| Course | Name | Credits | Prerequisites | Schedule |
|---|---|---|---|---|
| INTR 509 | Introduction to Applied Data Science | 3 | BS degree or permission | Spring and asynchronous online |
| BCB 551 | Communicating with Data | 2 | INTR 509 or BS degree or permission | Fall and asynchronous online |
| BCB 520 | Data Visualization | 3 | STAT 251 or INTR 509 or permission | Spring and asynchronous online |
| BCB 522 | Data Science Portfolio | 1 | INTR 509 and BCB 520 (Data Viz) | Asynchronous online |
| Elective | Varies | 3 | Varies | Varies |
Learn how to think about, organize, analyze, and visualize data. Communicate data-driven insights to technical and lay audiences.
PROFESSIONAL APPLICATIONS OF DATA SCIENCE
OVERVIEW
We live in an increasingly data-driven world. Basic data literacy and data science skills are becoming central to virtually every industry. Yet, limited opportunities exist to gain these skills without an advanced background in math and computer science. To address this workforce development need, we propose a competitively valued on-line graduate certificate in the Professional Applications in Data Science. The certificate is designed to offer rigorous training in the foundations of data science to anyone with a bachelor’s degree. Participants will learn how to think about, organize, analyze, and visualize data, and communicate data driven insights to diverse audiences. The curriculum emphasizes the use of data sets drawn from each student’s individual discipline, aligning the certificate’s workforce development impacts with the University of Idaho’s land grant mission.
Value Proposition:
The graduate certificate in Professional Applications in Data Science will provide unique value to UI constituencies by:
Aligning data science training with fields of nascent demand that are part of our land grant mission, including Agriculture, Natural Resources, and Education.
Requiring a digital data science portfolio with which students can demonstrate their proficiencies to potential employers.
Emphasizing training in data communication - including verbal presentation and data visualization - two components of data science that are underrepresented in competing certificates.
Filling a growing workforce development gap by offering a unique data science certificate that is appropriate for professionals with a bachelor’s degree who do not have a rigorous background in mathematics, statistics, or computer science.
Intended Audience:
This certificate leverages the University of Idaho’s interdisciplinary culture to provide integrative training in the foundations of data science. It is intended for:
Working professionals with a bachelor’s degree whose career increasingly involves the generation, management, analysis, and visualization of large data sets. The certificate is appropriate for professionals in STEM fields, Health Care, Business, Government, Education, Journalism, Athletics, Natural Resources, and Agriculture.
Graduate students in programs outside of the core technical disciplines of data science (statistics, math, engineering, or computer science). The certificate will complement disciplinary research methods courses with training in data management, visualization, and communication.
Undergraduates at the UI who wish to incorporate data science training into their degree and graduate with a Bachelor’s degree and a graduate 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.
CURRICULUM
Prerequisites:
A Bachelor’s degree OR the student has senior standing and is enrolled in a bachelor’s degree program at the University of Idaho.
Certificate Requirements (12 Credits Total)
note: Courses designated with “BCB 5XX” are new courses that will be offered in the 2023-24 academic year
Course Descriptions
INTR 509 Introduction to Applied Data Science (3 credits)
In person (spring) and asynchronous online.
Students are provided a foundation for “thinking with data” through the introduction of computational, statistical, and data literacy skills. This includes the selection, collection, cleaning, management, descriptive analysis, and exploratory analysis of a dataset unique to their professional interests using modern computing languages. This course is taught by Dr. Michael Overton.
BCB 521 Communicating with Data (2 credits)
In person (fall) and asynchronous online.
Students are taught writing and presentation skills to improve their communication of data-driven insights to specialist and lay audiences. The course emphasizes reproducible research practices, including literate programming (R Markdown) and version control (GitHub). Course content includes the conceptual foundations of communicating with data along with written and verbal communication assignments using data sets individualized to each student’s interest.
Text: Nolan and Stoudt. 2021. Communicating with data: The art of writing for data science. Oxford University Press.
Prerequisites: INTR 509 OR A BS degree OR permission.
BCB 520 Data Visualization (3 credits)
In person (spring) and asynchronous online
This course covers the conceptual foundations of data visualization and design. Students will learn how visualization design choices related to marks and channels, color, and spatial arrangement interact with the human perceptual system. The course considers tabular, network, and spatial data, and students will implement visualizations in R.
Text: Munzner. 2014. Visualization Analysis & Design. CRC Press.
Prerequisites: INTR 509 OR A BS degree OR Stat 251 OR Permission.
BCB 522 Online Portfolio (1 credit)
Asynchronous online
This course provides feedback, review, and approval of the student’s online data science portfolio. This portfolio is intended to represent the body of work accumulated by the student over the course of the certificate. It should contain examples of novel data products (such as FAIR data sets), analyses, and visualizations. All elements of the portfolio will be hosted online (likely in a GitHub repository or professional website), be open source, and demonstrate best practices of literate programming and reproducible research.
Electives:
The certificate allows each student to customize their training by choosing a 3-credit graduate elective.
For students seeking foundational training who have not already taken Stat 431 or its equivalent, we recommend Stat 431 or a 3-credit graduate level disciplinary research methods course.
For students seeking to add the certificate to an existing degree at UI, or students who already have some advanced technical training, additional electives are possible. Note that many of these optional electives have substantial disciplinary pre-requisites. Not all electives are available in an online format.
