Data Science (Minor)
Bowers College of Computing and Information Science
Program Description
Offered university-wide, the minor in Data Science equips students from any major with a solid understanding of the conceptual and methodological tools of data-driven discovery. Upon completing the minor, students will be ready to leverage competencies and skills to pursue careers in various fields and professions.
Academic Standards
Grade Requirements
All qualifying courses must be taken at Cornell for a letter grade. Grades of S/U or SX/UX grades will not be accepted.
Each course must be completed with a grade of C or better to count toward the minor.
Minor Declaration Information
Complete the Data Science Minor application once you are enrolled in or have completed the final courses that you need for the minor.
Questions about the minor should be directed to: Julia Aquadro, Assistant Director of Undergraduate Advising.
Submission Deadlines
If graduating in May or August, the form is due by May 31. If graduating in December, the form is due by December 31. Late submissions will not be accepted.
Program Information
- Minimum Credits for Minor: 18
Minor Requirements
The requirements to complete the minor balance the specific learning outcomes with flexibility and choice, with courses distributed across the participating colleges and disciplines, to ensure students can pragmatically complete the minor along with concurrently meeting major and distribution/elective requirements.
To Complete the Minor
- Six courses are required in total
- One course from the core statistics category. These are restricted to those courses for which a calculus-based understanding of probability can provide understanding in concepts such as maximum likelihood estimation.
- One course from the core computer programming category. This course might be either an introductory programming course or one of a select number of more advanced courses.
- Four courses from the courses listed under the following categories:
- Data Analysis
- Domain Expertise
- Big Data Ethics, Policy and Society
- Data Communication
- For the four categories, at least one course should be from each of three different categories, while the fourth can come from any category.
Important Information about Major/Minor Overlaps
Students may count a maximum of two courses toward both the Data Science minor and their own major’s core requirements, though they may count other courses they take for the minor toward their major’s elective requirements, provided their department approves.
1) CS Majors:
- You may count a maximum of two courses toward both the Data Science Minor and your CS core courses, CS electives, and/or CS practicum requirements for the CS major.
- You may, however, count other courses you take for the Data Science Minor toward your CS technical electives, external specialization, major-approved and/or advisor-approved elective coursework, but only if those courses meet the requirements for that category of elective.
2) Students who are majoring in a subject that requires an introductory programming course (such as CS 1110 ), must satisfy the “Core Computing” requirement with a more advanced programming course.
Given the overlap of INFO courses in the Data Science Minor and the Data Science Concentration in the Information Science Minor, students cannot declare both.
Required Courses
Core Statistics
| Code | Title | Hours |
|---|---|---|
| Core Statistics | ||
| Select one of the following: | ||
| ECE 3100 | Introduction to Probability and Inference for Random Signals and Systems | 4 |
| ENGRD 2700 | Eng Probability and Statistics: Modeling and Data Science | 4 |
