Degree Requirements
Requirements Overview
- Complete Core Coursework
- Complete Data Analytics Summer Experience
- Select and Complete Data Analytics Domain
The detailed requirements for both BA and BS degrees are organized as follows:
Bachelor of Arts
The Bachelor of Arts in Data Analytics (DA) requires a minimum of 46 credits of coursework.
1) Students must complete the following 34 credits of core coursework:
Code | Title | |
---|---|---|
DA 101 | Introduction to Data Analytics | |
CS 109 | Discovering Computer Science | |
or CS 111 | Discovering Computer Science: Scientific Data and Dynamics | |
or CS 112 | Discovering Computer Science: Markets, Polls, and Social Networks | |
or CS 113 | Discovering Computer Science: Physical Computing | |
or CS 114 | Discovering Computer Science: Computing for the Social Good | |
MATH 135 | Single Variable Calculus | |
or MATH 145 | Multivariable Calculus | |
DA 200 | Data Analytics Colloquium (Sophomore, 1 credit) | |
DA 200 | Data Analytics Colloquium (Junior or Senior, 1 credit) | |
DA 210/CS 181 | Data Systems | |
DA/MATH 220 | Applied Statistics | |
DA 301 | Practicum in Data Analytics | |
DA 351 | Advanced Descriptive Methods in Data Analytics | |
or DA 352 | Advanced Predictive Methods in Data Analytics | |
or DA 353 | Advanced Prescriptive Methods in Data Analytics | |
DA 401 | Seminar in Data Analytics |
2) Students must complete a DA 030 (summer internship or research project). This experience must be approved by the Data Analytics Program Committee and is normally undertaken during the summer before the senior year.
3 ) Students must acquire some depth in a domain of Data Analytics. See 'Domains in Data Analytics' for more information.
Bachelor of Science
Students who wish to acquire deeper methodological skills in data analytics and/or better prepare for graduate study may pursue a Bachelor of Science in Data Analytics. Students looking for added methodological depth and foundation, or to further strengthen their graduate school readiness, may also wish to pursue a minor or second major in Computer Science, Mathematics, or Applied Mathematics, or in another related quantitative field.
1) Students must complete the following 40 credits of core coursework:
Code | Title | |
---|---|---|
DA 101 | Introduction to Data Analytics | |
CS 109 | Discovering Computer Science | |
or CS 111 | Discovering Computer Science: Scientific Data and Dynamics | |
or CS 112 | Discovering Computer Science: Markets, Polls, and Social Networks | |
or CS 113 | Discovering Computer Science: Physical Computing | |
or CS 114 | Discovering Computer Science: Computing for the Social Good | |
MATH 145 | Multivariable Calculus | |
MATH 213 | Linear Algebra and Differential Equations (recommendation: take by the end of Sophomore year.) | |
DA 200 | Data Analytics Colloquium (once as a sophomore and once as a junior or senior, 2 credits total) | |
DA 210/CS 181 | Data Systems | |
DA/MATH 220 | Applied Statistics | |
DA 301 | Practicum in Data Analytics | |
Complete two advanced methods in data analytics courses (DA 35X). | ||
DA 351 | Advanced Descriptive Methods in Data Analytics | |
or DA 352 | Advanced Predictive Methods in Data Analytics | |
or DA 353 | Advanced Prescriptive Methods in Data Analytics | |
DA 401 | Seminar in Data Analytics |
2) Students must complete an additional methods-based elective course approved by the Data Analytics program. To avoid double-counting elective courses, a course used to satisfy another degree program’s major or minor elective requirement cannot be counted as the methods elective for the Bachelor of Science in Data Analytics. Likewise, a course that is used to satisfy a student’s Data Analytics Domain cannot be counted as the methods elective for the Bachelor of Science in Data Analytics. A full list of currently offered electives that meet this requirement is maintained by the Data Analytics program. Examples include:
Code | Title |
---|---|
DA 271 | Theory and Practice of Data Visualization |
CS 271 | Data Structures |
CS 339 | Artificial Intelligence |
CS 337/MATH 415 | Operations Research |
CS 377 | Database Systems |
ECON 467 | Econometrics II |
MATH 420 | Statistical Modeling |
MATH 422 | Time Series Analysis |
MATH 435 | Mathematical Modeling |
3) Students must complete a DA 030 (summer internship or research project). This experience must be approved by the Data Analytics Program Committee and is normally undertaken during the summer before the senior year.