Choose one of the following:
| Course | Name | Credits | Prerequisites |
|---|---|---|---|
| AVS 531 | Practical Methods in Analyzing Animal Science Experiments | 3 | 400-level statistics course |
| BE 521 | Image Processing and Computer Vision | 3 | (BE 242 and MATH 275) or permission |
| BE 541 | Instrumentation and Measurements | 3 | ENGR 240; Coreqs: STAT 301 |
| BIOL 526 | Systems Biology | 3 | (BIOL 115, BIOL 115L and MATH 170) or permission of instructor |
| BIOL 545 | Phylogenetics | 3 | PLSC 205 or BIOL 213 and BIOL 310 |
| BIOL 549 | Computer Skills for Biologists | 3 | BIOL 310 and STAT 251 or STAT 301; or Permission |
| BIOL 563 | Mathematical Genetics | 3 | MATH 160 or MATH 170 and STAT 251 or STAT 301 |
| CE 526 | Aquatic Habitat Modeling | 3 | A minimum grade of ‘C’ or better is required for all pre/corequisites; Prereqs: CE 322 and CE 325 or BE 355; or Permission |
| CE 579 | Simulation of Transportation Systems | 3 | Permission |
| CS 511 | Parallel Programming | 3 | CS 395 |
| CS 574 | Deep Learning | 3 | (CS 121 or MATH 330) and STAT 301 |
| CS 570 | Artificial Intelligence | 3 | CS 210 |
| CS 572 | Evolutionary Computation | 3 | CS 211 |
| CS 575 | Machine Learning | 3 | CS 210 |
| CS 577 | Python for Machine Learning | 3 | (CS 121 or MATH 330) and STAT 301 |
| CS 578 | Neural Network Design | 3 | Permission |
| CS 579 | Data Science | 3 | MATH 330 or Permission |
| CS 589 | Semantic Web and Open Data | 3 | CS 360 or CS 479 or CS 579 |
| CTE 519 | Database Applications and Information Management | 3 | NA |
| CYB 520 | Digital Forensics | 3 | CYB 310 |
| ED 571 | Introduction to Quantitative Research | 3 | Graduate standing |
| ED 584 | Univariate Quantitative Research in Education | 3 | ED 571 |
| ED 587 | Multivariate Quantitative Analysis in Education | 3 | ED 584 or Permission |
| ED 589 | Theoretical Applications and Designs of Qualitative Research | 3 | ED 574 or Permission |
| ED 590 | Data Analysis and Interpretation of Qualitative Research | 3 | ED 574 and ED 589 |
| ED 591 | Indigenous and Decolonizing Research Methods | 3 | NA |
| ED 592 | Decolonizing, Indigenous, and Action-Based Research Methods | 3 | NA |
| ED 595 | Survey Design for Social Science Research | 3 | Recommended Preparation: Foundations of Research course at graduate level. |
| EDAD 570 | Methods of Educational Research | 3 | NA |
| ENT 504 | Applied Bioinformatics | 3 | Permission |
| ENVS 511 | Data Wizardry in Environmental Sciences | 3 | STAT 251 |
| ENVS 551 | Research Methods in the Environmental Social Sciences | 3 | One course or experience in basic statistics or Instructor Permission |
| FOR 514 | Forest Biometrics | 3 | STAT 431 or equivalent |
| FOR 535 | Remote Sensing of Fire | 3 | FOR 375 or permission |
| GEOG 507 | Spatial Statistics and Modeling | 3 | STAT 431 or permission |
| GEOG 583 | Remote Sensing/GIS Integration | 3 | Coreqs: GEOG 385 or equivalent. |
| Math 538 | Stochastic Models | 3 | MATH 451 or Permission |
| MIS 555 | Data Management for Big Data | 3 | NA |
| NRS 578 | Lidar and optical remote sensing analysis using open-source software | 3 | STAT251 & WLF370 or STAT427 and NRS/FOR 472 or equivalent/instructor permission |
| POLS 558 | Research Methods for Local Government and Community Administration | 3 | STAT 251 |
| REM 507 | Landscape and Habitat Dynamics | 3 | Permission; Recommended Preparation: courses in ecology, statistics, and GIS. |
| Stat 431 | Statistical Analysis | 3 | STAT 251 or STAT 301 |
| STAT 514 | Nonparametric Statistics | 3 | STAT 431 |
| STAT 516 | Applied Regression Modeling | 3 | STAT 431 |
| Stat 517 | Statistical Learning and Predictive Modeling | 3 | STAT 431 |
| Stat 519 | Multivariate Analysis | 3 | STAT 431 or equivalent. |
| STAT 535 | Introduction to Bayesian Statistics | 3 | STAT 431 |
| STAT 555 | Statistical Ecology | 3 | MATH 451 or Permission |
| Stat 565 | Computer Intensive Methods | 3 | STAT 451, STAT 452, MATH 330, and computer programming experience or Permission |
| WLF 552 | Ecological Modeling | 3 | MATH 175 and FOR 221 or Permission. |
| WLF 555 | Statistical Ecology | 3 | MATH 451 or permission |
| WR 552 | Water Economics and Policy | 3 | AGEC 301 or AGEC 302, or ECON 351 or ECON 352, or by permission |
GENERAL UNIVERSITY REQUIREMENTS
In addition to the requirements specified in this document, the certificate would be subject to all UI Policies regarding Graduate Certificates.