Data Science

Applying analytical techniques

Data Science is an interdisciplinary field of study, drawing primarily from mathematics, statistics, and computer science. The major in Data Science combines coursework from these areas with a separate disciplinary focus so that an undergraduate majoring in Data Science may have ample opportunity to apply analytical techniques to problems of interest in fields as varied as Accounting, Catholic Studies, and Sport Management.

Recent analysis by LinkedIn in August 2018 shows there is currently a shortfall of about 6000 Data Science jobs in the Chicago metro area, with projections that demand will grow in the Upper Midwest and across the country.

Student Learning Outcomes
Student Learning Outcomes – Engineering
Students will be able to:
1. Import and clean data from a variety of sources and qualities using appropriate technologies for storage and retrieval.
2. Select and employ appropriate mathematical, computational or statistical methods for analyzing and visualizing data
3 Apply appropriate models and techniques to gain insights to and answer questions from the chosen disciplinary focus.
4. Effectively use knowledge (skills and conceptual understanding) from computing, mathematics, and statistics.
5. Independently learn new methodologies and technologies in the field of data science.
6. Communicate information clearly in multiple modes in audience-appropriate format, including:
a. Written
b. Oral
c. Visual
d. Interactive
7. Demonstrate a knowledge of the ethical, professional and disciplinary standards in data science and their content focus, and consistently apply ethical processes.
Major Requirements

Division of Mathematics, Engineering & Computer Science
Robert Keller, Ph.D., Chair

Requirements for the major in Data Science  (B.S.):

The proposed major requires 51 to 57 credit hours.  In particular, the major in Data Science has been formulated to combine a strong core of 39 credits comprising mathematics, statistics, and computing courses to be packaged together with 12-16 credits of coursework having a particular disciplinary focus that is of interest to a student. This allows students with widely-varying interests who wish to study in that area with a robust foundation in the fundamental concepts and skills of data science.  The specific courses forming a student’s disciplinary focus must be approved by the Data Science faculty prior to graduation.

Req Course



L-DAT 100: Introduction to Data Science


Select one from Req 2


L-CSC 115: Intro to Programming



L-EGR 116: Intro to Robotics Programming



L-MAT 150: Calculus  I



L-BAN 220: Data Visualization



L.MAT 220: Probability and Statistics



L.CSC 225: Data Structures and Algorithms


Select one from Req 7


L.MAT 250: Linear Algebra



L. MAT 260: Multivariable Calc



L.CSC 337: Database Programming



L.CSC 340: Machine Learning



L.MAT 420: Statistical Learning



L.BAN 460: Big Data



L.DAT 490: Capstone



Content Basics within Disciplinary Focus (100-200 level course)



Content Basics within Disciplinary Focus (200-300 level course)



Content Basics within Disciplinary Focus (200-300 level course)



Content Basics within Disciplinary Focus (300-400 level course)


51-57 total required credits

The only restrictions imposed on courses taken in the Disciplinary Focus Area are as follows:

  1. These courses may have different prefix codes but must represent a single disciplinary focus. For example, a student might focus on Financial Planning and Wealth Management, which encompasses courses from both Accounting and Business (ACC and BUS prefixes).

Courses in the Disciplinary Focus Area must be distinct from the others required for the Data Science major.

Sample Disciplinary Focus Areas & Courses

Sample Disciplinary Focus Areas

Generally any minor or other major at the College may serve as a student’s focus area. The following samples illustrate how smaller numbers of courses might comprise a Disciplinary Focus Area.

