Scroll down to read descriptions of the data science courses offered at Carthage, or click on these links for additional resources:

CSC 1030

Data Science I

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 computing environment.

CSC 1040

Data Science II

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

MTH 1120

Calculus I

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 or high school preparation

MTH 2040

Linear Algebra (WI)

An examination of linear equations, matrices, vector spaces, transformations, and eigensystems.
Prerequisite: MTH 1220

MTH 3090

Mathematics of Data Science

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 1810

CSC 1100

Introduction to Computing

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.

CSC 1810

Principles of Computer Science

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 call in the Fall term of their first year.

BUS 2150

Business Geographics and Data Visualization

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 decisionmaking, increased organizational efficiency, and effective organizational planning.

CSC 2810

Database Design and Management

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

MTH 2190

Ethics in Data Science

This course is designed to help students prepare for a career in the actuarial sciences, and to help students learn material covered on the first actuarial examination. Topics will include limits, series, sequences, derivatives of single and multivariate functions, integrals of single and multivariate functions, general probability, Bayes’ theorem, univariate probability distributions, and multivariate probability distributions.
Prerequisites: MTH 1220 and MTH 1240 or instructor approval

BUS 2110

Business Ethics (HUM)

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. Whistleblowing, insider trading, employees’ rights, multinational corporations, and other topics are discussed.

MTH 3350

Data Science Portfolio I

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

MTH 4350

Data Science Portfolio II

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

MTH 4990

Senior Thesis Completion

Students should register for MTH 4990 during the semester in which they plan to complete their Senior Thesis.

MTH 1050

Elementary Statistics

Methods of determining averages, variability, hypothesis testing, correlation, and of testing the significance of the statistics, prediction, and distribution-free statistics. A student may not receive credit for Elementary Statistics after receiving credit for any other statistics course.

MTH 3050


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 realworld data.
Prerequisites: MTH 1050 and MTH 1220 or instructor approval
Spring of odd-numbered years

BUS/ECN 2340

Business Statistics

The application of statistics to problems in business and economics, encompassing the gathering, organization, analysis, and presentation of data. Topics include descriptive statistics in tabular and graphical forms, the common measures of central tendency and dispersion, sampling and probability distributions, construction of confidence intervals and hypothesis testing, and correlation analysis. This course is offered as BUS 2340 or ECN 2340. Students who earned less than 20 on the math component of the ACT (or equivalent on the SAT) are encouraged to take a math class to strengthen their preparation for this class.

EXS/NSG 2330

Applied Statistics for Health and Human Services

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.

CSC 3530

Artificial Intelligence and Cognitive Modeling

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

ECN 3340

Introduction to Econometrics (SOC)

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 crosssection, time-series, and panel data; conducting hypothesis tests; and interpreting and critically evaluating published regression results.
Prerequisite: BUS/ECN 2340

GEO 1610

Introduction to Geographic Information Science: Mapping Your World (NLAB)

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.

GEO 2610

Advanced Geographic Information Science and Analytical Cartography (NLAB)

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

GEO 1210

Internet Mapping and Web GIS (NLAB)

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 geoenabled 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.

MGT 3100

Introduction to Business Analytics

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 decisionmaking.
Prerequisites: BUS/ECN 2340, SWK 2330, GEO 2900, MTH 1050, or MTH 3050 and sophomore standing or higher.