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Data Science, B.S.

The pro 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.

Admission Requirements

Begin Your Application

pro 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 pro 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 Admission 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 pro. 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 pro
    • 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:

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Information on Tuition and Fees

Miscellaneous Fees

Information on Summer Tuition

Scholarships and Financial Aid

There are two principal ways to help finance a pro 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.

pro 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.

  1. Graduates will be able to use programming and other computer science skills to analyze and interact with data.
  2. Graduates will be able to apply statistics to analyze data sets.
  3. Graduates will be able to acquire and manage complex data sets.
  4. Graduates will be able to use technical skills in predictive modeling.
  5. Graduates will be able to visualize data to facilitate the effective presentation of data-driven insights.
University Undergraduate Core32-35
Major Requirements
䳧1070Introduction to Computer Science: Taming Big Data3
䳧1300Introduction to Object-Oriented Programming4
䳧2100Data Structures4
䳧4710Databases3
䳧4750Machine Learning3
Mathematics/Statistics Requirements
Ѵձ1510Calculus I4
Ѵձ1520Calculus II4
Ѵձ1660Discrete Mathematics3
Ѵձ2530Calculus III4
Ѵձ3110Linear Algebra for Engineers3
ǰѴձ3120 Introduction to Linear Algebra
մ3850Foundation of Statistics3
մ4870Applied Regression3
մ4880Bayesian Statistics and Statistical Computing3
Data Science Integration Requirements
ٴմ1800Data Science Practicum I1
ٴմ2800Data Science Practicum II1
ٴմ4961Capstone Project I2
ٴմ4962Capstone Project II2
Major Electives12
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 Electives24-27
Total Credits120

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.

Plan of Study Grid
Year One
FallCredits
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
Credits16
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
Credits15
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
Credits14
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
Credits16
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
Credits15
Spring
մ4870 Applied Regression 3
䳧4750 Machine Learning 3
CORE3600 Ways of Thinking: Social and Behavioral Sciences 3
General Electives 6
Credits15
Year Four
Fall
ٴմ4961 Capstone Project I 2
CSCI/STAT Electives 6
CORE3500 Cura Personalis 3: Self in the World 1
General Electives 6
Credits15
Spring
ٴմ4962 Capstone Project II 2
CSCI/STAT Elective 3
General Electives 9
Credits14
Total Credits120

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.

Data Science, B.S. (STLCC 2+SLU)