| MATH 4710 | Basic Probability | 4 |
| MATH 4720 | Theory of Statistics | 4 |
| ORIE 3500 | Eng Probability and Statistics: Modeling and Data Science II | 4 |
| STSCI 3080 | Probability Models and Inference | 4 |
| STSCI 3110 | Applied Probability and Statistics | 4 |
| or ECON 3110 | Applied Probability and Statistics | |
| ECON 3130 | Probability and Statistics | 4 |
| STSCI 2200 | Statistics I | 4 |
Core Computing
| Code | Title | Hours |
|---|---|---|
| Core Computing | ||
| Select one of the following: | ||
| AEM 2820 | Introduction to Database Management Systems | 3 |
| AEM 2830 | VBA for Data Analysis and Business Modeling | 3 |
| AEM 2840 | Python Programming for Data Analysis and Business Modeling | 3 |
| AEM 2850 | R Programming for Business Analytics and Data Visualization | 3 |
| CS 1110 | Introduction to Computing: A Design and Development Perspective | 4 |
| CS 1112 | Introduction to Computing: An Engineering and Science Perspective | 4 |
| CS 3220 | 3 | |
| CS 4320 | Introduction to Database Systems | 3 |
| HADM 3710 | Python Programming | 3 |
| HADM 3740 | Fundamentals of Database Management and Data Analysis | 3 |
| ORIE 3120 | Practical Tools for Operations Research, Machine Learning and Data Science | 4 |
| STSCI 4060 | Python Programming and its Applications in Statistics | 4 |
| STSCI 4520 | Statistical Computing | 4 |
Data Analysis
| Code | Title | Hours |
|---|---|---|
| AEM 3275 | Introduction to Machine Learning in Business | 3 |
| ASTRO 3334 | Data Analysis and Research Techniques in Astronomy | 3 |
| ASTRO 3340 | Symbolic and Numerical Computing | 4 |
| ASTRO 4523 | Modeling, Mining and Machine Learning in Astronomy | 3 |
| BEE 4310 | Environmental Statistics and Learning | 4 |
| CHEME 4660 | Financial Data Markets and Mayhem for Scientists and Engineers | 3 |
| or CHEME 5660 | Financial Data, Markets, and Mayhem for Scientists and Engineers | |
| CS 3780 | Introduction to Machine Learning | 4 |
| CS 4787 | Principles of Large-Scale Machine Learning Systems | 4 |
| CS 4850 | Probability, Vectors, and Matrices in Computing | 4 |
| ECE 3200 | Foundations Machine Learning | 4 |
| ECE 4110 | Random Signals in Communications and Signal Processing | 4 |
| ECE 4200 | Fundamentals of Machine Learning | 4 |
| ECE 4250 | Digital Signal Processing and Statistical Inference | 4 |
| ECON 3120 | Applied Econometrics | 4 |
| ECON 4140 | Methods and Computation in Program Evaluation | 3 |
| ENGRD 2720 | Data Science for Engineers | 4 |
| HADM 3275 | Introduction to Machine Learning in Business | 3 |
| HADM 4750 | Machine Learning for Business and Hospitality Applications | 1.5 |
| HD 2930 | Introduction to Data Science for Social Scientists | 3 |
| HD 2940 | Data Science for Social Scientists II | 3 |
| INFO 2950 | Introduction to Data Science | 4 |
| INFO 2951 | Introduction to Data Science with R | 4 |
| INFO 3300 | Visual Data Analytics for the Web | 3 |
| INFO 3370 | Studying Social Inequality Using Data Science | 3 |
| INFO 3950 | Advanced Data Analytics | 3 |
| MATH 2310 | Linear Algebra for Data Science (if taken before FA24, not accepted) | 4 |
| ORIE 3741 | Learning with Big Messy Data | 4 |
| ORIE 4580 | Simulation Modeling and Analysis | 4 |
| ORIE 4740 | Statistical Data Mining I | 4 |
| ORIE 4820 | Data-Driven Decision Modeling and Analysis | 3 |
| PUBPOL 3100 | Multiple Regression Analysis | 4 |
| STSCI 4060 | Python Programming and its Applications in Statistics | 4 |
| STSCI 3200 | Statistics II | 4 |
| STSCI 4100 | Multivariate Analysis | 4 |
| STSCI 3740 | Data Mining and Machine Learning | 4 |
| STSCI 4060 | Python Programming and its Applications in Statistics | 4 |
