Loyola University Maryland

Data Science

Data Science Major

Data Science is the field of the twenty-first century.  It sits at the nexus of business, computer science, and statistics and as such, is a blending of the three fields. The goal of the data scientist is to perform analyses and make predictions using disparate sources of data. These analyses and predictions can help organizations and governments run more efficiently. The overarching goal of the field is the better use of information to make a difference in the world.

A major in Data Science prepares students to understand and enter this exciting field that unearths new knowledge on a daily basis. Technical skill coupled with a strong liberal arts education makes Loyola data science graduates especially desirable to employers. Typically, graduates assume professional responsibilities in positions such as data analyst or data scientist. Graduates are also prepared to continue their studies in data science or allied fields in graduate school.

Students majoring in data science will divide their major level courses among computer science, information systems, and statistics. The capstone course brings the major together in a culminating experience. In this course, students work with a client to apply their skills to a real problem.

The Data Science program leads to a Bachelor of Science (B.S.) degree. Faculty advisors help students coordinate their elective courses with their career plans. A minor in Data Science is also available.

Requirements for the Major

Requirements for a major and an example of a typical program of courses are as follows:

Foundational Component (12 required courses)

  • CS 151 - Computer Science through Programming
  • CS 295 - Discrete Structures or MA 295 - Discrete Structures or MA 395 - Discrete Methods
  • DS 303 - Discovering Information in Data
  • DS 496 - Ethical Data Science Capstone
  • IS 251 - Data Analytics and Information Systems or BH 251 - Data Analytics and Information Systems *
  • IS 353 - Data Management and Database Systems or CS 485 - Database Management Systems
  • IS 358 - Business Intelligence and Data Mining
  • MA 251 - Calculus I
  • ST 210 - Introduction to Statistics or ST 265 - Biostatistics or EC 220 - Business Statistics
  • ST 310 - Statistical Computing
  • ST 465 - Experimental Research Methods
  • ST 472 - Applied Multivariate Analysis

* Restricted to Sellinger Scholars.

Elective Component

Choose three courses from the following:

  • CS 312 - Object-Oriented Software Design
  • CS 456 - Web Programming or IS 458 - Web-Enabled Entrepreneurial Project
  • CS 484 - Artificial Intelligence
  • CS 487 - Big Data
  • EC 420 - Econometrics
  • EC 425 - Applied Economic Forecasting
  • IS 453 - Information Systems Analysis and Design
  • IS 460 - Data Visualization
  • MA 301 - Introduction to Linear Algebra
  • MA 481 - Operations Research
  • MA 485 - Stochastic Processes
  • MK 415 - Digital Marketing and Analytics
  • ST 461 - Elements of Statistical Theory I: Distributions
  • ST 466 - Experimental Design
  • ST 473 - Statistical Learning and Big Data
  • ST 475 - Survival Analysis and Generalized Linear Models
  • Appropriate course as approved by program director

For course descriptions, please see the Undergraduate Academic Catalog.

Sample Four-Year Undergraduate Curriculum

First Year

Fall Term

  • CS 151 - Computer Science through Programming
  • MA 251 - Calculus I
  • WR 100 - Effective Writing
  • Social Science Core
  • Language Core

Spring Term

  • ST 210 - Introduction to Statistics or ST 265 - Biostatistics or EC 220 - Business Statistics
  • IS 251 - Data Analytics and Information Systems
  • EN 101 - The Art of Reading
  • Social Science Core
  • Elective

Sophomore Year

Fall Term

  • CS 295 - Discrete Structures or MA 295 - Discrete Structures or MA 395 - Discrete Methods
  • DS 303 - Discovering Information in Data
  • PL 201 - Foundations of Philosophy or TH 201 - Theology Matters
  • HS-100 Level Core Course

Spring Term

  • IS 353 - Data Management and Database Systems or CS 485 - Database Management Systems
  • ST 310 - Statistical Computing
  • EN 200-Level or HS 300-Level Course
  • Fine Arts Core
  • Elective

Junior Year

Fall Term

  • IS 358 - Business Intelligence and Data Mining
  • ST 465 - Experimental Research Methods
  • PL 201 - Foundations of Philosophy or TH 201 - Theology Matters
  • Elective
  • Elective

Spring Term

  • ST 472 - Applied Multivariate Analysis
  • DS Elective
  • PL 200-Level or TH 200-Level Course
  • Elective
  • Elective

Senior Year

Fall Term

  • DS Elective
  • DS Elective
  • Ethics Core
  • Elective
  • Elective

Spring Term

  • DS 496 - Ethical Data Science Capstone
  • Elective
  • Elective
  • Elective
  • Elective

Notes

EC 102 and EC 103 are recommended to fulfill the Social Science Core.

MA 151 may be substituted for MA 251 with permission of the program director.

CS 212 is a prerequisite for CS 312. MA 351 is a prerequisite for ST 461.

Up to two graduate-level Data Science courses may be taken as a DS Elective with approval from the program director.

CS 485 and ST 465 are offered every other year.

The Data Science major cannot be combined with a minor in Computer Science, Information Systems, or Statistics. Students may double major with these programs.

An internship as DS 499 may be taken for university credit, but does not count as an elective in the BS DS. It is not anticipated that this course would substitute for the capstone as the ethical component in the capstone is particularly important. This aspect would not necessarily be present in the internship. Instead it is expected that the internship experience may enhance the capstone experience for the student by providing the relationship from which the student can develop the capstone project.

Dr. Olsen
Faculty

Megan Olsen, Ph.D.

Megan Olsen, Ph.D., aims to improve access for all students interested in computer science

Computer Science