Analytics, M.S.
Offered through Saint Louis University's School for Professional Studies, the Master of Science in Analytics curriculum will teach you to design and implement analytics projects to solve complex organizational problems through statistical and analytical techniques for analyzing datasets of various sizes. Through your coursework, you'll gain skills in project management, decision making, and learn how to communicate the intricacies of complex data more effectively.
Along the way, you’ll learn from a network of diverse peers from around the world, merging technology with human and organizational structures as you engage in knowledge discovery, management, and dissemination of industry-critical knowledge. You can also earn a graduate certificate that complements a master’s degree, often without taking additional credits, allowing you to tailor the program to your specific interests.
As part of the School for Professional Studies, this 33-credit master's program offers data-driven professionals like you a flexible option to meet your personal career goals. With multiple start terms, you can begin the master's program in fall, spring or summer. All courses are offered in online and hybrid formats in eight-week terms, making advanced education more accessible for working professionals. You will join a community of academics and practitioners from around the world and from a wide range of academic and professional backgrounds, providing the opportunity to learn from a network of peers.
Careers
SLU's M.S. in Analytics provides students with skills in data mining, data visualization, predictive analytics, design and implementation of analytics projects and data management. Graduates from this program are ready to discover the patterns within large quantities of data and provide insightful recommendations that inform organizational decision-making.
Recent trends in the job market data and experts’ predictions indicate that the job market for data analytics, business analytics and similar skill sets will only continue to grow in the future.
Admission Requirements
- Completed application.
- Official transcript from a degree-granting institution.
- Most successful applicants have an undergraduate grade point average of 3.0 or better.
- At least two years of work experience is recommended (includes full-time and part-time employment, internships, co-ops and similar experiences).
- Three external reference evaluations (not required for certificates).
- Statement of professional goals (approximately 500 words suggested) articulating what attracted you to this program, and how a master's degree in this field will benefit you in your present or future career.
- Resume or curriculum vitae.
Requirements for International Students
All admission policies and requirements for domestic students apply to international students along with the following:
- Applicants must demonstrate English language proficiency. Some examples of demonstrated English language proficiency include minimum score requirements for the following standardized tests: \
- Paper-based TOEFL: 550
- Internet-based TOEFL: 80
- IELTS: 6.5 o PTE: 54
• Academic records, in English translation, of students who have undertaken post-secondary studies outside the United States must include the courses taken and/or lectures attended, practical laboratory work, the maximum and minimum grades attainable, the grades earned or the results of all end-of-term examinations, and any honors or degrees received. WES and ECE transcripts are accepted.
Application and Assistantship Deadlines
Students should apply for the fall semester by August 6 and the spring semester by January 7.
Review Process
Applications are reviewed by program directors.
Scholarships and Financial Aid
For priority consideration for graduate assistantship, apply by Feb. 1.
For more information, visit the student financial services office online at http://finaid.slu.edu.
- Graduates will be able to employ research methodologies appropriate for the field of analytics
- Graduates will be able to apply program-specific knowledge to address practical problems using an ethical, evidence-based framework.
- Graduates will be able to implement analytics systems that facilitate context-appropriate decision making.
- Graduates will be able to utilize argumentation skills appropriate for a given problem or context.
| Code | Title | Credits |
|---|---|---|
| Graduate Core Courses | ||
| AA 5221 | Applied Analytics & Methods I | 3 |
| ORLD 5050 | Ethical, Evidence-Based Decision Making | 3 |
| Foundation Courses | ||
| AA 5000 | Foundations of Analytics | 3 |
| AA 5100 | Information Retrieval | 3 |
| AA 5200 | Visualization, Feedback and Dissemination | 3 |
| AA 5222 | Applied Analytics & Methods II: Survey Approaches | 3 |
| or AA 5223 | Applied Analytics & Methods II: Experimental Approaches | |
| AA 5250 | Project Management | 3 |
| Electives | ||
| Select three of the following: | 9 | |
| Advanced Analytics | ||
| Contemporary Issues in Analytics | ||
| Simulation and Modeling | ||
Elective | Student's choice outside of program | |
| Applied Research Project | ||
| AA 5961 | Applied Analytics Master’s Project - I | 1 |
| AA 5962 | Applied Analytics Master’s Project - II | 1 |
| AA 5963 | Applied Analytics Master’s Project - III | 1 |
| Total Credits | 33 | |
Continuation Standards
Students must maintain a cumulative grade point average (GPA) of 3.00 in all graduate/professional courses.
Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.
Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.
This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester. Requirements, course availability and sequencing are subject to change.
| Year One | ||
|---|---|---|
| Fall | Credits | |
| Fall 1 | ||
| Critical course: AA 5000 | Foundations of Analytics | 3 |
| Fall 2 | ||
| Critical course: ORLD 5050 | Ethical, Evidence-Based Decision Making | 3 |
| Credits | 6 | |
| Spring | ||
| Spring 1 | ||
| Critical course: AA 5221 | Applied Analytics & Methods I | 3 |
| Spring 2 | ||
| Critical course: AA 5222 or AA 5223 |
Applied Analytics & Methods II: Survey Approaches or Applied Analytics & Methods II: Experimental Approaches |
3 |
| Credits | 6 | |
| Summer | ||
| AA 5961 | Applied Analytics Master’s Project - I | 1 |
| Credits | 1 | |
| Year Two | ||
| Fall | ||
| Fall 1 | ||
| Critical course: AA 5200 | Visualization, Feedback and Dissemination | 3 |
| AA 5250 | Project Management | 3 |
| OR | ||
| Contemporary Issues in Analytics | ||
| Fall 2 | ||
| AA 5962 | Applied Analytics Master’s Project - II | 1 |
| Credits | 7 | |
| Spring | ||
| Spring 1 | ||
| AA 5300 | Advanced Analytics | 3 |
| OR | ||
| Simulation and Modeling | ||
| Spring 2 | ||
| Critical course: AA 5100 | Information Retrieval | 3 |
| Credits | 6 | |
| Summer | ||
| AA 5300 | Advanced Analytics | 3 |
| OR | ||
| Simulation and Modeling | ||
| Credits | 3 | |
| Year Three | ||
| Fall | ||
| Fall 1 | ||
| AA 5750 | Contemporary Issues in Analytics | 3 |
| OR | ||
| Project Management | ||
| Fall 2 | ||
| Credits | 3 | |
| Spring | ||
| AA 5963 | Applied Analytics Master’s Project - III | 1 |
| Credits | 1 | |
| Total Credits | 33 | |