Professors Economopoulos, Scoville (Coordinator), VanGilder; Associate Professor Mongan; Assistant Professors Aukers,Montesinos-Yufa, New, Takyi, Tralie, Yan; Instructor Grossbauer.
Data analytics is an interdisciplinary minor that combines statistics and computer science, preparing students to understand how to ask questions and present answers about a world in which exponentially growing amounts of quantitative data are becoming available. The minor trains students to organize and assemble large data sets, process them, and use them to describe and answer questions about the world in all of its complexity. In the context of the Ursinus core curriculum, the minor asks students to consider the question, “How can we understand the world?”
Requirements for Data Analytics Minor
A minor in data analytics consists of 24 credits, with 16–20 credits in required courses. Required courses are as follows:
Programming courses (prerequisites for DATA classes)
- STAT-141 and 142 or a two- to four-credit course focusing on R programming. Other courses will be accepted for this requirement with the permission of the data analytics coordinator.
- A two- to four-credit course focusing on Python programming. CS-170Q satisfies this requirement. Other courses will be accepted for this requirement with the permission of the data analytics coordinator.
In addition, data analytics minors must complete between four to eight elective credits to reach 24 credits in the minor. The following courses will satisfy the elective requirement:
- No more than one of the following courses, which require data gathering, description, and statistical analysis: ANSO-200, BIO-359, CHEM-315/315L, HEP-261W, NEUR/PSYC-432W, POL-300, STAT-242, STAT-243W, or ECON-300Q.
- Honors or research or an independent study in any discipline that involves significant data analysis may also count towards the minor, with the permission of the data analytics coordinator.
DATA/STAT-150. R for Data Science
A hands-on study of the statistical programming language R for liberal arts students. This course prepares students to collect, manipulate, analyze, and visualize real-world data using R with a focus on data science applications. A final project will consist of analyzing and visualizing CIE readings statistically. Key R packages to use include (and will not be limited to) base, readr, readxl, stringr, knitr, dplyr, dbplyr, lubridate, ggplot2, tidytext, mlr3, caret, rmarkdown, data-table, and the tidyverse suite. Examples will be drawn from various disciplines centered on students’ interests. Offered in Spring. Prerequisites: STAT-142 and either CIE-100 or CIE-150 or CIE-200, or permission from the instructor. Four hours per week. Four semester hours.
DATA-201. Data Analytics
A broad introduction to the field of data analytics. The course focuses on the proper deployment and use of data analytic tools and techniques that are successfully utilized by modern organizations. Students will be introduced two programming language: SQL and R. After taking the course, students will demonstrate a mastery of SQL queries to extract data from a relational database; demonstrate the ability to manipulate, analyze and visualize data; and demonstrate the ability to utilize statistical scripting languages and software tools to analyze data for insights. Three hours per week. Prerequisites: STAT-141Q, STAT-142 (or another approved course in R programming), CS-170 (or another approved course in Python programming). Four semester hours.
DATA-202. Data Care and Cleaning
Building upon foundational analytics tools and techniques, this course further explores advanced data pre-processing tools used in the field of data analytics. Students will focus on data care, data cleaning and data visualization with Python. By the end of this course students will master modern data pre-processing libraries in Python. Prerequisite: CS-170 (or another course in Python programming). Three hours per week. Four semester hours.
DATA-301. Data-Driven Insights and Society
An introduction to the methods used to build and manage databases, the storehouses of information used by organizations to understand their operations and make decisions. Students who successfully complete the courses will know how to choose appropriate tools for a given data management application; understand relations in the context of databases; construct distributed databases and NoSQL databases; and develop a substantial data management project. Prerequisites: DATA-201 and 202. Three hours per week.Four semester hours.
DATA-350. Topics in Data Analytics
An occasional course focusing on a special topic in data analytics. Prerequisites will vary. Three hours per week.Four semester hours.