The Bachelor of Science in Data Science is designed to meet the growing demand for data scientists or data analysts who can manage and analyze structured and unstructured data sets and extract meaningful knowledge to inform decisions. The curriculum consists of courses in computer science, mathematics and data management. Students learn about data processing and application development, machine learning and statistical modeling techniques, and the analytical and communication skills to explain results in a meaningful way. DePaul offers a Bachelor of Science degree through the School of Computing and a Bachelor of Arts degree in the College of Science and Health. The two programs share a common core of courses focusing on fundamental skills in data science that students take during the first two years. After the second year, the two degrees diverge in their emphasis and level of specialization.
| Program Requirements | Quarter Hours |
|---|---|
| Liberal Studies Requirements | 76 |
| Major Requirements | 100 |
| Open Electives | 16 |
| Total hours required | 192 |
Learning Outcomes
Students will be able to:
- Explain how data is represented for analytic applications.
- Select and apply techniques for data preparation including normalization and reduction.
- Perform exploratory analysis to gain preliminary understanding of data.
- Develop and evaluate predictive models.
- Perform an independent data science investigation, from data gathering and cleaning to application of data mining algorithms.
Liberal Studies Requirements
Honors program requirements can be found in the individual Colleges & Schools section of the University Catalog. Select the appropriate college or school, followed by Undergraduate Academics and scroll down.
| First Year Program | Hours | |
|---|---|---|
| Chicago Quarter | ||
| LSP 110 or LSP 111 | DISCOVER CHICAGO or EXPLORE CHICAGO | 4 |
| Focal Point | ||
| LSP 112 | FOCAL POINT SEMINAR | 4 |
| Writing | ||
| WRD 103 | COMPOSITION AND RHETORIC I 1 | 4 |
| WRD 104 | COMPOSITION AND RHETORIC II 1 | 4 |
| Quantitative Reasoning | ||
| Not Required | ||
| Sophomore Year | ||
| Race, Power, and Resistance | ||
| LSP 200 | SEMINAR ON RACE, POWER, AND RESISTANCE | 4 |
| Junior Year | ||
| Experiential Learning | ||
| Required | 4 | |
| Senior Year | ||
| Capstone | ||
| DSC 394 | DATA SCIENCE PROJECT 1 | 4 |
- 1
Students must earn a C- or better in this course.
Learning Domains
Arts and Literature (AL)
- 3 Courses Required
Historical Inquiry (HI)
- 2 Courses Required
Math and Computing (MC)
- Not Required
Philosophical Inquiry (PI)
- 2 Courses Required (see note below)
Religious Dimensions (RD)
- 2 Courses Required (see note below)
Scientific Inquiry (SI)
- 1 Lab Course Required
Social, Cultural, and Behavioral Inquiry (SCBI)
- 3 Courses Required
Note
Students must take one of the following ethics courses: CSC 208 (PI), PHL 248/MGT 248 (PI) or REL 228/MGT 228 (RD).
Specified required courses within Liberal Studies may have grade minimums (e.g. C- or better). Please consult your advisor or your college and major requirements.
Courses offered in the student's primary major cannot be taken to fulfill LSP Domain requirements. If students double major, LSP Domain courses may double count for both LSP credit and the second major. Students who choose to take an experiential learning course offered by the major may count it either as a general elective or the Experiential Learning requirement.
In meeting learning domain requirements, no more than one course that is outside the student’s major and is cross-listed with a course within the student’s major, can be applied to count for LSP domain credit. This policy does not apply to those who are pursuing a double major or earning BFA or BM degrees.
Major Requirements
Course Requirements
| Course | Title | Quarter Hours |
|---|---|---|
| CSC 241 | INTRODUCTION TO COMPUTER SCIENCE I | 4 |
| CSC 242 | INTRODUCTION TO COMPUTER SCIENCE II | 4 |
| CSC 300 | DATA STRUCTURES I | 4 |
| CSC 301 | DATA STRUCTURES II | 4 |
| CSC 321 | DESIGN AND ANALYSIS OF ALGORITHMS | 4 |
| DSC 323 | DATA ANALYSIS AND REGRESSION | 4 |
| DSC 324 | ADVANCED DATA ANALYSIS | 4 |
| CSC 355 | DATABASE SYSTEMS | 4 |
| DSC 341 | FOUNDATIONS OF DATA SCIENCE | 4 |
| DSC 333 | INTRODUCTION TO BIG DATA PROCESSING | 4 |
| DSC 345 | MACHINE LEARNING | 4 |
| DSC 365 | DATA VISUALIZATION | 4 |
| IT 223 | DATA ANALYSIS | 4 |
| MAT 140 | DISCRETE MATHEMATICS I | 4 |
| MAT 150 | CALCULUS I | 4 |
| MAT 151 | CALCULUS II | 4 |
| MAT 152 | CALCULUS III | 4 |
| MAT 220 | APPLIED LINEAR ALGEBRA | 4 |
| MAT 349 | APPLIED PROBABILITY | 4 |
| MAT 360 | GENERALIZED LINEAR MODELS | 4 |
| CMNS 201 | BUSINESS AND PROFESSIONAL COMMUNICATION | 4 |
| SE 350 | OBJECT-ORIENTED SOFTWARE DEVELOPMENT | 4 |
| Select eight (8) credit hours of Major Electives | 8 | |
Students may take CSC 243 and one (1) additional Major Elective in lieu of CSC 241 and CSC 242.
