Students earning a MS in Data Science will gain a wide variety of skills needed to work with many different types of data, and to analyze, visualize, and extract useful information from data in a variety of ways. They will apply those skills in various contexts, especially during their capstone consulting class or thesis work. This 30-credit program has two tracks (thesis and non-thesis), can be completed full-time or part-time, and includes courses from Computer Science, Statistics, and Mathematics.

The Data Science (MS) also offers an Accelerated Master's Pathway for Undergraduate students to complete their Graduate studies in a fifth year.  Further details of the AMP, including the suggested sequence of courses, can be found under the Curriculum tab.

Related Programs

Curriculum

The Master of Science in Data Science requires 30 hours of coursework and a capstone project. Students may elect to conduct research and write a thesis instead of the capstone.

Non-Thesis Track

Course List

Code	Title	Hours
Statistics Requirements		6
STAT 408	Applied Regression Analysis
STAT 410	Categorical Data Analysis
Computer Science Requirements		6
COMP 453	Database Programming
COMP 458	Big Data Analytics
Data Science Foundation		9
DSCI 401	Introduction to Data Science
STAT 438	Introduction to Predictive Analytics
or COMP 479	Machine Learning
DSCI 470	Data Science Consulting (Capstone)
Three Statistics or Computer Science 400 Level Electives 1,2		9
Total Hours		30

¹

A list of electives can be found later in this document.

²

For electives, students must take one COMP class for 3 credit hours, one STAT class for 3 credit hours, and one course in either COMP or STAT for 3 credit hours.

Thesis Track

Course List

Code	Title	Hours
Statistics Requirements		6
STAT 408	Applied Regression Analysis
STAT 410	Categorical Data Analysis
Computer Science Requirements		6
COMP 453	Database Programming
COMP 458	Big Data Analytics
Data Science Core		7
DSCI 401	Introduction to Data Science
STAT 438	Introduction to Predictive Analytics
or COMP 479	Machine Learning
Research		11
DSCI 494	Data Science Research Design
DSCI 499	Data Science Research
DSCI 595	Thesis Supervision
Total Hours		30

Electives

The list of electives is divided into primary and secondary electives. Primary electives are recommended classes to all data science students. Secondary electives are courses that may be a good fit for students with a specific area of emphasis. Students need to coordinate with the Graduate Program Director if they plan to take a secondary elective.

Course List

Code	Title	Hours
Primary COMP electives
COMP 406	Data Mining	3
COMP 429	Natural Language Processing	3
COMP 484	Artificial Intelligence	3
COMP 487	Deep Learning	3
COMP 488	Computer Science Topics (If the topic is relevant to data science. Example: Topics in Computer Vision)	1-4
Primary STAT electives
STAT 411	Applied Survival Analysis	3
STAT 421	Math Modeling & Simulation	3
or COMP 421	Math Models & Simulation
STAT 451	Applied Nonparametric Methods	3
STAT 444	Longitudinal Data Analysis and Mixed Modeling	3
STAT 488	Topics in Statistics (If the topic is relevant to data science. Examples: Multivariate Statistics, Bayesian Statistics)	1-3
Secondary COMP electives
COMP 436	Markup Languages	3
COMP 441	Human-Computer Interaction	3
COMP 460	Algorithms & Complexity	3
COMP 405	Database Administration	3
COMP 412	Open Source Computing	3
COMP 413	Intermediate Object-Oriented Development	3
COMP 418	Combinatorial Mathematics	3
COMP 424	Client-Side Web Design	3
COMP 474	Software Engineering	3
COMP 422	Software Development for Wireless and Mobile Devices	3
COMP 417	Social and Ethical Issues in Computing	3
COMP 490	Independent Project	1-6
COMP 499	Internship	1-6
COMP 477	IT Project Management	3
Secondary STAT electives
STAT 403	SAS Program & Applied Statistics	3
STAT 407	Statistical Design	3
STAT 404	Probability & Statistics I	3
STAT 405	Probability & Statistics II	3
STAT 498	Independent Study Statistics	1-6

Suggested Sequence of Courses

The below sequence of courses is meant to be used as a suggested path for completing coursework.  An individual student’s completion of requirements depends on course offerings in a given term as well as the start term for a major or graduate study.  Students should consult their advisor for assistance with course selection.

