Professional Applications of Data Science Graduate Academic Certificate
All required coursework must be completed with a grade of 'B' or better (O-10-b).
|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|
|Instrumentation and Measurements|
|Computer Skills for Biologists|
|Aquatic Habitat Modeling|
|Computational Biology: Sequence Analysis|
|Python for Machine Learning|
|Introduction to Quantitative Research|
|Neural Network Design|
|Semantic Web and Open Data|
|Spatial Analysis and Modeling|
|Remote Sensing IMAGE ANALYSIS/GIS Integration|
|Data Management for Big Data|
|Applied Regression Modeling|
|Statistical Learning and Predictive Modeling|
|Introduction to Bayesian Statistics|
|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|
|Remote Sensing of Fire|
|LIDAR and Optical Remote Sensing Analysis|
|Landscape and Habitat Dynamics|
|Water Economics and Policy Analysis|
* 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.