| STSCI 4100 | Multivariate Analysis | 4 |
| STSCI 4110 | Categorical Data | 3 |
| STSCI 4780 | Bayesian Data Analysis: Principles and Practice | 4 |
Big Data Ethics, Policy & Society
| Code | Title | Hours |
|---|---|---|
| ALS 1210 | Data Democratization | 3 |
| COMM 4300 | Ethics in New Media, Technology, and Communication | 3 |
| ENGL 3778 | Free Speech, Censorship, and the Age of Global Media | 4 |
| ENGRG 3605 | Ethics of Computing and Artificial Intelligence Technologies | 3 |
| GOVT 3999 | How Do You Know That? | 4 |
| INFO 1200 | Information Ethics, Law, and Policy | 3 |
| INFO 1260 | Choices and Consequences in Computing | 3 |
| or CS 1340 | Choices and Consequences in Computing | |
| INFO 3200 | Technology, Behavior and Society | 3 |
| or COMM 3200 | Technology, Behavior and Society | |
| INFO 3370 | Studying Social Inequality Using Data Science | 3 |
| INFO 4145 | Privacy and Security in the Data Economy | 3 |
| INFO 4240 | Designing Technology for Social Impact | 4 |
| INFO 4250 | Surveillance and Privacy | 3 |
| INFO 4505 | Computing and Global Development | 3 |
| INFO 4561 | Evaluation and Society | 3 |
| NBA 4920 | AI for Business Applications | 1.5-3 |
| PUBPOL 2070 | Big Data for Big Policy Problems | 4 |
| PUBPOL 2130 | Data and the State: How Governments See People and Places | 4 |
| PUBPOL 3520 | Economic and Policy Implications of Artificial Intelligence | 3 |
| PUBPOL 3725 | Ethics in Data, Data Science, and AI for Public Policy | 3 |
| STSCI 3600 | Integrated Ethics in Data Science | 2 |
| STSCI 4850 | Data Science Consulting | 2 |
Data Communication
| Code | Title | Hours |
|---|---|---|
| COGST 3420 | Human Perception: Application to Computer Graphics, Art, and Visual Display | 3 |
| COMM 2450 | Communication and Technology | 3 |
| COMM 3010 | Writing and Producing the Narrative for Digital Media | 3 |
| COMM 3150 | Organizational Communication: Theory and Practice | 3 |
| COMM 4360 | Communication Networks and Social Capital | 3 |
| COMM 4860 | Risk Communication | 3 |
| INFO 3312 | Data Communication | 3 |
| INFO 3950 | Advanced Data Analytics | 3 |
| INFO 4310 | Interactive Information Visualization | 3 |
| SOC 3580 | Big Data on the Social World | 3 |
Domain Expertise
| Code | Title | Hours |
|---|---|---|
| AEM 2770 | Excursions in Computational Sustainability | 3 |
| AEM 3100 | Business Statistics | 3 |
| AEM 4060 | Risk Simulation and Monte Carlo Methods | 3 |
| AEM 4110 | Introduction to Econometrics | 3 |
| AEM 4225 | Systems and Analytics in Accounting | 3 |
| AEM 4435 | Data Driven Marketing | 1.5 |
| AEM 4620 | Digital Innovation in Media Markets & Creative Industries | 3 |
| AEM 4660 | Business Simulation | 1.5 |
| ASTRO 3310 | Planetary Image Processing with MATLAB | 3 |
| BEE 4850 | Environmental Data Analysis and Simulation | 3 |
| BIOCB 3620 | Dynamic Models and Data in Biology | 4 |
| BIOCB 4381 | Biomedical Data Mining and Modeling | 3 |
| BIOCB 4830 | Quantitative Genomics and Genetics | 4 |
| BIOCB 4840 | Computational Genetics and Genomics | 4 |
| BIOEE 3550 | Data Analysis and Visualization in Ecology and Environmental Science | 3 |
| BIOEE 3611 | Field Ecology | 3 |
| BIOEE 4940 | Special Topics in Ecology and Evolutionary Biology | 1-6 |
| BIOMG 4810 | Principles of Population Genetics | 4 |
| BIOMG 4870 | Human Genomics | 3 |
| BIONB 3300 | Introduction to Computational Neuroscience | 4 |
| BIONB 4220 | Modeling Behavioral Evolution | 4 |
| BIONB 4380 | Computational Methods for Neurobiology & Behavior | 3 |
| BME 4790 | Modern Applications of Machine Learning and Artificial Intelligence for Biomedical Applications | 3 |