Traditional Master's Program

Non-thesis Track

Plan of Study Grid

Year One
Fall		Hours
DSCI 401	Introduction to Data Science	4
STAT 408	Applied Regression Analysis	3
COMP 453	Database Programming	3
	Hours	10
Spring
STAT 410	Categorical Data Analysis	3
COMP 458	Big Data Analytics	3
COMP or STAT 400-Level Elective 1		3
	Hours	9
Year Two
Fall
STAT 438 or COMP 479	Introduction to Predictive Analytics or Machine Learning	3
COMP or STAT 400-Level Elective 1		3
COMP or STAT 400-Level Elective 1		3
DSCI 470	Data Science Consulting	2
	Hours	11
	Total Hours	30

¹

For electives, students must take one COMP class for 3 credit hours, one STAT class for 3 credit hours, and one course in either COMP or STAT for 3 credit hours.

 Thesis Track

Plan of Study Grid

Year One
Fall		Hours
DSCI 401	Introduction to Data Science	4
STAT 408	Applied Regression Analysis	3
DSCI 494	Data Science Research Design	2
	Hours	9
Spring
STAT 410	Categorical Data Analysis	3
DSCI 499	Data Science Research	3
COMP 458	Big Data Analytics	3
	Hours	9
Year Two
Fall
STAT 438 or COMP 479	Introduction to Predictive Analytics or Machine Learning	3
DSCI 499	Data Science Research	3
COMP 453	Database Programming	3
	Hours	9
Spring
DSCI 499	Data Science Research	2
DSCI 595	Thesis Supervision	1
	Hours	3
	Total Hours	30

Accelerated Master's Pathway 

Non-Thesis Track

Plan of Study Grid

Senior
Fall		Hours
STAT 438 or COMP 479	Introduction to Predictive Analytics 1 or Machine Learning	3
	Hours	3
Spring
STAT 410	Categorical Data Analysis 1	3
COMP 458	Big Data Analytics 1	3
	Hours	6
Master's
Fall
DSCI 401	Introduction to Data Science	4
STAT 408	Applied Regression Analysis	3
COMP 453	Database Programming	3
	Hours	10
Spring
COMP or STAT 400-Level Elective 2		3
COMP or STAT 400-Level Elective 2		3
COMP or STAT 400-Level Elective 2		3
DSCI 470	Data Science Consulting	2
	Hours	11
	Total Hours	30

¹

Other classes can be allowed with permission from the Data Science Director and Graduate Program Director.

²

For electives, students must take one COMP class for 3 credit hours, one STAT class for 3 credit hours, and one course in either COMP or STAT for 3 credit hours.

Thesis Track

Plan of Study Grid

Senior
Fall		Hours
STAT 438 or COMP 479	Introduction to Predictive Analytics 1 or Machine Learning	3
	Hours	3
Spring
STAT 410	Categorical Data Analysis 1	3
COMP 458	Big Data Analytics 1	3
	Hours	6
Master's
Fall
DSCI 401	Introduction to Data Science	4
STAT 408	Applied Regression Analysis	3
DSCI 494	Data Science Research Design	2
DSCI 499	Data Science Research	4
	Hours	13
Spring
DSCI 499	Data Science Research	4
COMP 453	Database Programming	3
DSCI 595	Thesis Supervision	1
	Hours	8
	Total Hours	30

¹

Other classes can be allowed with permission from the Data Science Director and Graduate Program Director.

²

For electives, students must take one COMP class for 3 credit hours, one STAT class for 3 credit hours, and one course in either COMP or STAT for 3 credit hours.

Guidelines for Accelerated Master's Pathways

In Accelerated Master’s Pathways, students share limited, authorized credits between their Undergraduate and Graduate degrees to facilitate completion of both degrees in a shorter amount of time. Shared credits are Graduate level credit hours (400-level or higher) taken during the Undergraduate career and then applied both to the Undergraduate degree and towards Graduate program requirements.

Admission and Matriculation

Accelerated Master's Pathways are designed to enhance opportunities for advanced training for Loyola’s Undergraduates. Admission to these programs is competitive and will depend upon a positive review of credentials by the Graduate program. Accordingly, the admission requirements for these programs may be higher than those required if the Master’s degree were pursued entirely after the receipt of a Bachelor’s degree.

