Data Science

Immerse yourself in applying analytical techniques with a Bachelor’s Degree in Data Science

The Loras College Data Science major 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.

LinkedIn illustrates 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.

Learn About Our Major in Data Science

Student Experience

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.

Curriculum

View Highlighted Courses

 

Data Science
Data Science is a developing field that combines computer science, statistics, and domain-specific knowledge. This course will introduce students to the field of Data Science via case studies and projects from various domains, including business, digital humanities, social sciences, and sports. Projects will include data visualization, summary, and prediction.

Tools & Methods for Analytics
Analytics is the study of various models, methods and tools that can be applied to gain insights from data. It involves collecting, cleaning, analyzing, summarizing and presenting data in a scalable and generalizable manner. In this course, students will learn to implement each of these steps using appropriate programming environments.

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.

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

Finance
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

View All Data Science Course Offerings PDF

Major Requirements

Students will complete the following requirements in order to achieve a major in Data Science. 

Degree Requirements

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

Req Course

Cr’s

1

 Overview of Data Science

3
Select one from Req 2

2

 Intro to Programming

4

2

 Intro to Robotics Programming

4

3

  Calculus of One Variable I-FM

4

4

 Data Visualization

3

5

 Tools & Methods for Analytics

3

6

 Probability and Statistics

3

7

 Data Structures and Algorithms

4
Select one from Req 8

8

 Linear Algebra

3

8

 Multivariable Calculus

4

9

 Database Programming

3

10

 Machine Learning

3

11

 Statistical Learning

3

12

 Big Data Analytics

3

13

 Capstone

3

14

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

3-4

15

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

3-4

16

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

3-4

17

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

3-4

54-59 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.

View Full Requirements & Additional Information PDF

Data Science Program

  • 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.

Career Opportunities

“What can you do with a Data Science degree?”

As a Data Science major, you could break into any of the following data science careers.

  • Data Analyst
  • Data Architect
  • Data Scientist
  • Business Intelligence Developer
  • Statistician
  • Business Analyst

Supporting Your Investment

Loras takes great pride in supporting your investment – both through providing an exceptional learning experience and in sharing the cost of your degree. 100% of Loras students receive financial aid. We have scholarships, grants and special awards for all students based on their achievements and financial need.

Frequently Asked Questions

How long will it take me to earn my Loras degree?

Most students earn their undergraduate degree in four years or less. If you have questions about transferring any previously earned credits or degrees, please see our Transfer Student Information.

How much is tuition?

At Loras College, financial access to education is one of our defining values. We are committed to helping all of our students make their degree affordable. We partner with every student and family to understand their unique financial needs ensuring 100% of Loras students receive financial aid. We offer Scholarships, grants and special awards for all students based on their achievements and financial need. Loras is consistently ranked as one of the best universities for return on investment.  View our Tuition and Fees page.

How do I apply for financial aid?

Submit your federal FAFSA, apply to Loras College and visit loras.edu/financial-aid for information, scholarship opportunities and much more.

Meet Our Professors

Robert Keller, Ph.D.
Division of Mathematics, Engineering, & Computer Science Chair
Professor of Mathematics
563.588.7015 | Robert.Keller@loras.edu
Full Profile

Susan Crook, Ph.D.
Associate Professor of Mathematics
563.588.7794 | Susan.Crook@loras.edu
Full Profile

Jacob Heidenreich, Ph.D.
Associate Professor of Mathematics
563.588.7793 | Jacob.Heidenreich@loras.edu
Full Profile

Karen Heidenreich, Ph.D.
Instructor of Mathematics
563.588.7971 | karen.heidenreich@loras.edu
Full Profile

William Hitchcock, M.B.A.
Professor of Management Information Systems
563.588.7286 | William.Hitchcock@loras.edu
Full Profile

Angela Kohlhaas, Ph.D.
Associate Professor of Mathematics
563.588.7152 | Angela.Kohlhaas@loras.edu
Full Profile

Michael Thompson, Ph.D.
Associate Professor of Computer Science
563.588.7570 | Michael.Thompson@loras.edu
Full Profile