A Catalog of Data Intensive Designated Courses at Elon University

List of Data Intensive Designated Courses

  1. BUS 2110 – Management Information Systems. This course uses various examples of data from the business world and teach students how to use Excel and Tableau. Students will prepare data for analysis and perform descriptive analytics techniques to answer Data Driven Opportunities. In addition, students are taught skills needed to obtain their Excel Associate Certification.
  2. CSC 1100 – Data Science and Visualization. In this course, students complete numerous data-oriented assignments by employing essential concepts and basic software tools to engage in data cleaning, manipulation, analysis, and visualization.
  3. CSC 3211 (as of Fall 2024) – Database Systems. This course focuses on designing, implementing and using database systems, with emphasis on relational and object-relational models. Students design and deploy relational database models using commercial database management tools. Students will learn database query languages and will be able to design complex reports and queries to answer domain problems. Labs, projects, and exams all provide opportunities for students to analyze, prepare, format, and query data in appropriate manners to identify trends and features of the datasets or maintain database systems.
  4. CSC 4422– Data Mining and Machine Learning. This course is focused on data wrangling, visualization, and modeling. Students use Python along with the most common Data Science libraries to apply skills to real-world data sets. Assignments will include in-class labs and larger out-of-class projects that ask students to write code to apply key concepts as well as write text to reflect on the effectiveness of different data analysis approaches and to interpret results.
  5. ECO 3300 – Introduction to Econometrics. This course focuses on teaching students to: (1) identify appropriate methods for analyzing economic data, interpret the results of those analyses, and understand the limitations of different estimation methods; (2) communicate the results of their analysis in both written and oral form; and (3) use STATA to clean and transform data, analyze the data, and develop well-specified models to estimate relationships between variables.
  6. FIN 4973 – Blockchain and Emerging Financial Technologies. This course immerses students in the rapidly evolving landscape of financial technology, with a special focus on data mastery. The course structure is meticulously designed to guide students through the entire data analysis process, from initial acquisition to advanced interpretation. This course challenges students through projects where they will work with extensive datasets, commence with data procurement and data cleansing, and learn to identify discrepancies for accurate amalgamation. Students will employ Excel and Python to visualize data, perform descriptive statistics, and conduct comparative analyses. A unique aspect of this course is the incorporation of OpenAI’s Chat GPT as an educational tool.
  7. FIN 4975 – (as of Fall 2024) – Data Analysis in Finance . In this course, students learn how to use statistical programming tools to analyze financial data. Students will learn about basic Python programming, with a particular focus on the core finance data analysis stack, Python tools will be used to visualize and summarize financial data. Students will also learn how to use Python to look at finance-specific tasks, such as managing dates in financial time series, and performance analysis and portfolio optimization using additional. Students will also be exposed to more modern techniques related to machine learning in finance.
  8. HST 1210 – Unruly Origins: US to 1865 (Only sections taught be Dr. Amanda Kleintop : Spring 2024 Section A).
    In this introductory history course, students read sources and regularly complete “SOCC Analyses” (Source, Observe, Contextualize, Corroborate). Students evaluate the origins of sources, assess what a primary source can reliably tell them about the past, identify the historical context in which the primary source is produced and how the context changes their interpretation. Students also think critically about when the primary source is used and give a specific example of other data they would need to better understand the assigned source. To help students understand that historians must go out and find primary sources—they aren’t provided in pre-printed anthologies, students complete an “Archive Dig”, visit the Elon University Archives, and are introduced to the ethical considerations of organizing and collecting archival sources.
  9. HSS 2850
  10. HST 1220 – Contested Democracy: US from 1865 (Only sections taught be Dr. Amanda Kleintop : Spring 2024 Section B). See above.
  11. MGT 4110 – Data Wrangling. This course teaches students to collect, extract, transform, and load data with two software, Python and SQLite. Students learn to extract data from relational databases by using a structured query language, collect unstructured data with various methods such as application programming interface and web scraping, and transform and clean data after learning fundamental concepts and analytics of natural language processing and network analysis. As a course project, students identify questions of their interest and search and wrangle data relevant to their questions. This practice is designed to encourage students to critically think about what data are needed in context and what issues exist in gathering real-world data.
  12. MGT 4250 – Data Visualization and Storytelling. This course teaches students to create data visualizations in Tableau and Python and share them with insights through web applications. Colors, shapes, and other visual features are used to effectively represent data-driven insights. The course also covers some supervised and unsupervised learning models and lets students interpret model performance.
  13. MGT 4260 – Data Mining for Managerial Decision Making. Emphasizing the use of the R programming language, this course guides students through the entire data analysis lifecycle: from understanding business needs and the dataset to preparation, modeling, evaluation, deployment, and maintenance.
  14. MTH 2300 – Mathematics Methods in Data Analytics. This course focuses on two categories of methods in Data Analysis, grouping/cluster analysis and regression/interpolation. Students use Python to implement these techniques. While studying cluster analysis, students focus on (1) connections that can be visualized by graph or network and analyzing connections with spectral analysis and (2) projecting data in order to visualize, using Principal Component Analysis. In either case, students must determine the appropriateness of using the respective technique, clean the data, visualize the data and interpret their results.
  15. PHY 3130 (as of Fall 2024) – Modern Astrophysics. Students also identify ways to perform data analysis by making use of statistical tests and various forms of visualization. At the end of the semester, students communicate their results in a Python Jupyter Notebook with annotated analysis and through a research presentation. In this course-embedded research (CER) course, student spend the first half of the semester learning the basics of astrophysics by using Python Jupyter notebooks that incorporate real astrophysical data. Students spend the second half of the semester carrying out research projects. These projects require to students to think critically about the data required to answer research question, and to obtain the data using SQL searches with the online Sloan Digital Sky Survey database.