BIO 115: Principles of Biology I
BIO 116: Principles of Biology II
BIO 250: Genetics
BIO 330: Evolutionary Ecology

ACC 227: Managerial Accounting
BUS 350: Principles of Finance
BUS 352: Investments
BUS 451: Intermediate Financial Management

History (European)
HIS 140: Modern Europe since 1750
HIS 288: The Historian as Investigator
HIS 349: The Second World War
HIS 404: Historical Geography

Politics (American)
POL 101: Issues in American Politics
POL 204: State & Local Politics
POL 304: Identity Politics in America
POL 331: Political Thought and Contemporary Social Issues

Sport Management
SMG 150: Intro to Sport Management
SMG 225: Sport Business
SMG 240: Sport & Society
SMG 422: Sport Sales & Sponsorship or SMG 468: Sport Marketing & Promotions

Program Objectives
  • Prepare students to utilize skills and practices of data science, preparing them for many careers, connecting to a wide variety of areas of study.
  • Teach students a variety of ways to use data to discover findings and communicate those findings.
  • Prepare students to be life-long learners in the field of data science by providing sufficient foundational depth in mathematics, statistics, and computer science.
  • Contribute to the application of and growth of data science in ethical ways.
Questions? Contact Us!

Robert Keller, Ph.D.
Division of Mathematics, Engineering, & Computer Science Chair
Professor of Mathematics
563.588.7015 |

Curriculum Vitae

Robert Keller is Associate Professor of Mathematics at Loras College and Chair of the Division of Mathematics, Engineering, and Computer Science. Rob has taught at Loras since earning his Ph.D. in Mathematics in 1999 from the University of North Carolina, Chapel Hill. From 2000-2004 he taught 5th- and 6th-grade Dubuque public school students part time through the Talented and Gifted program. For the past decade, Rob has delivered professional development in mathematics, and more recently in STEM education, for practicing K-12 teachers. These have included workshops for high school teachers transitioning to a standards-based beginning algebra series, and more than six years as a lead organizer and presenter for the Loras College Lesson Study Project. Funded by several large grants, this project was a successful partnership involving the Mississippi Bend and Keystone Area Education Agencies and educators from Loras College that ultimately served hundreds of teachers throughout eastern Iowa.

Currently, he is co-director of a three-year Title IIA-funded project which seeks to build capacity to deliver integrated middle school science and mathematics content. More than 50 middle school teachers from six school districts are currently involved in this unique project. Rob has also been active in the education and formation of future K-12 teachers. He co-directed the development of a two-course sequence in mathematics content for K-8 teachers at Loras College (funded by a Preparing Mathematicians to Educate Teachers grant awarded through the NSF and Mathematics Association of America), which he now regularly teaches. He has collaborated with Bridgette Stevens (formerly at the University of Northern Iowa) on testing methods to promote the integration of reflective practices in mathematics courses for elementary teachers, work that was funded by an inter-institutional grant from UNI. In addition, from 2002-2004 Rob led efforts with Joyce Becker of Luther College and Catherine Miller of the University of Northern Iowa to update Iowa state requirements for pre-service Secondary Math Education majors (with funding by grants from the Regents Academy and UNI).

Shikhar Acharya
Assistant Professor of Business Analytics
563.588.7784 |

Shikhar Acharya is working as Assistant Professor of Business Analytics at Loras College, Dubuque, Iowa from Fall 2017. He teaches various analytics courses both at undergraduate and graduate level. Some of the courses he is teaching are Applied Analytics, Predictive Modeling, Programming for Analytic Methods, etc. He worked as Visiting Instructor at the Department of Industrial and Management Systems Engineering, University of South Florida from Fall 2015 to Summer 2017. Before joining USF, he taught at Missouri Western State University as Assistant Professor of Business Statistics in Spring 2015.

His primary research is in the area of detection of malicious devices. He has applied various machine learning algorithms and statistical methods such as Hidden Markov Models, Neural Networks, Support Vector Machines, etc. He has peer reviewed publications in this area and has presented his work in various conferences and forums. He has collaborated with various local organizations and advised them on data related issues. This is done by incorporating their problems in the courses he teaches. These collaborations have provided his students with real life working experience. He received his Ph.D. in Systems Engineering from Missouri University of Science and Technology, Rolla, Missouri. He has an undergraduate in Computer Engineering and he also holds an MBA degree.