Major Electives
Students must earn a grade of C- or higher in all Major Elective courses. Students must select the eight (8) credit hours of Major Electives from the following list of courses, grouped by topic:
Computer Science
| Course | Title | Quarter Hours |
|---|---|---|
| SCIENTIFIC COMPUTING | ||
| CONCEPTS OF PROGRAMMING LANGUAGES | ||
| WEB APPLICATIONS | ||
| OPTIMIZED C++ | ||
| COMPUTER SYSTEMS I | ||
| COMPUTER SYSTEMS II | ||
| DISTRIBUTED SYSTEMS | ||
| INTRODUCTION TO SOFTWARE ENGINEERING |
Mathematics
| Course | Title | Quarter Hours |
|---|---|---|
| MULTIVARIABLE CALCULUS I | ||
| BAYESIAN STATISTICS | ||
| PROBABILITY AND STATISTICS I | ||
| PROBABILITY AND STATISTICS II | ||
| PROBABILITY AND STATISTICS III | ||
| STOCHASTIC PROCESSES | ||
| APPLIED TIME SERIES AND FORECASTING | ||
| OPERATIONS RESEARCH: LINEAR PROGRAMMING | ||
| OPERATIONS RESEARCH: OPTIMIZATION THEORY |
Artificial Intelligence
| Course | Title | Quarter Hours |
|---|---|---|
| SYMBOLIC PROGRAMMING | ||
| FOUNDATIONS OF ARTIFICIAL INTELLIGENCE |
Image Analytics
| Course | Title | Quarter Hours |
|---|---|---|
| INTRODUCTION TO DIGITAL IMAGE PROCESSING | ||
| APPLIED IMAGE ANALYSIS |
Geographic Information Systems
| Course | Title | Quarter Hours |
|---|---|---|
| GEOGRAPHIC INFORMATION SYSTEMS I: DIGITAL MAPPING | ||
| GEOGRAPHIC INFORMATION SYSTEMS II: COMMUNITY GIS | ||
| EARTH OBSERVATION | ||
| EARTH OBSERVATION II | ||
| GEOGRAPHIC INFORMATION SYSTEMS III: SPATIAL ANALYSIS FOR SUSTAINABILITY | ||
| GIS ANALYSIS OF ENVIRONMENTAL AND PUBLIC HEALTH | ||
| WEB GIS AND SPATIAL DATA VISUALIZATION ON THE WEB |
Information Technology
| Course | Title | Quarter Hours |
|---|---|---|
| INTRODUCTORY COMPUTING FOR THE WEB | ||
| WEB DEVELOPMENT I | ||
| WEB DEVELOPMENT II | ||
| INTRODUCTION TO MOBILE APPS |
Research
| Course | Title | Quarter Hours |
|---|---|---|
| RESEARCH COLLOQUIUM | ||
| INDEPENDENT STUDY | ||
| RESEARCH EXPERIENCE |
Open Electives
Open elective credit also is required to meet the minimum graduation requirement of 192 hours.
See www.cdm.depaul.edu to see sample schedule of course requirements on a year-by-year basis.
Degree Requirements
Students in this degree must meet the following requirements:
- Complete a minimum of 192 credit hours (generally 48 courses)
- Earn a grade of C- or higher in WRD 103, WRD 104, and all Major and Minor courses
- Earn a grade of D or higher in all other Liberal Studies and Open Elective courses
- Maintain a cumulative GPA of 2.0 or higher
Program Combination Restrictions
Students pursuing the BS in Data Science are forbidden from pursuing the BA in Data Science through the College of Science and Health. Students pursuing the BS in Data Science are also forbidden from pursuing the Minor in Data Science.