Professional Applications in Data Science Certificate

A graduate certificate that trains non-specialists how to think about, organize, analyze, and visualize data, and communicate data-driven insights to technical and lay audiences.

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.

The University of Idaho’s value proposition for Professional Applications in Data Science Certificate:

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)

Course	Name	Credits	Prerequisites	Schedule
INTR 509	Introduction to Applied Data Science	3	BS degree or permission	Spring and asynchronous online
BCB 5xx	Communicating with Data	2	INTR 509 or BS degree or permission	Spring and asynchronous online
BCB 5xx	Data Visualization	3	STAT 251 or INTR 509 or permission	Fall and asynchronous online
BCB 5xx	Data Science Portfolio	1	INTR 509 and BCB 520 (Data Viz)	Asynchronous online
Electives	Varies	3	Varies	Varies

Note: Courses designated with “BCB 5XX” are new courses that will be offered in the 2023-24 academic year

Electives

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

Governance and Administration

Administrative Home

Data Science is an applied field of study combining insights and techniques from multiple disciplines such as statistics, computer science, informatics, and social sciences. To maintain the interdisciplinary focus of the program, we propose housing the Professional Applications in Data Science certificate in the Bioinformatics and Computational Biology Program within the College of Science. Three of the required courses that will be created will use the BCB prefix. In addition, the BCB Administration (Director and Governing Board) and faculty are drawn from almost every College at the UI and are routinely called upon to consider curriculum and training from an interdisciplinary perspective.

Governance and Oversight

The BCB Director, governing board, and faculty would have curricular control over the certificate content.

We recommend that the BCB Director or the Dean of the College of Science appoint a faculty coordinator for the certificate in Professional Applications in Data Science. The faculty coordinator will be responsible for guiding the certificate through curriculum approvals, helping recruit students, assessment, and coordinating with faculty who teach the included courses.

A subcommittee within BCB could also be appointed to help maintain the certificate’s interdisciplinary focus and broad domain impact.

General University Requirements

In addition to the requirements specified in this document, the certificate would be subject to all UI Policies regarding Graduate Certificates.