  16. PSY 2300 – (as of Fall 2024) – Cognitive Psychology . In this class, Students are asked to critically evaluate common research methods used in cognitive psychology and determine how those methods could change the data and therefore the conclusions researchers make. Additionally, students are asked to assess how and why participant behaviors or methodological changes would impact results in different ways, synthesizing data with theory and contextual factors. Students use the COGLAB software to collect data and interpret different data visualizations, including heat maps, bar/line graphs, and standardized metrics. A particular emphasis is placed on understanding and interpreting graphs, and how to determine if data support or fail to support hypotheses.
  17. PSY 2970 (as of Fall 2024) – Experimental Research Methods and Statistics. This course is centered around data collection and analysis, focused on the development of research questions, designing experimental research studies to test hypotheses, and analyzing data. Students are asked to critically evaluate common research methods used in psychological science and determine how those methods could change the data and conclusions. Additionally, students are asked to assess how and why participant behaviors or methodological changes would impact results in different ways, synthesizing data with theory and contextual factors. Students are introduced to ethical issues of manipulation of a person’s or animal’s psychological experience and/or behavior, as well as ethical behavior regarding data, such as p-hacking and the importance of ethical research practices. Students learn multiple data analytic techniques, including descriptive statistics, z-tests, t-tests, one-way ANOVA and factorial ANOVA. Excel and SPSS are incorporated into this course.
  18. PSY 3970 (as of Fall 2024) – Nonexperimental Research Methods and Statistics. Over the course of the semester, students review relevant literature, generate research questions and hypotheses, analyze data, and summarize their findings. While conducting secondary data analysis, students learn about the ethical considerations of conducting research with children or learn about informed consent with convenience/snowball sampling methodology. Students work extensively with SPSS throughout the semester. They clean their data before conducting their analyses, which includes assessing the reliability of multi-item measures using Cronbach’s alpha, reverse-coding survey items, and creating variables They run and interpret descriptive analyses and utilize linear regression models to test their hypotheses and understand relationships among variables in their data.
  19. PSY 4970 (as of Fall 2024) – Empirical Research Seminar. In this course, students work on a semester-long independent research project, in which they design, conduct, analyze, and report (in written and oral forms) their work. Explaining sources, definitions, and measures, and considering ethical use, is an explicit part of designing student research projects in this class.
  20. SOC/ANT 2160 (as of Fall 2024) – Quantitative Research Methods. Students in this course practice ethical data collection by collaboratively generating an original informed consent form and going through the IRB process. Students work with multiple datasets, including originally collected survey data, General Social Survey data, and student-collected state-level data on social indicators (e.g., CDC overdose death rates, BLS unemployment rates). Students use these datasets to gain experience with descriptive statistics, bivariate analysis (ANOVA, Chi-square tests), and multivariate analysis (OLS regression, logistic regression). Students identify appropriate statistical tests given the variables’ levels of measurement, and explain why particular analytic strategies are inappropriate. Students also export data from Qualtrics into SPSS, whereupon they transform the data for analysis. This entails reverse-coding, recoding to collapse categories, and scale construction following analyses of internal reliability.
  21. STC 3620 – Strategic Research Methods. This course gives students hands-on experience conducting primary research using qualitative and quantitative methods to address communications problems. Groups of students are tasked with identifying a brand that will serve as their “client” and developing a research objective to address the client’s problem/challenge they’ve identified. Students complete a secondary research brief to gain an understanding of the client, challenge, and relevant stakeholders and design, conduct, and analyze research using surveys, focus groups, and social listening. Students are expected to understand and consider the ethical challenges associated with the research process at every stage. For each method, students develop an executive summary that synthesizes the results of their research in an effective and visually engaging report and includes a section addressing limitations associated with the methodology, including sampling, instruments, data interpretation, researcher bias, etc.
  22. STS 2120 – Statistics in Application. This course is the gateway course for all Statistics majors and minors and for Data Analytics majors. It also serves as a First-Year Foundation course and a service course for many other quantitative majors across campus. Learning objectives for this course follow two streams, as students learn both introductory statistics and introduction to coding in SAS statistical software.
  23. STS 2300 – Introduction to Data Analytics. This course has students working with real-world data daily. Students work with a variety of real world data sets. Students clean/manage, visualize, analyze, and model data. They communicate their results in a written and visual format to different audiences.
  24. STS 2320 (as of Fall 2024) – Statistical Modeling. This course provides students the tools to perform regression analysis (simple linear regression, multiple regression, and logistic regression) on all kinds of data. Students must evaluate assumptions and determine which regression method is best. Students use R or SAS and practice writing about their findings throughout the course.
  25. STS 3250 (as of Fall 2024) – Design and Analysis of Experiments. Students in this course carry out a semester long project involving completing a proposal for an experiment, refining their protocol, collecting data from the experiment, and analyzing the data to draw conclusions. Through this project, students are able to implement basic principles of statistical design: randomization, replication, and blocking. Students produce a formal written paper and a recorded presentation, a podcast, a newspaper article, a series of Tik Tok videos, or some other creative form of summarizing their work.
  26. STS 3300 (as of Fall 2024) – Statistical Methods for Data Analytics. This course introduces students to advanced coding concepts using real data and to data ethics. Throughout the course, students work on a replication project that requires students to replicate results from a peer-reviewed manuscript. Students are given a large, raw, and messy dataset and then make proper decisions on which observations and variables to include. During the replication process, students must clean the data, create new variables, and analyze the data per the original article.
  27. STS 3470 –Statistical Computing for Simulation and Theory. Significant portions of the course will teach data management including cleaning, transforming, and visualizing data using the most relevant R tools (dplyr package). The course will also teach students how to develop shiny applets.