Analytics, M.S.
Learn 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 in Saint Louis University's Master of Science in Analytics program, you'll gain skills in project management and 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 the fall or spring. 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 a wide range of academic and professional backgrounds, providing you the opportunity to learn from a network of peers.
Faculty
As a student in the School for Professional Studies at Saint Louis University, you’ll learn from exceptional faculty who are leading experts in their fields. They bring real-world knowledge to the classroom and are dedicated to your professional success. Learn more on our faculty page.
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.
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 https://www.slu.edu/financial-aid/index.php.
- 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.
Admission Requirements
- Completed application
- Undergraduate degree (most successful applicants have an undergraduate grade point average of 3.0 or better)
- Official transcript from a degree-granting institution
- Statement of purpose (about 500 words)
- Resume or curriculum vitae
- External reference recommendations (encouraged but not required)
Upon admission, a new student must successfully complete a virtual meeting with their academic coach to be enrolled in first term coursework.
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
- PTE: 54
• Academic records, in English translation, of students who have undertaken postsecondary 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.
Program Requirements
| 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 | ||
Critical course: AA 5750 |
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 | ||
Critical course: AA 5800 |
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 | |