The undergraduate Data Analytics degree at MacMurray College provides a blend of math, statistics, computer science, business, and science courses to address complex issues, develop solutions for real-world problems, and develop a deeper understanding of human activity. The last ten years have yielded an explosion in the availability of data extracted from traces of human behavior, including smart phones, search engine queries, internet transactions, consumer behavior, or social media. These very large datasets are an incredible resource for businesses, enabling them to test new hypotheses and study phenomena on a previously unprecedented scale.
The undergraduate Data Analytics major at MacMurray College will teach students how to record, organize, analyze, and interpret data through the usage of statistics and various research methods. Learning how to gather, organize, analyze, and interpret data can be an important skill in a wide variety of careers, such as in business, health administration, social or natural science-related research, marketing, advertising, and public policy.
Students can be expected to achieve the following learning outcomes.
- Define and explain the key concepts and models relevant to data science.
- Design, implement, and evaluate the experimental design, data collection, mining, analysis, and the presentation and communication of information derived from large datasets to broad audiences.
- Knowledge of how to apply analytic techniques, including statistical and data mining approaches, to large public and private data sets to extract meaningful insights.
- Acquisition of hands-on experience with relevant software tools, languages, and data file construction to solve practical problems in data analytics.
- Develop team skills to ethically research, develop, and evaluate analytic solutions to improve individual and project outcomes.
Data Analytics majors are required to successfully complete ACCT 221 and 222; BUSA 223, 301, 316, and 400; ECON 210 and 220; FINC 345; MGMT 317; MARK 330; MATH 131 and 135; and PSYC 221 and 222. Majors will also complete DATA 208, 211, 218, 228, 238, 358 and 393.
DATA 208. Foundations of Data Analytics. (3) The emergence of new data sources is transforming the role of the data analyst from one who simply reports information to one who is charged with making sense of the available data and distilling from it the salient aspects for the given audience. In this course, students will examine the concepts of data analysis and how it informs the business process. Emphasis will be placed on the development of sound research questions; the identification and verification of data sources; the retrieval, cleaning, and manipulation of data; and the process for identifying the data elements that are relevant for a given audience. An overview of the regulatory organizations that govern the release of data will also be reviewed.
DATA 211. Introduction to Logic. (3) A study of the techniques of critical thinking with the aim of making logic a tool for reasoning in everyday life and in the student's academic discipline. Emphasis will be on practical exercises in reasoning and in detecting logical fallacies. No prerequisite. Cross-listed with PHIL 211.
DATA 218. Fundamentals of Data Mining. (3) A large portion of data analytics focuses on identifying meaningful patterns in data. Using a case studies approach, students will examine effective strategies that blend both hypothesis testing and data-driven discovery methods to identify meaningful data patterns and apply that knowledge to common business problems. Emphasis will be placed on data-mining tasks such as classification, clustering, and sequential pattern discovery. Prerequisite: DATA 208.
DATA 228. Advanced Statistics: Regression Analysis and Predictive Analytics. (3) This is a second course in statistics that builds upon knowledge gained in PSYC 221. Students will learn to build statistical models and implement regression analysis in real-world problems from engineering, sociology, psychology, science, and business. Topics include multiple regression models (including first-order, second-order, and interaction models with quantitative and qualitative variables), regression pitfalls, and residual analysis. Students will gain experience not only in the mechanics of regression analysis (often by means of a statistical software package) but also in deciding on appropriate models, selecting inferential techniques to answer a particular question, interpreting results, and diagnosing problems. Prerequisites: MATH 121 and 131 and PSYC 221 with a grade of C or better.
DATA 238. Introduction to Statistical Analysis System. (3) The SAS programming suite of products is commonly used throughout the industry for making sense of the vast amount of data that is available today and for turning that data into actionable items for an organization. Through the creation of SAS programs of varying complexity, students will solve common data analysis problems and learn the general programming conventions of SAS along with the data management and reporting utilities of the basic SAS product. This course will also provide students with an overview of the wide array of SAS data analytics products and their use within various industries. Prerequisites: MATH 121 and PSYC 221 with a grade of C or better.
DATA 348. Sports Analytics and Data Visualization. (3) Introduction to data collection and analytic techniques through the study of sports. The class will discuss theory, development, and application of analytics in sports. General topics include data mining, data cleaning, and visualization of large data sets. Students will have the opportunity to conduct analyses of their own design. Prerequisites: MATH 121 and 121 and PSYC 221 with a grade of C or better.
DATA 358. Introduction to Geographic Information Systems. (3) This course is designed to introduce the student into the exciting new world of mapping software. Mapping software has found many uses throughout government, universities, and business, as well as in the public policy arena. Students will learn how to work with different kinds of data sets and how to incorporate them into customized maps for analysis and presentation.