Professional Applications of Data Science Graduate Academic Certificate
All required coursework must be completed with a grade of B or better (O-10-b).
| Code | Title | Hours |
|---|---|---|
| Introduction to Data Science - Choose one of the following: | 3 | |
| Data Management for Big Data | ||
| Data Science | ||
| Introduction to Applied Data Science | ||
| Reproducible Data Science | ||
| Data Visualization - Choose one of the following: | 3 | |
| Foundations of Data Visualization | ||
| Data Visualization for Managerial Decision Making | ||
| Applied Data Science with Python | ||
| Communicating with Data - Choose 3 credits total from the following: | 3 | |
| Communicating with Data | ||
| Data Science Portfolio | ||
| Communication for Science Professionals | ||
| Electives (Choose one of the following) | 3 | |
| Practical Methods in Analyzying Animal Science Experiments | ||
| Foundations of Data Visualization | ||
| Image Processing and Computer Vision | ||
| Instrumentation and Controls | ||
| Data Visualization for Managerial Decision Making | ||
| Data Management for Big Data | ||
| Systems Biology | ||
| Phylogenetics | ||
| Computer Skills for Biologists | ||
| Mathematical Genetics | ||
| Aquatic Habitat Modeling | ||
| Parallel Programming | ||
| Digital Forensics | ||
| Computational Biology: Sequence Analysis | ||
| Data Science | ||
| Applied Data Science with Python | ||
| Semantic Web and Open Data | ||
| Artificial Intelligence | ||
| Machine Learning | ||
| Deep Learning | ||
| Evolutionary Computation | ||
| Python for Machine Learning | ||
| Neural Network Design | ||
| Introduction to Quantitative Research Methods | ||
| Univariate Quantitative Research in Education | ||
| Advanced Quantitative Research Methods | ||
| Theoretical Applications and Designs of Qualitative Research | ||
| Advanced Qualitative Research Methods | ||
| Indigenous and Decolonizing Research Methods | ||
| Decolonizing, Indigenous, and Action-Based Research Methods | ||
| Survey Design for Social Science Research | ||
| Methods of Educational Research | ||
| Data Wizardry in Environmental Sciences | ||
| Research Methods in the Environmental Social Sciences | ||
| Remote Sensing of Fire | ||
| Spatial Analysis and Modeling | ||
| Remote Sensing/GIS Image Analysis | ||
| Introduction to Applied Data Science | ||
| Foundations of Machine Learning | ||
| Stochastic Models | ||
| LIDAR and Optical Remote Sensing Analysis | ||
| Research Methods for Local Government and Community Administration | ||
| Landscape and Habitat Dynamics | ||
| Statistical Analysis | ||
| Nonparametric Statistics | ||
| Applied Regression Modeling | ||
| Statistical Learning and Predictive Modeling | ||
| Multivariate Analysis | ||
| Introduction to Bayesian Statistics | ||
| Statistical Ecology | ||
| Computer Intensive Statistics | ||
| Ecological Modeling | ||
| Reproducible Data Science | ||
| Statistical Ecology | ||
| Water Economics and Policy Analysis | ||
| Total Hours | 12 | |
Courses to total 12 credits for this certificate
Please see the Graduate Student Handbook for details and program requirements on earning this certificate.
Student Learning Outcomes
Upon completion of the certificate, students will be able to:
- Use open-source software to reproducibly manage, analyze, and visualize large, complex, and noisy data sets.
- Practice high quality and ethical data stewardship.
- Understand and execute data exploration.
- Effectively communicate data driven insights to experts and non-experts.
- Demonstrate their skills with an online portfolio of analyses and visualizations relevant to their field of specialization.