Susan Crook, Ph.D.
Associate Professor of Mathematics
563.588.7794 |

Susan Crook earned her Ph.D. from North Carolina State University in 2013. At NCSU she worked in Numerical Analysis focusing on curve matching, which has real-world applications in object recognition and assembly. Since coming to Loras, Dr. Crook’s research has focused more on happy numbers (with a research group supported by the American Institute of Mathematics). She is interested in inquiry-based learning, both in the classroom and as a research topic. While she enjoys research, her real passion is teaching.

At Loras, Dr. Crook has had the pleasure of teaching a variety of math courses and engaging with students across many majors. She loves getting students actively involved in playing with mathematics so that they can enjoy the “ah ha!” moments that mathematicians do. In her free time, Dr. Crook enjoys cooking and baking, traveling the world, discovering new musicians, and reading while cuddled up with her two cats, Penny and Nona.

Jacob Heidenreich, Ph.D.
Associate Professor of Mathematics
563.588.7793 |

Curriculum Vitae

Prof. Heidenreich’s training is in mathematics and philosophy, centering on the field of Mathematical Logic. He has a strong interest in the historical development of mathematics, as well as the philosophical issues that have arisen during that development. In the past, he has worked on developing undergraduate research and the senior experience in the math program at Loras College. He developed the system by which math majors engaged in undergraduate research and present that research to their peers and professors. He also was responsible for beginning a tradition of student attendance and presentation at undergraduate conferences in mathematics. Recently, his interest is in the use of games in the classroom to enable deep student learning. He studies good game design, and how those design principles can be used to design various assignments and activities. He also develops games for use as teaching tools in the classroom.

Karen Heidenreich
Instructor of Mathematics
563.588.7971 |

Dr. Karen Heidenreich earned her Ph.D. in mathematics from the University of Notre Dame in August 2000, where she studied highest weight representations of infinite dimensional Lie algebras.  Before coming to Loras with her husband Jacob, she taught for six years at Grand Valley State University in Allendale, MI.  Currently Dr. Heidenreich teaches classes both on the Loras campus and at the local high schools.  She also serves as a member of the STEM committee for the Northeast Iowa Council of the Boy Scouts of America, where she works to develop exploratory STEM activities for elementary age children.  In her spare time she enjoys reading, sewing, playing board games, and hanging out with her kids.

William Hitchcock, M.B.A.
Professor of Computing and Information Technology
563.588.7286 |

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.

Angela Kohlhaas, Ph.D.
Associate Professor of Mathematics
563.588.7152 |

Curriculum Vitae

Dr. Kohlhaas received her PhD in mathematics in 2010 from the University of Notre Dame, where she studied commutative algebra. She enjoys finding ways of visualizing abstract algebraic and geometric concepts, and her students spend an inordinate amount of time playing with Play-Doh as a result. She also loves engaging students in undergraduate research projects, with topics ranging from the mathematics of origami to symmetries of Sudoku. She recently developed a January term course investigating the mathematics of musical compositions and perspective art which she is excited to be teaching in January 2015. Outside of mathematics, Professor Kohlhaas can often be found playing Ultimate Frisbee, at the piano, or cooking spicy food.

Michael Thompson, Ph.D.
Associate Professor of Computer Science
563.588.7570 |

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.

Matthew Rissler, Ph.D.
Associate Professor of Mathematics
563.588.7792 |

Dr. Rissler is originally from Virginia, but spent nine years in Indiana at Goshen College and the University of Notre Dame earning degrees in Mathematics, Physics, and Applied Math. Since 2008 he has been at Loras College teaching all of these and Statistics. His classes tend to involve using laptops to complete activities and modeling projects. Rissler’s research interests lie in the areas of agent-based modeling, statistics and utilizing computers in teaching Mathematics. Current and recent senior projects he has advised include simulating battles between orcs and elves (if you like LotR, or humans and zombies if you don’t), statistical modeling of production by players in the WNBA, and looking at streaks in baseball at the college level.