Loyola University Chicago

Mathematics and Statistics

Curriculum

Whereas other statistics programs in the Chicago area tend to focus more on theoretical statistics, Loyola’s emphasis on applied statistics gives students an advantage in many evidence-based fields such as the industrial, biomedical, marketing, educational, financial, and contract (CRO) sectors.

Our 29-credit, three-semester Master of Science program (based on full-time study) is novel in that it requires students to have completed just one introductory statistics course plus two calculus courses (differential and integral) during undergraduate studies for entrance into our program; multivariate calculus is not required to gain admission (although some students have indeed covered the third calculus course).

Our program can also be completed on a part-time basis, as has been the case for several of our students and graduates. Although some of the classes are taught in the late afternoon and evenings, others are taught during the day, so part-time students - who are working full-time - need to have some flexibility in their work schedules (i.e., making up work time outside of traditional working hours).

Curriculum

Upon completion of our MS program in Applied Statistics, students are expected to have:

  1. Mastered the art and science of choosing and/or developing the appropriate statistical model for a given dataset-situation, and have mastered the skill of interpreting the chosen model.
  2. Received sufficient exposure to basic theorems and proofs used in introductory probability and statistical inference.
  3. Worked with data from application fields such as public/global health, medical, industrial and environmental research.
  4. Received training to ethically apply statistical training in the real world.
  5. Obtained hands-on experience and assimilated the course material via our 2-credit Statistical Consulting capstone/practicum class.
  6. Sufficiently mastered the course and practicum material to either obtain gainful employment in the field or attend a Ph.D. program.

Students are required to complete nine three-credit courses; at least seven of these nine course must be at the 400-level. When including the two-credit capstone Statistical Consulting course mentioned above, this is a total of 29 credits for the MS degree. Regardless of the specialization, all students are required to take the following six courses (17 credits):

  • Elements of Statistical Consulting—STAT 401 (2 credits)
  • SAS Programming and Applied Statistics—STAT 403
  • Probability and Statistical Theory I—STAT 404
  • Probability and Statistical Theory II—STAT 405
  • Statistical Design of Analysis of Experiments—STAT 407
  • Applied Regression Analysis—STAT 408

The four (12 credits) additional elective courses may be chosen from the following:

  • Stochastic Processes (STAT 406)
  • Categorical Data Analysis (STAT 410)
  • Applied Survival Analysis (STAT 411)
  • Applied Multivariate Statistical Analysis (STAT 488)
  • Advanced Statistical Theory (STAT 426)
  • Advanced Biostatistics (STAT 436)
  • Predictive Analytics (STAT 438)
  • Sampling Methods (STAT 440)
  • Bayesian Statistical Methods (STAT 441)
  • Longitudinal Data Analysis and Mixed Modelling (STAT 444)
  • Applied Spatial Statistics (STAT 445)
  • Applied Nonlinear Regression (STAT 447)
  • Data Visualization (STAT 450)
  • Nonparametric Statistics (STAT 451)
  • Topics in Statistics and Biostatistics (STAT 488)
  • Other course approved by the Statistics Graduate Program Director (GPD), including as many as two relevant MATH or STAT courses at the 300 level. For example, interested students may take as many as two of the following courses: Combinatorial Mathematics (MATH 418), Algebraic Coding Theory (MATH 428), Financial Mathematics I (MATH 445), or Structural Equation Modeling (PSYC 493). Courses outside of the STAT designation must be approved by the GPD.
  • Independent Study in Statistics and Biostatistics—STAT 499

Students specializing in Biostatistics are strongly encouraged to take electives from the following: STAT 410STAT 411STAT 436, STAT 444, and STAT 451. 

 Here is the typical sequence for students entering during the Fall semester: 

First Fall SemesterSpring SemesterSecond Fall Semester
STAT 403
STAT 404
STAT 408
STAT 405
Elective
Elective
STAT 407
Elective
Elective
Statistical Consulting Capstone (2 Credits)
9 Credits 9 Credits 11 Credits

Graduate students are expected to maintain an average of not less than “B” (3.0 of 4.0). No more than two grades of “C” or “C+” and no grades lower than “C” may be counted as fulfilling degree requirements. Such grades, however, will be calculated in the GPA. No student will graduate with less than a 3.00 average for all graduate level courses and undergraduate courses taken for graduate credit.

In addition, students who earn multiple grades of “C” are subject to review and possible withdrawal from the program.

An important defining characteristic of the field of Applied Statistics is that it is inherently collaborative in nature. This MS program underscores the process of shared learning, joint discovery, dialogue, and communication. Participation is required in a two-credit course in Statistical Consulting (STAT 401), in which consulting techniques are explored and discussed. Students take this course toward the end of their studies, so that it will serve as a true capstone course, one which brings together all previous classes and helps students to synthesize their knowledge.

In addition, cohorts of students are paired with Loyola researchers to assist them with the analysis of their data so as to aid them in answering their underlying queries. This service will be provided to Loyola researchers on a voluntary basis and pro bono. Researchers are encouraged to present important background and research hypotheses to the class as a whole so that the class can discuss the project in a case study format. Statistics students enrolled in this course will be actively supervised by Statistics faculty members and will report their findings and experiences back to the class upon completion of the project.

As a result, this program interacts intensively with researchers in need of assistance with data analysis. For example, with those in the Departments of Biology, Biostatistics and Bioinformatics, Chemistry, Economics, Psychology, and Sociology, just to name a few. Students may also find opportunities to collaborate with researchers in the Center for Urban Environmental Research and Policy (CUERP), the Center for Urban Research and Learning (CURL), the Parmly Hearing Institute, or university administration. 

Specializations

SpecializationDescription
Biostatistics  The Biostatistics specialization covers non- and pre-clinical statistical methods, bioassay, statistical genetics, clinical trials, and bioinformatics.
Environmental Statistics The Environmental Statistics specialization addresses Geographic Information Systems (GIS), spatial statistics, and environmetrics.
General Applied Statistics The specialization in General Applied Statistics includes non-medical applications such as actuarial, commercial, data-mining, industrial, marketing, and national defense.
Predictive Analytics/Modeling The Predictive Modeling specialization focuses on big data analytics and modeling.

Further Information

For more information on our curriculum and program, visit our Curriculum Frequently Asked Questions page.