Twin Cities campus

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Twin Cities Campus

Geographic Information Science M.G.I.S.

Geography, Environment, Society
College of Liberal Arts
Link to a list of faculty for this program.
Contact Information
Department of Geography, 414 Social Sciences Building, 267 19th Avenue South, Minneapolis, MN 55455 (612-624-1498; fax: 612-624-1044)
Email: mgis@umn.edu
Website: http://cla.umn.edu/mgis
  • Program Type: Master's
  • Requirements for this program are current for Fall 2020
  • Length of program in credits: 35
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Geographic Information Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The professional master of geographic information science (MGIS), administered by the Department of Geography, provides graduate-level work in the theory, applications, and technology of geographic information science (GIS). Courses for the program are divided into three broad categories. Core courses provide the conceptual and theoretical underpinnings for a comprehensive, well-rounded knowledge of GIS; a set of technology courses focuses on specific software and techniques of GIS; and elective courses provide additional breadth to the program by allowing students to take courses related to their area of interest.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
Other requirements to be completed before admission:
Completion of a college-level course in statistics and computer programming, either through previous coursework or online (subject to approval by the GIS director of graduate studies), prior to or during the first year of the MGIS program.
Special Application Requirements:
Applicants must submit an application form; transcripts; a clearly written personal statement of career interests and goals; and three letters of recommendation from persons familiar with their academic and/or employment background. The GRE is not required. All materials must be submitted by January 30 for fall semester entrance and by September 1 for spring semester entrance.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 100
    • Internet Based - Writing Score: 24
    • Internet Based - Reading Score: 22
  • IELTS
    • Total Score: 7.5
  • MELAB
    • Final score: 84
The preferred English language test is Test of English as Foreign Language.
Key to test abbreviations (TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan C: Plan C requires 29 major credits and 6 credits outside the major. There is no final exam.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 2 semesters must be completed before filing a Degree Program Form.
Students must complete a professional portfolio, and a set of concluding experiences including a public presentation, an exit survey, and a final meeting with an advisor.
Required Courses (13 credits)
Take the following courses in consultation with the advisor. Courses must be taken A-F, with a minimum grade of B- earned for each.
FNRM 5131 - Geographical Information Systems (GIS) for Natural Resources (4.0 cr)
or GEOG 5561 - Principles of Geographic Information Science (4.0 cr)
GIS 5571 - ArcGIS I (3.0 cr)
GIS 5572 - ArcGIS II (3.0 cr)
GIS 8501 - GIS Project Management and Professional Development (3.0 cr)
Advanced GIS Focus Courses (6 credits)
Select at least 3 credits of 5-level coursework and at least 3 credits of 8-level coursework from the following in consultation with the advisor. If FNRM 8205 is selected, it must be taken for 3 credits. If GIS 8990 is selected, it must be taken for 3 credits. Courses must be taken A-F with a minimum grade of B- earned for each.
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science (3.0 cr)
ESPM 5295 - GIS in Environmental Science and Management (4.0 cr)
FNRM 5462 - Advanced Remote Sensing and Geospatial Analysis (3.0 cr)
GEOG 5543 - Advanced Geocomputing (3.0 cr)
GEOG 5562 - GIS Development Practicum (3.0 cr)
GEOG 5563 - Advanced Geographic Information Science (3.0 cr)
GEOG 5564 - Urban Geographic Information Science and Analysis (3.0 cr)
GEOG 5588 - Advanced Geovisualization (3.0 cr)
GIS 5574 - Web GIS and Services (3.0 cr)
GIS 5577 - Spatial Database Design and Administration (3.0 cr)
GIS 5578 - GIS Programming (3.0 cr)
CSCI 8715 - Spatial Data Science Research (3.0 cr)
FNRM 8205 - Research Problems: Spatial Data Analysis (1.0-5.0 cr)
GEOG 8290 - Seminar in GIS and Cartography (3.0 cr)
GEOG 8291 - Seminar in GIS, Technology, and Society (3.0 cr)
GEOG 8292 - Seminar in GIS: Spatial Analysis and Modeling (3.0 cr)
GEOG 8293 - CyberGIS (3.0 cr)
GEOG 8294 - Spatiotemporal Modeling and Simulation (3.0 cr)
GIS 8990 - Research Problems in GIS (1.0-6.0 cr)
Electives (10 credits)
Select at least 10 elective credits from the following in consultation with the advisor. Other courses, including those listed in the Outside Coursework requirement below, can be applied to this requirement with advisor approval.
