Data Science Bachelor of Science
Leading to a Bachelor of Science Degree in Data Science
The Bachelor of Science in Data Science program provides students with a foundation in the fields of statistics and computer science in addition to data-insightful topics such as machine learning, data mining, and data visualization. The curriculum provides hands-on training in data science software for the data analysis lifecycle, including data acquisition, data management, data analysis, model development and deployment, and visualization. Throughout the curriculum, students are made aware of the ethical implications of the elements of the data analysis lifecycle. Students acquire the skillset to be competitive in the job market and apply these skills directly in the work environment through two required co-op work semesters.
Program Educational Objectives
- Apply Mathematical concepts to model real-world problems, implement solutions, and validate outcomes
- Effectively use data science software for data analysis lifecycle: data acquisition, data management, data preparation and integration, data analysis, model development and deployment, and visualization
- Demonstrate competencies in statistical analysis and fundamental computational concepts
- Communicate data findings effectively to an audience, in oral, visual, and/or written formats
- Be aware of the ethical consequences of data-informed decision-making
Student Outcomes
- Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.
Four Year Program
Total Required Credits: 120
This is a four-year full-time program, which begins the fall semester of the student's first year and is planned to end after the summer semester of the student's fourth year.
| First Year | ||
|---|---|---|
| Fall Semester | Credits | |
| COMP1000 | COMPUTER SCIENCE I | 4 |
| MATH1776 | CALCULUS 1A | 2 |
| MATH1777 | CALCULUS 1B | 2 |
| MATH2300 | DISCRETE MATHEMATICS | 4 |
| English Sequence | 4 | |
| Credits | 16 | |
| Spring Semester | ||
| COMP1050 | COMPUTER SCIENCE II | 4 |
| MATH1550 | FOUNDATIONS OF APPLIED MATHEMATICS | 4 |
| MATH1876 | CALCULUS 2A | 2 |
| MATH1877 | CALCULUS 2B | 2 |
| English Sequence | 4 | |
| Credits | 16 | |
| Second Year | ||
| Fall Semester | ||
| COMP2000 | DATA STRUCTURES | 4 |
| MATH2100 | PROBABILITY & STATISTICS FOR ENGINEERS | 4 |
| MATH2860 | LINEAR ALGEBRA & MATRIX THEORY | 4 |
| HSS Elective* | 4 | |
| Credits | 16 | |
| Spring Semester | ||
| COMP2350 | ALGORITHMS | 4 |
| COMP3125 | DATA SCIENCE FUNDAMENTALS | 4 |
| MATH2200 | ADVANCED STATISTICS | 4 |
| GENERAL Elective | 4 | |
| COOP2500 | INTRODUCTION TO COOPERATIVE EDUCATION | 0 |
| Credits | 16 | |
| Summer Semester | ||
| OPTIONAL COOP EDUCATION | ||
| Credits | 0 | |
| Third Year | ||
| Fall Semester | ||
| DATA3010 | DATA MINING | 4 |
| COMP2650 | DATABASES | 4 |
| COMM4335 | SEE IT AND SAY IT WITH DATA VIZ | 4 |
| Science Elective | 4 | |
| Credits | 16 | |
| Spring Semester | ||
| COOP3500 | COOP EDUCATION 1 | |
| Credits | 0 | |
| Summer Semester | ||
| MATH2025 | MULTIVARIABLE CALCULUS | 4 |
| Data Science Elective 1 | 4 | |
| HSS Elective* | 4 | |
| Science Elective | 4 | |
| Credits | 16 | |
| Fourth Year | ||
| Fall Semester | ||
| COMP/MATH4050 | MACHINE LEARNING | 4 |
| Data Science Elective 1 | 4 | |
| HSS Elective* | 4 | |
| Credits | 12 | |
| Spring Semester | ||
| COOP4500 | COOP EDUCATION 2 | |
| Credits | 0 | |
| Summer Semester | ||
| DATA5500 | SENIOR DESIGN | 4 |
| Data Science Elective 1 | 4 | |
| Data Science Elective 1 | 4 | |
| Credits | 12 | |
| Total Credits | 120 | |
ENGL/HSS Note
Students are required to complete:
- At least one course in Humanities: CSAS, HSSI, HIST, HUMN, LITR and PHIL
- At least one course in the Social Sciences: CSAS, HSSI, COMM, ECON, ENVM, POLS, PSYC and SOCL
- The remaining course from either the Humanities or Social Sciences category.
Students with a three English course sequence may use the third English course to satisfy a Humanities requirement.
A minimum of 20 credits total, including English, humanities, and social science credit, is required to complete the humanities and social sciences graduation requirement.
At least one of the HSS electives must be an Ethics course.
Math Placement may alter the course schedule above.
