Program Overview

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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.

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 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.

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.

Admission Requirements

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 applicants file.

Tuition

Tuition Per Year: $56,960

Scholarships and Financial Aid

There are two principal ways to help finance a Saint Louis University education: scholarships and financial aid.

Program Requirements

Data science students must complete a minimum total of 62 credits for the major.

• University Undergraduate Core: 32-35 credits
• Major Requirements:
  • CSCI 1070: Introduction to Computer Science: Taming Big Data: 3 credits
  • CSCI 1300: Introduction to Object-Oriented Programming: 4 credits
  • CSCI 2100: Data Structures: 4 credits
  • CSCI 4710: Databases: 3 credits
  • CSCI 4750: Machine Learning: 3 credits
  • Mathematics/Statistics Requirements:
    • MATH 1510: Calculus I: 4 credits
    • MATH 1520: Calculus II: 4 credits
    • MATH 1660: Discrete Mathematics: 3 credits
    • MATH 2530: Calculus III: 4 credits
    • MATH 3110: Linear Algebra for Engineers: 3 credits
    • STAT 3850: Foundation of Statistics: 3 credits
    • STAT 4870: Applied Regression: 3 credits
    • STAT 4880: Bayesian Statistics and Statistical Computing: 3 credits
  • Data Science Integration Requirements:
    • DATA 1800: Data Science Practicum I: 1 credit
    • DATA 2800: Data Science Practicum II: 1 credit
    • DATA 4961: Capstone Project I: 2 credits
    • DATA 4962: Capstone Project II: 2 credits
  • Major Electives: 12 credits
    • Select four courses, must include at least two CSCI courses and at least one STAT course, from the following:
      • CSCI 2300: Object-Oriented Software Design
      • CSCI 2500: Computer Organization and Systems
      • CSCI 2510: Principles of Computing Systems
      • CSCI 3100: Algorithms
      • CSCI 3300: Software Engineering
      • CSCI 4610: Concurrent and Parallel Programming
      • CSCI 4620: Distributed Computing
      • CSCI 4740: Artificial Intelligence
      • CSCI 4760: Deep Learning
      • CSCI 4830: Computer Vision
      • CSCI 4845: Natural Language Processing
      • STAT 4800: Probability Theory
      • STAT 4840: Time Series
      • STAT 4850: Mathematical Statistics
  • General Electives: 24-27 credits
• Total Credits: 120

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 science course counting toward their major in each academic year (until all requirements are completed).

Roadmap

The roadmap is a recommended semester-by-semester plan of study for programs and assumes full-time enrollment unless otherwise noted.

Program Notes

• STAT 3850 Foundation of Statistics (3 credits) and CSCI 2100 Data Structures (4 credits) are crucial to this program, as they serve as prerequisites for all of the upper division STAT and CSCI courses.
• Possible STAT electives include STAT 4840 Time Series (3 credits), MATH 4800 Probability Theory (3 credits), and STAT 4850 Mathematical Statistics (3 credits).
• Possible CSCI electives include CSCI 2300 Object-Oriented Software Design (3 credits), CSCI 3100 Algorithms (3 credits), CSCI 3300 Software Engineering (3 credits), CSCI 4610 Concurrent and Parallel Programming (3 credits), CSCI 4620 Distributed Computing (3 credits), CSCI 4740 Artificial Intelligence (3 credits), CSCI 4760 Deep Learning (3 credits), CSCI 4830 Computer Vision (3 credits), and CSCI 4845 Natural Language Processing (3 credits).
• 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.

Data Science, B.S. (SLU-Madrid)

Unlock the power of data to solve real-world problems and drive innovation with a Bachelor of Science in Data Science from Saint Louis University-Madrid.

Curriculum Overview (SLU-Madrid)

This data science program focuses on equipping you with the knowledge and skills to analyze, interpret, and extract meaningful insights from data. It integrates elements of computer science, mathematics, and statistics with the goal of addressing real-world problems through data-driven decision-making.

Fieldwork, Internships, and Careers (SLU-Madrid)

Class sizes are intentionally kept small at all levels to encourage active participation, foster meaningful discussions, and make it easier for professors to address individual learning needs. This focused approach enhances understanding, promotes collaboration, and ultimately leads to better educational outcomes. This environment and the teaching of award-winning full-time faculty and industry experts will place you in an unbeatable starting point to enter the job market.

Admission (SLU-Madrid)

SLU-Madrid Application

• Application Deadlines:
  • May 1 - Fall admission (Aug. 1 for EU students)
  • Sept. 1 - Spring admission (Dec. 1 for EU students)
  • March 1 - Summer sessions (for applicants who require a student visa)
  • April 15 - Summer sessions (for applicants who do not require a student visa)