Data Science, B.S.
The Saint Louis University Bachelor of Science in Data Science is an interdisciplinary program supported by the Department of Computer Science and the Department of Mathematics and Statistics. SLU's curriculum is modeled upon guidelines for undergraduate programs in data science as endorsed by the American Statistical Association's Board of Directors. Classes are small and are taught by enthusiastic instructors.
Curriculum Overview
The B.S. in data science is among the most rigorous degrees offered at SLU. This program combines carefully selected computer science, statistics and mathematics courses with four semesters of practica and capstone experiences. The result is an education rooted in the fundamentals but that also provides hands-on experience with cleaning, visualizing, analyzing and reporting on data. Students choose electives within the major to specialize in the computer science or statistical aspects of data science.
Fieldwork and Research Opportunities
Faculty in the data science program do research in machine learning, natural language processing, time series, topological data analysis, and in other areas of statistics, computer science and mathematics.
There are multiple research, internship and consulting opportunities for students in the data science program. Past students have done cross-disciplinary work with ArchCity Defenders, the Department of Sociology, the Department of Languages, Literature and Cultures, the Department of English and the Medical School Liver Center, while others have done work in data science itself, doing research with faculty within the departments of Mathematics and Statistics, the Department of Computer Science and the Lincoln Lab at MIT, among others.
The SLU Data Science Club provides students with an opportunity to practice their predictive modeling in competitions. Some competitions are hosted locally by SLU solely for students at SLU, while others pit SLU students against students and professionals from across the world.
Careers
The McKinsey Report estimated that the United States would face a shortfall of 140,000 to 190,000 people with deep analytical skills, while also needing 1.5 million managers and analysts with the know-how to make decisions based on the analysis of big data.
The Harvard Business Review calls data scientist "the sexiest job of the 21st century,” and Glassdoor has ranked data scientist as the No. 1 overall job in the USA in terms of the number of job openings, earning potential and career opportunities rating. Data is being produced in many places, and companies need employees who can analyze the data and communicate the results. Students with a B.S. in data science will be well-positioned to work in technology, government, research and consulting fields, among others.
91Ƭ Requirements
Saint Louis University also accepts the Common Application.
Freshman
All applications are thoroughly reviewed with the highest degree of individual care and consideration to all credentials that are submitted. Solid academic performance in college preparatory coursework is a primary concern in reviewing a freshman applicant’s file.
To be considered for admission to any Saint Louis University undergraduate program, applicants must be graduating from an accredited high school, have an acceptable HiSET exam score or take the General Education Development (GED) test.
Transfer
Applicants must be a graduate of an accredited high school or have an acceptable score on the GED.
Students who have attempted fewer than 24 semester credits (or 30 quarter credits) of college credit must follow the above freshmen admission requirements. Students who have completed 24 or more semester credits (or 30 quarter credits) of college credit mustsubmit transcripts from all previously attended college(s).
In reviewing a transfer applicant’s file, the Office of 91Ƭ holistically examines the student’s academic performance in college-level coursework as an indicator of the student’s ability to meet the academic rigors of Saint Louis University. Where applicable, transfer students will be evaluated on any courses outlined in the continuation standards of their preferred major.
International Applicants
All admission policies and requirements for domestic students apply to international students along with the following:
- Demonstrate English Language Proficiency
- Proof of financial support must include:
- A letter of financial support from the person(s) or sponsoring agency funding the time at Saint Louis University
- A letter from the sponsor's bank verifying that the funds are available and will be so for the duration of study at the University
- 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.
Tuition
Tuition | Cost Per Year |
---|---|
Undergraduate Tuition | $54,760 |
Additional charges may apply. Other resources are listed below:
Information on Tuition and Fees
Scholarships and Financial Aid
There are two principal ways to help finance a Saint Louis University education:
- Scholarships: Scholarships are awarded based on academic achievement, service, leadership and financial need.
- Financial Aid: Financial aid is provided through grants and loans, some of which require repayment.
Saint Louis University makes every effort to keep our education affordable. In fiscal year 2023, 99% of first-time freshmen and 92% of all students received financial aid and students received more than $459 million in aid University-wide.
For priority consideration for merit-based scholarships, apply for admission by December 1 and complete a Free Application for Federal Student Aid (FAFSA) by March 1.
For more information on scholarships and financial aid, visit the Office of Student Financial Services.
- Graduates will be able to use programming and other computer science skills to analyze and interact with data.
- Graduates will be able to apply statistics to analyze data sets.
- Graduates will be able to acquire and manage complex data sets.
- Graduates will be able to use technical skills in predictive modeling.
- Graduates will be able to visualize data to facilitate the effective presentation of data-driven insights.