Data Science Electives
A total of 16 semester credit hours of technical electives must be taken as a part of the program. Students may choose, after consultation with their primary advisor, among the electives offered each semester. Technical elective courses include the following courses. The school may add to the list of available electives on a semester by semester basis.
| Course | Title | Credits |
|---|---|---|
| MATH1950 | FINANCIAL MATHEMATICS | 4 |
| MATH2250 | TIME SERIES | 4 |
| MATH2425 | CRYPTOLOGY | 4 |
| MATH3150 | STOCHASTIC PROCESSES | 4 |
| MATH3200 | DIFFERENTIAL GEOMETRY | 4 |
| MATH3225 | FUNCTIONAL ANALYSIS | 4 |
| MATH3250 | HAZARD & CATASTROPHE MODELING | 4 |
| MATH3500 | CALCULUS IV | 4 |
| MATH4050 | MACHINE LEARNING | 4 |
| MATH4100 | INDUSTRIAL PROBLEMS IN APPLIED MATHEMATICS | 4 |
| MATH4400 | INTRODUCTION TO ABSTRACT ALGEBRA | 4 |
| MATH4475 | ACTUARIAL MATHEMATICS | 4 |
| MATH4575 | COMPLEX VARIABLES | 4 |
| MATH4875 | REAL ANALYSIS I | 4 |
| MATH4975 | REAL ANALYSIS II | 4 |
| MGMT2750 | INTEGRATIVE FINANCIAL ACCOUNTING (requires School approval ) | 4 |
| COMP4750 | EMBEDDED ARTIFICIAL INTELLIGENCE | 4 |
| COMP4725 | BIG DATA PROGRAMMING | 4 |
| COMP3725 | SOCIAL NETWORK ANALYSIS | 4 |
| Any 2000-Level courses in BIOE,BIOL, BMED,CHEM,CIVE,COMP,ELEC,ELMC,ENGR,ENVM,MECH,PHYS,SCIN will also count as Technical Electives* Prerequisites must be met and School approval is required. | ||
| Any DATA course | ||
Science Electives
| Course | Title | Credits |
|---|---|---|
| BIOL1100 | CELL & MOLECULAR BIOLOGY | 4 |
| BIOL1700 | ANATOMY & PHYSIOLOGY I | 4 |
| BIOL2200 | ADVANCED MOLECULAR BIOLOGY | 4 |
| BIOL3000 | APPLICATIONS IN GENETICS | 4 |
| CHEM1100 | GENERAL CHEMISTRY I | 4 |
| CHEM1600 | GENERAL CHEMISTRY II | 4 |
| PHYS1250 | ENGINEERING PHYSICS I | 4 |
| PHYS1750 | ENGINEERING PHYSICS II | 4 |
| PHYS2000 | INTRODUCTION TO ASTRONOMY | 4 |
| PHYS3100 | MODERN PHYSICS | 4 |
| The following courses require School approval to satisfy the Science Elective requirement | ||
| BIOL2990 | INDEPENDENT STUDY IN BIOLOGY | 4 |
| BIOL3800 | SPECIAL TOPICS IN BIOLOGY | 4 |
| CHEM2990 | INDEPENDENT STUDY IN CHEMISTRY | 4 |
| CHEM3800 | SPECIAL TOPICS IN CHEMISTRY | 4 |
| PHYS2990 | INDEPENDENT STUDY IN PHYSICS | 4 |
| PHYS3800 | SPECIAL TOPICS IN PHYSICS | 4 |
Three Year Program
Total Required Credits: 120
This is a three-year full-time accelerated program, which begins the fall semester of the student's first year and is planned to end after the summer semester of the student's third year.
| First Year | ||
|---|---|---|
| Fall Semester | Credits | |
| COMP1000 | COMPUTER SCIENCE I | 4 |
| MATH1776 | CALCULUS 1A | 2 |
| MATH1777 | CALCULUS 1B | 2 |
| MATH2300 | DISCRETE MATHEMATICS | 4 |
| English Sequence | 4 | |
| Science Elective | 4 | |
| Credits | 20 | |
| Spring Semester | ||
| COMP1050 | COMPUTER SCIENCE II | 4 |
| MATH1550 | FOUNDATIONS OF APPLIED MATHEMATICS | 4 |
| MATH1876 | CALCULUS 2A | 2 |
| MATH1877 | CALCULUS 2B | 2 |
| English Sequence | 4 | |
| Science Elective | 4 | |
| Credits | 20 | |
| Second Year | ||
| Fall Semester | ||
| COMP2000 | DATA STRUCTURES | 4 |
| COMP2650 | DATABASES | 4 |
| MATH2100 | PROBABILITY & STATISTICS FOR ENGINEERS | 4 |
| COMM4335 | SEE IT AND SAY IT WITH DATA VIZ | 4 |
| HSS Elective* | 4 | |
| COOP2500 | INTRODUCTION TO COOPERATIVE EDUCATION | 0 |
| Credits | 20 | |
| Spring Semester | ||
| COMP2350 | ALGORITHMS | 4 |
| COMP3125 | DATA SCIENCE FUNDAMENTALS | 4 |
| MATH2200 | ADVANCED STATISTICS | 4 |
| MATH2025 | MULTIVARIABLE CALCULUS | 4 |
| MATH2860 | LINEAR ALGEBRA & MATRIX THEORY | 4 |
| Credits | 20 | |
| Summer Semester | ||
| COOP3500 | COOP EDUCATION 1 | |
| Credits | 0 | |
| Third Year | ||
| Fall Semester | ||
| COMP/MATH4050 | MACHINE LEARNING | 4 |
| DATA3010 | DATA MINING | 4 |
| Data Science Elective 1 | 4 | |
| Data Science Elective 1 | 4 | |
| HSS Elective* | 4 | |
| Credits | 20 | |
| Spring Semester | ||
| COOP4500 | COOP EDUCATION 2 | 0 |
| Credits | 0 | |
| Summer Semester | ||
| DATA5500 | SENIOR DESIGN | 4 |
| Data Science Elective 1 | 4 | |
| Data Science Elective 1 | 4 | |
| HSS Elective* | 4 | |
| General Elective 2 | 4 | |
| Credits | 20 | |
| Total Credits | 120 | |
ENGL/HSS Note
Students are required to complete:
- At least one course in Humanities: CSAS, HSSI, HIST, HUMN, LITR and PHIL
- At least one course in the Social Sciences: CSAS, HSSI, COMM, ECON, ENVM, POLS, PSYC and SOCL
- The remaining course from either the Humanities or Social Sciences category.
