ANALYTICS MINOR
Francis J. Noonan School of Business
James Padilla, J.D., Dean
Requirements for the minor in Analytics:
| Req | Course | Cr’s |
| 1 | L.BAN-210: Essentials of Analytics | 3 |
| Select one from Req 2 (courses listed or equivalent course with prior approval) | ||
| 2 | L.BUS 250: Business Statistics | 3 |
| 2 | L.MAT 115: Statistics-FM | 4 |
| 2 | L.MAT 220: Probability and Statistics | 3 |
| 2 | L.PSY 211: Research Methods and Statistics | 4 |
| 2 | L.SOC 333: Statistical Analysis | 3 |
| 3 | L.BAN 310: Data Visualization | 3 |
| 4 | L.CIT-221: Data Analysis | 3 |
| 5 | L.BAN-300: Applied Analytics | 3 |
| Select one from Req 6 | ||
| 6 | L.BAN-320: Predictive Modeling | 3 |
| 6 | L.DAT-200: Tools & Methods for Analytics | 3 |
| 6 | L.BAN -330: Intro to Data Science | 3 |
| 6 | L.BAN-340: Innovation | 3 |
| 6 | L.BAN-450: Marketing Analytics | 3 |
| 6 | L.CIT-340: Machine Learning | 3 |
| 6 | L.BAN-460: Big Data Analytics | 3 |
| 18 total required credits | ||
L.BAN-210: Essentials of Analytics
This course provides an introduction to the field of Business Analytics, with a foundational basis in Business Statistics. Specific analytic topics covered include: Data Mining, Data Warehousing, Data Visualization and Analytics Software. 3 credits.
L.BUS-250: Business Statistics
An introduction to basic statistical measurements: sampling theory, including estimation of parameters, hypothesis testing and basic decision theory. Other topics include correlation analysis, time series analysis, seasonal fluctuations, trend fitting, and cyclical measurement. 3 credits.
L.MAT-115: Statistics-FM
Basic statistical concepts and methods. Descriptive statistics and probability, distribution and sampling theory, hypothesis testing and analysis of variance, correlation and regression. Prerequisite: a grade of C- or better in L.MAT-091 or placement into L.MAT 115. 4 credits.
L.MAT-220: Probability and Statistics
A study of the fundamental techniques used in descriptive statistics as applied to real-world data and the processes associated with the design and analysis of experiments; application of theories from calculus to the construction of cumulative distributions for continuous random variables and computation of associated probabilities, expected values and variances. Prerequisites: L.MAT-150, and one of L.MAT-160, L.CIT-115, or L.EGR-116. 3 credits.
L.PSY-211: Research Methods and Statistics
An introduction to the use of research strategies and tools of measurement in psychology. The SPSS computer program will be used to introduce basic data handling, descriptive and correlational statistics. Students will be expected to participate in elementary research studies, produce APA-style research papers, and evaluate research literature. Prerequisite: Grade of C- or better in the foundational mathematics requirement, L.PSY-101 or equivalent. 4 credits.
L.SOC-333: Statistical Analysis
Rationale and use of various statistical procedures including measures of central tendency, dispersion, inference and association. Students gain experience in coding and entering data, recoding variables, and creating composite measures with the use of computer software. Students develop their own hypotheses and conduct independent statistical analyses of survey data. Encouraged for all social science majors. Prerequisites: Grade of C- or better in L-SOC-332, L.CRJ-323, or L.PSY-211. 3 credits. Fall semester.
*Note– Students are considered for a prerequisite waiver at the discretion of the Professor.
L.BAN-310: Data Visualization
This course provides an introduction to the field of data and information visualization, a key sub-field in the area of data analysis and mining. Specific analytic topics covered include: tables & charting, multi-dimensionality of data, handling unstructured data, and advanced visualization tools and techniques. Prerequisite: L.CIT 110 or L.CIT 221. 3 credits.