Code | Title | Credits |
---|---|---|
University Undergraduate Core | 32-35 | |
Major Requirements | ||
䳧1070 | Introduction to Computer Science: Taming Big Data | 3 |
䳧1300 | Introduction to Object-Oriented Programming | 4 |
䳧2100 | Data Structures | 4 |
䳧4710 | Databases | 3 |
䳧4750 | Machine Learning | 3 |
Mathematics/Statistics Requirements | ||
Ѵձ1510 | Calculus I | 4 |
Ѵձ1520 | Calculus II | 4 |
Ѵձ1660 | Discrete Mathematics | 3 |
Ѵձ2530 | Calculus III | 4 |
Ѵձ3110 | Linear Algebra for Engineers | 3 |
ǰѴձ3120 | Introduction to Linear Algebra | |
մ3850 | Foundation of Statistics | 3 |
մ4870 | Applied Regression | 3 |
մ4880 | Bayesian Statistics and Statistical Computing | 3 |
Data Science Integration Requirements | ||
ٴմ1800 | Data Science Practicum I | 1 |
ٴմ2800 | Data Science Practicum II | 1 |
ٴմ4961 | Capstone Project I | 2 |
ٴմ4962 | Capstone Project II | 2 |
Major Electives | 12 | |
Select four courses, must include at least two CSCI courses and at least one STAT course, from the following: | ||
䳧2300 | Object-Oriented Software Design | |
䳧2500 | Computer Organization and Systems | |
䳧2510 | Principles of Computing Systems | |
䳧3100 | Algorithms | |
䳧3300 | Software Engineering | |
䳧4610 | Concurrent and Parallel Programming | |
䳧4620 | Distributed Computing | |
䳧4740 | Artificial Intelligence | |
䳧4760 | Deep Learning | |
䳧4830 | Computer Vision | |
䳧4845 | Natural Language Processing | |
մ4800 | Probability Theory | |
մ4840 | Time Series | |
մ4850 | Mathematical Statistics | |
General Electives | 24-27 | |
Total Credits | 120 |
Non-Course Requirements
All Science and Engineering B.A. and B.S. students must complete an exit interview/survey near the end of their bachelor's program.
Continuation Standards
Students must have a minimum of a 2.00 cumulative GPA in data science major courses by the conclusion of their sophomore year, must maintain a minimum of 2.00 cumulative GPA in these courses at the conclusion of each semester thereafter, and must be registered in at least one data sciencecourse counting toward their major in each academic year (until all requirements are completed).
Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollmentunless 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 | |
䳧1070 | Introduction to Computer Science: Taming Big Data † | 3 |
Ѵձ1660 | Discrete Mathematics | 3 |
Ѵձ1510 | Calculus I (Critical course: پھ 䰿鷡3200) † | 4 |
䰿鷡1000 | Ignite First Year Seminar | 2 |
䰿鷡1500 | Cura Personalis 1: Self in Community | 1 |
䰿鷡1900 | Eloquentia Perfecta 1: Written and Visual Communication | 3 |
Credits | 16 | |
Spring | ||
䳧1300 | Introduction to Object-Oriented Programming † | 4 |
Ѵձ1520 | Calculus II † | 4 |
ٴմ1800 | Data Science Practicum I † | 1 |
CORE1600 | Ultimate Questions: Theology | 3 |
General Electives | 3 | |
Credits | 15 | |
Year Two | ||
Fall | ||
䳧2100 | Data Structures † | 4 |
Ѵձ2530 | Calculus III | 4 |
CORE1200 | Eloquentia Perfecta 2: Oral and Visual Communication | 3 |
CORE1700 | Ultimate Questions: Philosophy | 3 |
Credits | 14 | |
Spring | ||
մ3850 | Foundation of Statistics | 3 |
ٴմ2800 | Data Science Practicum II | 1 |
CSCI Elective | 3 | |
Ѵձ3110 | Linear Algebra for Engineers | 3 |
CORE2500 | Cura Personalis 2: Self in Contemplation | 0 |
CORE3800 | Ways of Thinking: Natural and Applied Sciences | 3 |
General Electives | 3 | |
Credits | 16 | |
Year Three | ||
Fall | ||
䳧4710 | Databases | 3 |
մ4880 | Bayesian Statistics and Statistical Computing | 3 |
CORE2800 | Eloquentia Perfecta 3: Creative Expression | 3 |
CORE3400 | Ways of Thinking: Aesthetics, History, and Culture | 3 |
General Elective | 3 | |
Credits | 15 | |
Spring | ||
մ4870 | Applied Regression | 3 |
䳧4750 | Machine Learning | 3 |
CORE3600 | Ways of Thinking: Social and Behavioral Sciences | 3 |
General Electives | 6 | |
Credits | 15 | |
Year Four | ||
Fall | ||
ٴմ4961 | Capstone Project I | 2 |
CSCI/STAT Electives | 6 | |
CORE3500 | Cura Personalis 3: Self in the World | 1 |
General Electives | 6 | |
Credits | 15 | |
Spring | ||
ٴմ4962 | Capstone Project II | 2 |
CSCI/STAT Elective | 3 | |
General Electives | 9 | |
Credits | 14 | |
Total Credits | 120 |
- †
Students must earn a C- or better.
- ‡
Strongly recommended for capstone
Program Notes
• մ3850 Foundation of Statistics (3 cr) and 䳧2100 Data Structures (4 cr) are crucial to this program, as they serve as prerequisites for all of the upper division STAT and CSCI courses. As such, they should be taken as soon as reasonably possible.
• Possible STAT electives include մ4840 Time Series (3 cr), MATH4800 Probability Theory (3 cr) and մ4850 Mathematical Statistics (3 cr).
• Possible CSCI electives include 䳧2300 Object-Oriented Software Design (3 cr), 䳧3100 Algorithms (3 cr), 䳧3300 Software Engineering (3 cr), 䳧4610 Concurrent and Parallel Programming (3 cr), 䳧4620 Distributed Computing (3 cr), 䳧4740 Artificial Intelligence (3 cr), 䳧4760 Deep Learning (3 cr), 䳧4830 Computer Vision (3 cr), and 䳧4845 Natural Language Processing (3 cr).
• At least one elective must have a STAT designator and at least two electives must have a CSCI designator.
• Twelve hours of CSCI/STAT electives are required.
2+SLU programs provide a guided pathway for students transferring from a partner institution.