Students with a three English course sequence may use the third English course to satisfy a Humanities requirement.
A minimum of 20 credits total, including English, humanities, and social science credit, is required to complete the humanities and social sciences graduation requirement.
At least one of the HSS electives must be an Ethics course.
Math Placement may alter the course schedule above.
Data Science Electives
A total of 16 semester credit hours of technical electives must be taken as a part of the program. Students may choose, after consultation with their primary advisor, among the electives offered each semester. Technical elective courses include the following courses. The school may add to the list of available electives on a semester by semester basis.
| Course | Title | Credits |
|---|---|---|
| MATH1950 | FINANCIAL MATHEMATICS | 4 |
| MATH2250 | TIME SERIES | 4 |
| MATH2425 | CRYPTOLOGY | 4 |
| MATH3150 | STOCHASTIC PROCESSES | 4 |
| MATH3200 | DIFFERENTIAL GEOMETRY | 4 |
| MATH3225 | FUNCTIONAL ANALYSIS | 4 |
| MATH3250 | HAZARD & CATASTROPHE MODELING | 4 |
| MATH3500 | CALCULUS IV | 4 |
| MATH4050 | MACHINE LEARNING | 4 |
| MATH4100 | INDUSTRIAL PROBLEMS IN APPLIED MATHEMATICS | 4 |
| MATH4400 | INTRODUCTION TO ABSTRACT ALGEBRA | 4 |
| MATH4475 | ACTUARIAL MATHEMATICS | 4 |
| MATH4575 | COMPLEX VARIABLES | 4 |
| MATH4875 | REAL ANALYSIS I | 4 |
| MATH4975 | REAL ANALYSIS II | 4 |
| MGMT2750 | INTEGRATIVE FINANCIAL ACCOUNTING (requires School approval ) | 4 |
| COMP4750 | EMBEDDED ARTIFICIAL INTELLIGENCE | 4 |
| COMP4725 | BIG DATA PROGRAMMING | 4 |
| COMP3725 | SOCIAL NETWORK ANALYSIS | 4 |
| Any 2000-Level courses in BIOE,BIOL, BMED,CHEM,CIVE,COMP,ELEC,ELMC,ENGR,ENVM,MECH,PHYS,SCIN will also count as Technical Electives* Prerequisites must be met and School approval is required. | ||
| Any DATA course | ||
Science Electives
| Course | Title | Credits |
|---|---|---|
| BIOL1100 | CELL & MOLECULAR BIOLOGY | 4 |
| BIOL1700 | ANATOMY & PHYSIOLOGY I | 4 |
| BIOL2200 | ADVANCED MOLECULAR BIOLOGY | 4 |
| BIOL3000 | APPLICATIONS IN GENETICS | 4 |
| CHEM1100 | GENERAL CHEMISTRY I | 4 |
| CHEM1600 | GENERAL CHEMISTRY II | 4 |
| PHYS1250 | ENGINEERING PHYSICS I | 4 |
| PHYS1750 | ENGINEERING PHYSICS II | 4 |
| PHYS2000 | INTRODUCTION TO ASTRONOMY | 4 |
| PHYS3100 | MODERN PHYSICS | 4 |
| The following courses require School approval to satisfy the Science Elective requirement | ||
| BIOL2990 | INDEPENDENT STUDY IN BIOLOGY | 4 |
| BIOL3800 | SPECIAL TOPICS IN BIOLOGY | 4 |
| CHEM2990 | INDEPENDENT STUDY IN CHEMISTRY | 4 |
| CHEM3800 | SPECIAL TOPICS IN CHEMISTRY | 4 |
| PHYS2990 | INDEPENDENT STUDY IN PHYSICS | 4 |
| PHYS3800 | SPECIAL TOPICS IN PHYSICS | 4 |