GEOG 5511 - Principles of Cartography (4.0 cr)
GEOG 5531 - Numerical Spatial Analysis (4.0 cr)
GEOG 5541 - Principles of Geocomputing (3.0 cr)
GEOG 5543 - Advanced Geocomputing (3.0 cr)
GEOG 5562 - GIS Development Practicum (3.0 cr)
GEOG 5563 - Advanced Geographic Information Science (3.0 cr)
GEOG 5564 - Urban Geographic Information Science and Analysis (3.0 cr)
GEOG 5588 - Advanced Geovisualization (3.0 cr)
GEOG 8290 - Seminar in GIS and Cartography (3.0 cr)
GEOG 8291 - Seminar in GIS, Technology, and Society (3.0 cr)
GEOG 8292 - Seminar in GIS: Spatial Analysis and Modeling (3.0 cr)
GEOG 8293 - CyberGIS (3.0 cr)
GEOG 8294 - Spatiotemporal Modeling and Simulation (3.0 cr)
GIS 5530 - GIS Internship (1.0-3.0 cr)
GIS 5555 - Basic Spatial Analysis (3.0 cr)
GIS 5573 - Introduction to Digital Mapping: ArcGIS Basics (2.0 cr)
GIS 5574 - Web GIS and Services (3.0 cr)
GIS 5576 - Spatial Digital Humanities (3.0 cr)
GIS 5577 - Spatial Database Design and Administration (3.0 cr)
GIS 5578 - GIS Programming (3.0 cr)
GIS 5590 {Inactive} (3.0 cr)
GIS 8990 - Research Problems in GIS (1.0-6.0 cr)
Outside Coursework (6 credits)
Select at least 6 credits from the following in consultation with the advisor. Digital Archaeology must be taken if Anth 5980 is chosen.
ANTH 5980 - Topics in Anthropology (3.0 cr)
CSCI 4041 - Algorithms and Data Structures (4.0 cr)
CSCI 4131 - Internet Programming (3.0 cr)
CSCI 4707 - Practice of Database Systems (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5561 - Computer Vision (3.0 cr)
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science (3.0 cr)
CSCI 8715 - Spatial Data Science Research (3.0 cr)
ESPM 5031 - Applied Global Positioning Systems for Geographic Information Systems (3.0 cr)
ESPM 5295 - GIS in Environmental Science and Management (4.0 cr)
FNRM 5114 - Hydrology and Watershed Management (3.0 cr)
FNRM 5216 {Inactive} (1.0 cr)
FNRM 5228 - Advanced Topics in Assessment and Modeling of Forests (3.0 cr)
FNRM 5262 - Remote Sensing and Geospatial Analysis of Natural Resources and Environment (3.0 cr)
FNRM 5362 - Drones: Data, Analysis, and Operations (3.0 cr)
FNRM 5462 - Advanced Remote Sensing and Geospatial Analysis (3.0 cr)
FNRM 5562 - Field Remote Sensing (1.0 cr)
FNRM 8205 - Research Problems: Spatial Data Analysis (1.0-5.0 cr)
GDES 5341 - Interaction Design (3.0 cr)
GDES 5342 - Advanced Web Design (3.0 cr)
GDES 5371 - Data & Information Visualization (3.0 cr)
IDSC 4431 {Inactive} (2.0 cr)
IDSC 6041 - Information Technology Management (2.0 cr)
IDSC 6423 - Enterprise Systems (2.0 cr)
INET 4061 - Data Science I: Fundamentals (4.0 cr)
INET 4707 - Introduction to Databases (4.0 cr)
INET 4062 - Data Science II: Advanced (4.0 cr)
MOT 5001 - Technological Business Fundamentals (2.0 cr)
MOT 5002 - Creating Technological Innovation (3.0 cr)
MSBA 6311 - Programming for Data Science (3.0 cr)
MSBA 6321 - Data Management, Databases, and Data Warehousing (3.0 cr)
MSBA 6331 - Big Data Analytics (3.0 cr)
MSBA 6411 - Exploratory Data Analytics (3.0 cr)
PA 5231 - Transit Planning and Management (3.0 cr)
PA 5271 - Geographic Information Systems: Applications in Planning and Policy Analysis (3.0 cr)
PA 5928 - Data Management and Visualization with R (1.5 cr)
PA 5929 - Data Visualization: Telling Stories with Numbers (2.0 cr)
VMED 5181 - Spatial Analysis in Infectious Disease Epidemiology (3.0 cr)
 
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FNRM 5131 - Geographical Information Systems (GIS) for Natural Resources
Credits: 4.0 [max 4.0]
Course Equivalencies: FNRM 3131/FNRM 5131/FR 3131/
Grading Basis: A-F or Aud
Typically offered: Every Fall
Geographic information systems (GIS), focusing on spatial data development and analysis in the science and management of natural resources. Basic data structures, sources, collection, and quality; geodesy and map projections; spatial and tabular data analyses; digital elevation data and terrain analyses; cartographic modeling and layout. Lab exercises provide practical experiences complementing theory covered in lecture. prereq: Grad student or instr consent
GEOG 5561 - Principles of Geographic Information Science
Credits: 4.0 [max 4.0]
Course Equivalencies: Geog 3561/ Geog 5561
Typically offered: Every Fall & Spring
Introduction to the study of geographic information systems (GIS) for geography and non-geography students. Topics include GIS application domains, data models and sources, analysis methods and output techniques. Lectures, reading, and hands-on experience with GIS software. prereq: grad
GIS 5571 - ArcGIS I
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