| CHEME 4660 | Financial Data Markets and Mayhem for Scientists and Engineers | 3 |
| or CHEME 5660 | Financial Data, Markets, and Mayhem for Scientists and Engineers | |
| CHEM 4810 | Computational Methods in Chemistry | 3 |
| COGST 3140 | Computational Psychology | 3 |
| CRP 4080 | Introduction to Geographic Information Systems (GIS) | 4 |
| CS 4300 | Language and Information | 3 |
| or INFO 4300 | Language and Information | |
| CS 4740 | Natural Language Processing | 4 |
| EAS 3450 | Environmental Geophysics | 3 |
| EAS 5555 | Theory and Practice of Earth System Modeling | 3 |
| ECON 3120 | Applied Econometrics | 4 |
| ECON 3140 | Econometrics | 4 |
| ECON 3720 | The Economics of Health Care Markets | 3 |
| ECON 3860 | Resource Economics | 3 |
| ECON 4660 | Behavioral Economics | 4 |
| ENTOM 3030 | Applied Statistics: Biological Experiments in Practice | 4 |
| GOVT 3282 | Data Science Applications in Political and Social Research | 4 |
| HADM 4050 | Revenue Management | 3 |
| HADM 4770 | Advanced Business Modeling | 1.5 |
| ILRHR 4664 | Talent Analytics | 3 |
| ILRGL 3330 | Research Methods for Labor Policy | 3 |
| ILROB 4710 | Social Science Research Methods | 3 |
| INFO 3140 | Computational Psychology | 3 |
| INFO 3350 | Text Mining History and Literature | 3 |
| INFO 4100 | Learning Analytics | 3 |
| INFO 4555 | Business Intelligence Systems | 4 |
| NS 4300 | Proteins, Transcripts, and Metabolism: Big Data in Molecular Nutrition | 3 |
| NTRES 3100 | Applied Population Ecology | 3 |
| NTRES 4100 | Advanced Conservation Biology | 4 |
| NTRES 4120 | Wildlife Population Analysis: Techniques and Models | 3 |
| ORIE 4120 | Inventory, Operations, and Supply Chain Management: Models and Optimization | 3 |
| ORIE 4126 | Principles of Supply Chain Management | 4 |
| ORIE 4154 | Revenue Optimization and Marketplace Design | 3 |
| ORIE 4630 | Operations Research Tools for Financial Engineering | 4 |
| ORIE 4656 | Extreme Values in Finance | 3 |
| ORIE 4742 | Info Theory, Probabilistic Modeling, and Deep Learning with Scientific and Financial Apps | 3 |
| PLSCI 4000 | Concepts and Techniques in Computational Biology | 4 |
| PLSCI 4200 | Geographic Information Systems (GIS): Concepts and Application | 3 |
| PLSCI 4290 | Remote Sensing and Modeling for Ecosystems | 3 |
| PUBPOL 2155 | Data Management and Programming for Policy and Society | 3 |
| PUBPOL 3120 | Research Design and Methods of Social Research | 3 |
| PUBPOL 3130 | Behavioral Economics and Public Policy | 3 |
| or ECON 3670 | Behavioral Economics and Public Policy | |
| PUBPOL 3280 | Fundamentals of Population Health | 3 |
| or GDEV 3280 | Fundamentals of Population Health | |
| PUBPOL 3400 | The Economics of Consumer Policy | 3 |
| or ECON 3610 | The Economics of Consumer Policy | |
| PUBPOL 3550 | Economics of Education | 3 |
| PUBPOL 3600 | Economics of Crime | 3 |
| PUBPOL 3670 | Economics and Environmental Policy | 3 |
| or ECON 3850 | Economics and Environmental Policy | |
| PUBPOL 3780 | Global Comparative Health Care Systems | 3 |
| PUBPOL 4080 | Demographic Techniques | 3 |
| PUBPOL 4101 | Causal Inference and Data Analysis for Public Policy | 3 |
| PUBPOL 4110 | Pollution, Climate Change, and Health | 3 |
| STS 4040 | Digital Due Process Clinic | 3 |
Learning Outcomes
- Build core, foundational skills and knowledge in statistical techniques and computer programming.
- Implement these skills in disciplinary settings that align with their chosen areas of study.
- Conduct data analysis and interpretation.
- Effectively synthesize, present and communicate their results.
- Understand the broader contexts and impact of big and complex data in scholarship and in our society.