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 |
|---|---|---|
| INTR 509 | Introduction to Applied Data Science | 3 |
| BCB 521 | Communicating with Data | 2 |
| BCB 520 | Foundations of Data Visualization | 3 |
| BCB 522 | Data Science Portfolio | 1 |
| Electives (Choose 1 of the following)* | 3 | |
| Practical Methods in Analyzying Animal Science Experiments | ||
| Image Processing and Computer Vision | ||
| Systems Biology | ||
| Phylogenetics | ||
| Instrumentation and Measurements | ||
| Computer Skills for Biologists | ||
| Mathematical Genetics | ||
| Aquatic Habitat Modeling | ||
| Parallel Programming | ||
| Computational Biology: Sequence Analysis | ||
| Digital Forensics | ||
| Artificial Intelligence | ||
| Deep Learning | ||
| Machine Learning | ||
| Python for Machine Learning | ||
| Introduction to Quantitative Research | ||
| Evolutionary Computation | ||
| Neural Network Design | ||
| Data Science | ||
| Semantic Web and Open Data | ||
| Spatial Analysis and Modeling | ||
| Remote Sensing IMAGE ANALYSIS/GIS Integration | ||
| Stochastic Models | ||
| Data Management for Big Data | ||
| Statistical Analysis | ||
| Nonparametric Statistics | ||
| Applied Regression Modeling | ||
| Statistical Learning and Predictive Modeling | ||
| Multivariate Analysis | ||
| Introduction to Bayesian Statistics | ||
| Statistical Ecology | ||
| Computer Intensive Statistics | ||
| Univariate Quantitative Research in Education | ||
| Multivariate Quantitative Analysis in Education | ||
| Theoretical Applications and Designs of Qualitative Research | ||
| Data Analysis and Interpretation of Qualitative Research | ||
| Indigenous and Decolonizing Research Methods | ||
| Decolonizing, Indigenous, and Action-Based Research Methods | ||
| Survey Design for Social Science Research | ||
| Methods of Educational Research | ||
| Research Methods for Local Government and Community Administration | ||
| Data Wizardry in Environmental Sciences | ||
| Research Methods in the Environmental Social Sciences | ||
| Forest Biometrics | ||
| Remote Sensing of Fire | ||
| LIDAR and Optical Remote Sensing Analysis | ||
| Landscape and Habitat Dynamics | ||
| Ecological Modeling | ||
| Statistical Ecology | ||
| Water Economics and Policy Analysis | ||
| Total Hours | 12 | |
* Students should work with advisors for potential substitution waivers.
Courses to total 12 credits for 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.