4) Students must acquire some depth in a domain of Data Analytics. See 'Domains in Data Analytics' for more information.
Domains in Data Analytics
Students pursuing BA or BS in Data Analytics must acquire some depth in a domain of Data Analytics. They will then carry this disciplinary knowledge into their summer experience and senior seminar. Students may satisfy this requirement in one of two ways.
- They may choose to take the designated set of courses from a specific department (see table below).
- They may submit an individualized 3-4 course domain elective plan, which must include at least one analytics-intensive course, to be considered for approval by the Data Analytics Program Committee. A successful one-page proposal will clearly describe the student’s desired learning goals and how the proposed courses together achieve these goals. The proposal should also demonstrate the feasibility of completing the proposed courses in the time remaining before graduation. Proposals must be submitted before the end of the sophomore year.
Code | Title | |
---|---|---|
Biology (4 courses) | ||
BIOL 210 | Molecular Biology and Unicellular Life | |
BIOL 220 | Multicellular Life | |
BIOL 230 | Ecology and Evolution | |
and one of the following: | ||
BIOL 345 | Eukaryotic Cell Biology (Dr. Yoo only) | |
BIOL 350 | Genomics | |
BIOL 356 | Special Topics (Biostatistics) | |
Economics (4 courses) | ||
ECON 101 | Introductory Macroeconomics | |
ECON 102 | Introductory Microeconomics | |
ECON 302 | Intermediate Microeconomic Analysis | |
ECON 307 | Introductory Econometrics | |
Earth and Environmental Sciences (4 courses) | ||
EESC 111 | Planet Earth | |
Either | ||
EESC 234 | Applied GIS for Earth and Environmental Sciences | |
Or | ||
EESC 222 & EESC 223 | Geographic Information Systems I and Geographic Information Systems II | |
And one of the following: | ||
EESC 200 | Environmental Geology | |
EESC 210 | Historical Geology | |
EESC 211 | Rocks, Minerals & Soils | |
And one of the following: | ||
EESC 300 | Geomorphology | |
EESC 310 | Global Biogeochemical Cycles | |
EESC 311 | Structural Geology | |
EESC 313 | Environmental Hydrology | |
EESC 314 | Sedimentology & Stratigraphy | |
EESC 333 | Stable Isotopes in the Environment | |
Sustainability & Environmental Studies (4 courses) | ||
SES 100 | Introduction to Sustainability and Environmental Studies | |
SES 200 | Environmental Analysis | |
And one of the following: | ||
SES 215 | Renewable Energy Systems | |
EESC 234 | Applied GIS for Earth and Environmental Sciences | |
SES 222 & SES 223 | Geographic Information Systems I and Geographic Information Systems II | |
SES 240 | Environmental Politics and Decision-Making | |
SES 274 | Ecosystem Management | |
And one of the following: | ||
SES 256 | Farmscape: Visual Immersion in the Food System | |
SES 262 | Environmental Dispute Resolution | |
SES 264 | Environmental Planning and Design | |
SES 334 | Sustainable Agriculture and Food Systems | |
Philosophy (3 courses) | ||
PHIL 121 | Ethics: Philosophical Considerations of Morality | |
or PHIL 126 | Social and Political Philosophy | |
PHIL 205 | Logic | |
PHIL 210 | Philosophy of Science | |
Physics (3 courses) | ||
Either: | ||
PHYS 121 & PHYS 122 | General Physics I and General Physics II | |
Or | ||
PHYS 125 & PHYS 126 & PHYS 127 | Physics I: Quarks to Cosmos and Physics II: Mechanics, Fluids, and Heat and Physics III: Electricity, Magnetism, Waves, and Optics | |
PHYS 312 | Experimental Physics | |
Psychology (3 courses) | ||
PSYC 100 | Introduction to Psychology | |
PSYC 200 | Research Methods and Statistics | |
PSYC 2XX/3XX | Psychology elective (except research courses, 370, 410, 361-364, 451-452) |
Additional Points of Interest
Data Analytics majors wishing to study abroad should do so in the spring semester of their junior year. Data Analytics courses are not normally taken at other institutions, although on rare occasions, a suitable substitute may be found for DA 351/2/3 - Advanced Methods for Data Analytics.
If a student uses AP credit to skip a course in their chosen domain area, that course must be replaced with a suitable substitute, determined in cooperation with the appropriate department.