Business Analytics

Follow your passion for Analytics

Business Analytics is a truly interdisciplinary major with applications in all areas of business. Students embarking on this path will develop solid skills in data mining and methods of discovery, all while exploring the role of ethics and the social value associated with big data collection and usage.

Loras College is a leader in analytics, and this program is part of our Center for Business Analytics that also includes an MBA in Analytics and a Certificate in Analytics. These programs generate a culture of data science and offer students a glimpse into the diverse opportunities available beyond graduation.

Students will be expected to participate in internships and at the annual Analytics Symposium beyond their classroom activities.

Preview sample 4 year plan.


Additional Information
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If you have additional questions about our Analytics programs or would like us to send you a brochure, please call or email Doug Gambrall at 563.588.7910

Student Learning Outcomes
Student Learning Outcomes – Business Analytics
1. Apply statistical concepts to data analysis and modeling
2. Apply business analytics techniques using programming languages and software
3. Import, clean, and transform data in different platforms
4. Create and apply visualization for better understanding of data
5. Utilize the outcomes of analytics for better business decision making
6. Effectively communicate the findings of analysis in written and oral form
7. Create predictive models using machine learning algorithms
8. Apply ethical reasoning to business analytics issues
Major Requirements

Francis J. Noonan School of Business
James Padilla, J.D., Dean

Please Note: Students must get PRIOR written permission to transfer in credit once they have matriculated at Loras. Students wishing to take a summer school class on another campus must get written permission from the Dean of the School of Business or their designee BEFORE taking the class. If a student has taken courses on other campuses and then transfers to Loras the Dean of the School of Business or their designee may make a determination of those transfer credits.

Since students who are double majoring within the School of Business will not be taking L.BUS-490 more than once, they need to complete an additional upper level course in either major to replace the second seminar course when applicable.

Requirements for the major in Business Analytics (B.A.):
Students must earn a cumulative average of 2.00 or better in all coursework toward their major.

Req Course Cr’s
1 L.MAT 150: Calculus 4
2 L.ACC-227: Managerial Accounting 3
3 L.BUS-230: Principles of Management 3
4 L.BUS-240: Principles of Marketing 3
5 L.BUS-350: Managerial Finance 3
Select one from Req. 6
6 L.CIT-115: Introduction to Programming 4
6 L.EGR-116: Intro to Programming with Robotics 4
Select one from Req. 7
7 L.MAT-115: Statistics 4
7 L.MAT-220: Probability and Statistics 3
7 L.BUS-250: Business Statistics 3
Select one from Req. 8
8 L.BAN-330: Introduction to Data Science 3
8 L.DAT-200: Tools & Methods for Analytics 3
9 L.BAN-210: Essentials of Analytics 3
10 L.BAN-310: Data Visualization 3
11 L.CIT-221: Data Analysis 3
12 L.BAN-340: Innovation 3
13 L.BUS-490: Business Seminar 3
Select one from Req. 14
14 L.BAN-320: Predictive Modeling 3
14 L.CIT-340: Machine Learning (pre-requisite of CIT 225) 3
Select one from Req. 15
15 L.BAN-460: Big Data Analytics (pre-requisite of CIT 225) 3
15 L.CIT-318: Database Management 3
Select one from Req. 16
16 L.BAN-300: Applied Analytics 3
16 L.BAN-450: Marketing Analytics 3
50 total required credits

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
Course Descriptions

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.

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.

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.

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.

BAN-330: Introduction 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.

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.

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.

BAN-460: Big Data Analytics

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.


McKinsey estimates there will be 1.9 million jobs to support big data by 2015, and McKinsey Quarterly estimates that business intelligence needs will exceed available workers by 60 percent by then as well.

Data scientists and analytics professionals are needed in all areas of business, including but not limited to:

  • Social Marketing
  • Supply Chain Management
  • Health Care and Medical Research
  • Financial Modeling and Prediction
  • Accounting and Audit
  • Insurance and Actuarial Analytics
  • Business Process Management and Operations
  • Government and Environmental Studies
  • Sustainability
  • Sports Analytics
Questions? Contact Us!

James Padilla
Francis J. Noonan School of Business Dean
563.588.7405 |

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 |

Dale Lehman, Ph.D.
Center for Business Analytics Director
Professor of Business
563.588.7725 |

Curriculum Vitae

Dale Lehman has a Ph.D. in Economics from the University of Rochester. He has taught at a dozen universities and was Director of the MBA programs at Alaska Pacific University. He has also held industry positions at Bell Communications Research and SBC. He teaches in a number of specialized MBA programs in Europe.

Dale’s interests are in applied data analysis. This includes visualization of patterns in data, analysis that highlights meaningful stories hidden within data, and replication/validation of data analysis. He is particularly interested in applications of data analysis to problems related to health care, natural resources, telecommunications and information, and finance.

Dale has co-authored three books and numerous articles. He enjoys hiking, cross-country skiing, golf, travel, and teaching at small private universities.