L.CIT-221: Data Analysis
This course focuses on evaluating and analyzing different types of business-related data and developing effective solutions. It will utilize current spreadsheet and database software as tools to facilitate the interpretation of the data. The course will have a lab component requiring student laptop computers equipped with spreadsheet and database software. Prerequisites: L.ACC-227 or L.ACC-228. 3 credits.
L.BAN-300: Applied Analytics
This course provides an opportunity for students to conduct analyses of real data, following all stages from data acquisition and preparation through analysis and presentation. While good data analysis requires many skills, the vast majority of an analyst’s time is spent on preparing, cleaning, and understanding what the data actually means – how was data collected, how is data measured, and what does each variable really mean? There are no prerequisites and students are expected to have a range of abilities from novices to some with statistics backgrounds. Work will be done in groups with tasks appropriate for each student’s skill level. Projects will vary in subject areas, and may include survey data, use of public databases (e.g., Census data or sports data), or data sets collected by individual entities (such as particular business entities). Prerequisite: A statistics course. 3 credits.
L.BAN-320: Predictive Modeling
The rapid expansion of data availability has made possible considerable advances in modeling for the purpose of prediction. Virtually all decisions, at least in part, depend on predictions of what will happen if something changes (either under our control or not). This course explores applications of a variety of current predictive modeling techniques to data. Included are multiple regression modeling, logistic regression, decision trees, random forests, neural networks, and simulation analysis. The emphasis will be on applied analysis, utilizing data from a wide variety of areas, including business, politics, socioeconomic conditions, health, sports and entertainment, etc. Students will build and compare predictive models, learn how to evaluate these models, and how to apply model results to improve decision making. Prerequisite: L.BAN 330 or L.DAT 200. 3 credits.
L.BAN-330: Intro to Data Science
Data science is the process of collecting, cleaning, analyzing, summarizing and presenting data in a scalable and generalizable manner. In this course, students will learn each of these steps using R, an open source analytics language, culminating in a project. Prerequisites: L.CIT 115, L.MAT 220. 3 credits.
L.BAN-340: Innovation
Gaining a competitive advantage in today’s business environment increasingly demands that organizations know how to innovate. Creativity, continuous improvement, and the ability to turn ideas into action are critical to standing out above the rest. Specific topics will include: the innovation process, disruptive technologies, why plans are bad, and when NOT to listen to your customers. We will also apply our knowledge via an innovation simulation. Prerequisites: L.ACC 227, L.BUS 230, L.BUS 240. 3 credits.
L.BAN-450: Marketing Analytics
This course explores the topic of Marketing analytics which has grown significantly in recent years in response to the rapidly increasing supply of data generated by marketing campaigns, online sales, websites, social media, customer relationship management programs and integrated marketing communication campaigns. Through enhanced technology, more data are available than ever before. But marketers are faced with the dilemma of how to convert the massive amount of available data into usable information. In this course students will engage in the systematic study of these data which are employed, through the use of statistical analysis and technology, to improve decision making. Prerequisites: L.BAN 210, L.BUS 240. 3 credits
L.CIT-340: Machine Learning
This course introduces students to topics in the Machine Learning area of Artificial Intelligence. It will include an introduction to some popular algorithms computers use to make decisions and predictions based on problems consisting of varied types of data. In addition to utilizing the algorithms themselves, students will learn about different methods of evaluating these algorithms and how to choose an algorithm for a particular problem. Prerequisite: L.CIT 225. 3 credits.
L.BAN-460: Big Data Analysis
Big Data is often referred to as a new form of natural resource. This is a poor metaphor, as Big Data is growing faster than any other natural resource grows. Big Data is really a more accurate view of the past, what an economist might refer to as closer to perfect information. This of course can be used for great social value or for personal destruction. This course should be a combination of tactical skills of Apache’s hadoop/map-reduce along with the relevant discussion of the social opportunities offered by big data. Prerequisite: L.CIT 225. 3 credits.
