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.

ADDITIONAL INFORMATION
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

DATA SCIENCE
Division of Mathematics, Engineering & Computer Science
Robert Keller, Ph.D., Chair
robert.keller@loras.edu
563.588.7015

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

Cr’s

1

L-DAT 100: Introduction to Data Science

3

Select one from Req 2

2

L-CSC 115: Intro to Programming

4

2

L-EGR 116: Intro to Robotics Programming

4

3

L-MAT 150: Calculus  I

4

4

L-BAN 220: Data Visualization

3

5

L.MAT 220: Probability and Statistics

3

6

L.CSC 225: Data Structures and Algorithms

4

Select one from Req 7

7

L.MAT 250: Linear Algebra

3

7

L. MAT 260: Multivariable Calc

4

8

L.CSC 337: Database Programming

3

9

L.CSC 340: Machine Learning

3

10

L.MAT 420: Statistical Learning

3

11

L.BAN 460: Big Data

3

12

L.DAT 490: Capstone

3

13

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

3-4

14

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

3-4

15

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

3-4

16

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

3-4

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.

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

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 | Robert.Keller@loras.edu
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Shikhar Acharya
Assistant Professor of Business Analytics
563.588.7784 | shikhar.acharya@loras.edu
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Susan Crook, Ph.D.
Associate Professor of Mathematics
563.588.7794 | Susan.Crook@loras.edu
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Jacob Heidenreich, Ph.D.
Associate Professor of Mathematics
563.588.7793 | Jacob.Heidenreich@loras.edu
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Karen Heidenreich
Instructor of Mathematics
563.588.7971 | karen.heidenreich@loras.edu
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William Hitchcock, M.B.A.
Professor of Computing and Information Technology
563.588.7286 | William.Hitchcock@loras.edu
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Angela Kohlhaas, Ph.D.
Associate Professor of Mathematics
563.588.7152 | Angela.Kohlhaas@loras.edu
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Michael Thompson, Ph.D.
Associate Professor of Computer Science
563.588.7570 | Michael.Thompson@loras.edu
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Matthew Rissler, Ph.D.
Associate Professor of Mathematics
563.588.7792 | Matthew.Rissler@loras.edu
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