Students enrolled in an Accelerated Master's Pathway who choose not to continue to the Master’s degree program upon completion of the Bachelor’s degree will face no consequences.

Ideally, a student will apply for admission to an AMP program as they approach 90 credit hours in their Undergraduate career.

Students will not officially matriculate into the Master’s degree program and be labeled as a Graduate student by the university, with accompanying changes to tuition and Financial Aid (see below), until the Undergraduate degree has been awarded. Once admitted to the Graduate program, students must meet the academic standing requirements of their Graduate program as they complete the program curriculum.

Advising and Registration

Students in their final Undergraduate year will work with Advising in the home School of their Bachelor's program(s), as well as the Graduate Program Director of the Master’s program. Any 400-level or higher courses that the student plans to enroll in should be reviewed by both advisors to ensure that these courses will complete requirements for both degrees.

Registration in Graduate level courses during the Undergraduate year may require assistance from the Graduate Program Director and/or the student’s current academic advisor to enroll.

Shared Credits

Only courses taken at the 400-level or higher will count toward the Graduate program. At the Undergraduate level, students are restricted to enrolling in and sharing up to the number of Graduate level credits explicitly indicated in the catalog for their selected AMP program.

In general, Graduate level coursework should not be taken prior to admission into the Accelerated Master's Pathway. Exceptions may be granted for professional programs where curriculum for the Accelerated Master's Pathway is designed to begin earlier. On the recommendation of the program’s Graduate Program Director, students may take one of their Graduate level courses before they are admitted to the Accelerated Master’s Pathway if they have advanced abilities in their discipline and course offerings warrant such an exception.

Degree Requirements and Conferral

Undergraduate degree requirements are in no way impacted by admission to an Accelerated Master’s Pathway. Students should not, for example, attempt to negotiate themselves out of a writing intensive requirement on the basis of admission to a Graduate program.

The program’s Graduate Program Director will designate credit hours to be shared through the advising form and Master’s degree conferral review process. Graduate credit hours taken during the Undergraduate career will not be included in the Graduate GPA calculation.

If students wish to transfer credits from another university to Loyola University Chicago, the program’s Graduate Program Director will review the relevant syllabus or syllabi to determine whether it meets the criteria for a 400-level course or higher.

Programs with specialized accreditation requirements that allow programs to offer Graduate curriculum to Undergraduate students will conform to those specialized accreditation requirements.

Degrees are awarded sequentially. All details of Undergraduate commencement are handled in the ordinary way as for all students in the School/College/Institute. Once matriculated in the Graduate program, students abide by the graduation deadlines set forth by the Graduate program. Students in these programs must be continuously enrolled from Undergraduate to Graduate degree program unless given explicit permission by their program for a gap year or approved leave of absence. In offering the option of an Accelerated Master’s Pathway, the university is making possible the acceleration of a student’s Graduate degree completion. It should be understood that students may not request deferral of their matriculation into the Master’s degree program. If students would like to delay their Graduate studies after earning the Undergraduate degree, they may apply for admission to the traditional Master’s degree program. Any application of Graduate credit earned while in the Undergraduate program is subject to the policies of the Graduate degree granting school.

Responsible Conduct of Research

All PhD students and students in thesis-based Master's degree programs must successfully complete UNIV 370 Responsible Conduct in Research and Scholarship or other approved coursework in responsible conduct of research as part of the degree requirements. It is strongly recommended that students complete this two-day training before beginning the dissertation/thesis stage of the program.

Graduate & Professional Standards and Regulations

Students in graduate and professional programs can find their Academic Policies in Graduate and Professional Academic Standards and Regulations under their school. Any additional University Policies supersede school policies.

Learning Outcomes

• The ability to manage large data sets in preparation for data science analysis.
• A working knowledge of statistical techniques and computer algorithms, and the ability to apply these methods to a wide array of real-world problems.
• The ability to perform a data science analysis from beginning to end while adhering to the principles of reproducible and ethical research.
• The ability to program in both the R and Python programming languages.
• Complete a project demonstrating competence in the field of data science. Non-thesis track: Students will be required to complete a real-world data science project prior to graduating from this program, either through our consulting course, an internship, an independent study, or other appropriate project. Thesis track: Students will be required to undertake a research project culminating in a thesis.