James Padilla
Francis J. Noonan School of Business Dean
563.588.7405 | james.padilla@loras.edu
Padilla served as associate dean/associate professor of the School of Business Administration at Marymount University in Arlington, Va. Prior to that, he was an associate professor at the Tiffin University School of Business in Tiffin, Ohio, serving as dean there for two years; assistant professor in the Department of Movement Science at Grand Valley State University in Allendale, Mich.; assistant professor at Ball State University’s School of Physical Education, Sport and Exercise Science in Muncie, Ind.; assistant professor at University of Saint Francis’ Keith Busse School of Business and Entrepreneurial Leadership in Fort Wayne, Ind.; and a lecturer at Ivy Tech State College, also in Fort Wayne.
Originally from the Chicago area, Padilla is an expert in the athlete disability insurance field. Over the past 20 years, he has worked with players, coaches, agents, financial advisors and professional teams in regards to properly protecting themselves and their assets. He later merged his own insurance firm with Braman Insurance in Merrillville, Ind., and still serves as a consultant for Braman.
Padilla received his undergraduate degree in sociology from Northern Illinois University. He received his juris doctorate at Southern Illinois University School of Law in Carbondale, Ill., and an executive certificate in sports management from Loyola University Chicago.
Shikhar Acharya
Assistant Professor of Business Analytics
563.588.7784 | shikhar.acharya@loras.edu
William Hitchcock, M.B.A.
Professor of Computing and Information Technology
563.588.7286 | William.Hitchcock@loras.edu
In 1984, William Hitchcock graduated Magna Cum Laude with a BBA degree from the University of Wisconsin – Whitewater, double majoring in Marketing and Management Computer Systems. Upon graduation, he began working as a Programmer/Analyst for the Oscar Mayer Foods Corporation headquartered in Madison, Wisconsin. Most of his development work was with marketing decision support systems utilizing retail store audit information. While working full time at Oscar Mayer, he began his graduate studies at the University of Wisconsin – Madison in 1986. He completed his work and graduated with an MBA degree majoring in Finance, Investments, and Banking in 1988. In 1989, Hitchcock made a career move to begin teaching business courses at Loras College in Dubuque, Iowa. He has taught business coursework to both traditional college students and professionals working in the Dubuque area. In 2011, he served as the Faculty Director of the Study Abroad program in Dublin, Ireland. He has a special interest in International/Irish studies, and has since taught several Irish-themed courses including a summer course in Ireland in 2014.
Patrick Marzofka, M.B.A.
Associate Professor of Business Administration
563.588.7283 | Pat.Marzofka@loras.edu
Pat Marzofka began working at Loras College in 1987. He received his Bachelor’s degree in economics from the University of Wisconsin-Eau Claire and an MBA in marketing from the University of Wisconsin Madison. Before Loras, Marzofka taught at two small schools in Wisconsin and later worked in marketing research at Shopko.
Marzofka explains that his favorite part of working at Loras is the opportunity to interact with students inside and outside of the classroom. He considers teaching fun and rewarding. He is passionate about the topics he teaches and has discovered that each class has its own personality. “Students can make or break the class!” he explains. Based on his many years in the classroom, Marzofka believes that computer simulation is an effective tool in the education process because it focuses on experiential learning. In the class Marketing Management, he uses simulations to guide students to understand how to work in a group, be creative and have fun in the process, even if the outcome seems uncertain. Furthermore, Marzofka enjoys seeing the long lasting friendships that started in his classes or began as a result of one of his class projects.
Michael Thompson, Ph.D.
Associate Professor of Computer Science
563.588.7570 | Michael.Thompson@loras.edu
After growing up in suburban Minneapolis/St. Paul, Minnesota; Dr. Michael Thompson attended Central College in Pella, Iowa where he graduated with a double major in Mathematics and Computer Science as well as a minor in Philosophy. After graduating, he worked as a programmer for Advanced Technologies Group, Inc. in West Des Moines, Iowa. He then attended graduate school at the University of Wisconsin-Madison where he received his Ph.D. in Computer Sciences, with an emphasis on Optimization. While there, Dr. Thompson researched methods of finding the minimum of a nonconvex function, with applications in protein-ligand docking. His current research interests include applications in Artificial Intelligence using Support Vector Machines and other techniques relating to business analytics, specifically in how they relate to sports.