in regression models, chisquare test of independence and other nonparametric statistical tests, time series models and forecasting, linear time series models, moving average and autoregressive models, estimation, data analysis, index numbers, forecasting with time series models, forecasting errors and confidence intervals, and application of statistics to significant real-world data.
Prerequisites: MTH 1050 and MTH 1220 or instructor approval
Offered in Spring
Applied Statistics for Health and Human Services (MTH)
EXS/NSG 2330 / 4 credits
This course presents a practical approach to utilizing statistics in situations encountered in the Health and Human Services professions. Fundamental statistical theories and concepts are presented to help students understand the rationale and purpose of using statistical computations. Basic parametric statistical analyses, as well as the mathematical logic behind these calculations, will be presented. Students will learn how to perform hypothesis testing with normal distributions and also learn to interpret and critically evaluate research outcomes. This knowledge will allow students to be evidence-based practitioners and critical consumers of peer-reviewed research.
Prerequisite: Junior standing and accepted Nursing majors or declared Exercise and Sport Science or Allied Health majors.
Artificial Intelligence and Cognitive Modeling
CSC 3530 / 4 credits
This course explores the primary approaches for developing computer programs that display characteristics we would think of as being intelligent. Students will analyze how intelligent systems are developed and implemented with a focus on exploring how human behavior on cognitive tasks can be used to inform the development of these artificial systems, as well as how the performance and behavior of these artificial systems can inform our understanding of human cognition.
Prerequisite: CSC 2560 with a C- or higher or with permission of instructor
Introduction to Econometrics (SOC)
ECN 3340 / 4 credits
Econometrics is a set of tools researchers use to estimate relationships between variables, test theories, and make forecasts, all using real-world data. Econometric analysis supports decision-making in public policy, business, the court system, and academia. This course provides a rigorous introduction to econometrics, with a particular emphasis on multiple regression analysis. Topics include formulating good research questions; estimating regression models using cross-section, time series, and panel data; conducting hypothesis tests; and interpreting and critically evaluating published regression results.
Prerequisite: BUS/ECN 2340, MTH 1050, or MTH 1055
Offered in Spring
Introduction to Geographic Information Science: Mapping Your World (NLAB)(SE)(QR)
GEO 1610 / 4 credits
This course provides an introduction to portraying spatial data and making data maps for a variety of applications. Students work in a hands-on lab/lecture setting while exploring computer mapping production techniques: cartographic design, communication properties of thematic maps, data selection and quality, and the problems of graphic display in print and electronic formats. Students will apply the course material by completing a variety of mapping projects. Students need no specialized computer skills to enter the course, but they will be expected to manipulate data and maps using the computer methods discussed in class.
Offered in Fall/Spring
Advanced Geographic Information Science and Analytical Cartography (NLAB)(QR)
GEO 2610 / 4 credits
This course explores advanced problems and techniques in both raster and vector systems. Topics include scientific visualization of problems, layer overlays, distance measurement and transformation, data management, creation and analysis of statistical surfaces, geographic pattern analysis, and data quality. Students will apply the course material by performing a variety of analyses on different types of geographic data.
Prerequisite: GEO 1610 or consent of the instructor
Internet Mapping and Web GIS (NLAB)
GEO 1210 / 4 credits
The Web GIS (geographic information systems) revolution is radically altering how spatially explicit information about the world around us is consumed, applied, and shared. This course aims to enable students from diverse academic backgrounds and interests to (1) search, retrieve, and visualize geographically referenced data using a wide variety of general purpose, government, and specific-purpose web maps and apps; (2) use ESRI ArcGIS Online, Business Analyst Online, and Community Analyst to find geospatial data, create multilayered thematic maps, and conduct spatial analyses; and (3) build their own web apps, story maps, or geo-enabled mobile apps, through individual as well as group-based projects. Students need no specialized computer skills to enter the course, but they will be expected to manipulate data and maps using the computer methods discussed in class.
Introduction to Business Analytics (QR)
MGT 3100 / 4 credits
A survey of the mathematical models of Management Science and Operations Research (such as linear programming, queuing theory, decision analysis, and simulation) applied to managerial decision-making.
Prerequisites: BUS/ECN 2340, SWK 2330, GEO 2900, MTH 1050, or MTH 3050 and sophomore standing or higher.