First of a two-course series focusing on ArcGIS Desktop. Overview of ArcGIS system and its use for spatial data processing. Data capture, editing, geometric transformations, map projections, topology, Python scripting, and map production. prereq: [GEOG 5561 or equiv, status in MGIS program, familiarity with computer operating systems] or instr consent
GIS 5572 - ArcGIS II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Continues GIS 5571. Raster analysis, dynamic segmentation, geometric networks, geocoding, Python scripting, and data interoperability. Substantial projects include map and poster design and production. prereq: [5571, [GEOG 5561 or equiv], in MGIS program] or instr consent
GIS 8501 - GIS Project Management and Professional Development
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Project management/professional development. Portfolio creation, career exploration, degree program planning. GIS project management through lectures, class exercises, guest speakers. prereq: MGIS student or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc. prereq: Familiarity with Java, C++, or Python
ESPM 5295 - GIS in Environmental Science and Management
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Application of geographic information science and technologies (GIS) in complex environmental problems. Students gain experience in spatial data collection, database development, and spatial analysis, including GNSS and field attribute collection, image interpretation, and existing data fusion, raster/vector data integration and analysis, information extraction from LiDAR data, DEM conditioning and hydrologic analysis, neighborhood analysis, bulk processing and automation, and scripting. Problems vary depending on topics, often with extra-University partners. *Please note that students should have completed a semester-long, introductory lab/lecture GIS course at the graduate or undergraduate level before enrolling in this course, e.g., FNRM 5131. We do not require any given course because students come from varied universities and backgrounds. That said, we assume a knowledge commensurate with a comprehensive introductory course. Students seeking a first course are directed to FNRM 5131. If you have questions regarding your capabilities, please contact the instructor prior to enrolling.
FNRM 5462 - Advanced Remote Sensing and Geospatial Analysis
Credits: 3.0 [max 6.0]
Course Equivalencies: FNRM 3462/FNRM 5462
Typically offered: Every Spring
This course builds on the introductory remote sensing class, FNRM 3262/5262. It provides a detailed treatment of advanced remote sensing and geospatial theory and methods including Object-Based Image Analysis (OBIA), lidar processing and derivatives, advanced classification algorithms (including Random Forest, Neural Networks, Support Vector Machines), biophysics of remote sensing, measurements and sensors, data transforms, data fusion, multi-temporal analysis, and empirical modeling. In-class and independent lab activities will be used to apply the course topics to real-world problems. Prior coursework in Geographic Information Systems, remote sensing, and statistics is necessary. Prereq: grad student or instr consent
GEOG 5543 - Advanced Geocomputing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The availability of computing infrastructures such as high-performance and cloud computing, highspeed networks, and rich data has led to a new scientific paradigm using computational approaches, termed computational science. Geocomputation is the "application of a computational science paradigm to study a wide range of problems in geographical and earth systems (the geo) contexts" (Openshaw, 2014). This course will delve into advanced topics in geocomputation as well as related areas ranging from geographic information and spatial big data to cyberinfrastructure and parallel computation. Students will engage in hands-on exercises learning principles and best practices in geocomputing while using cutting-edge computational infrastructures.
GEOG 5562 - GIS Development Practicum
Credits: 3.0 [max 3.0]
Prerequisites: GIS 5571 or #
Typically offered: Periodic Fall
Algorithms/data structures for digital cartographic data, topological relationships, surface modeling, and interpolation. Map projections, geometric transformations, numerical generalization, raster/vector processing. Hands-on experience with software packages. prereq: GIS 5571 or instr consent
GEOG 5563 - Advanced Geographic Information Science
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Advanced study of geographic information systems (GIS). Topics include spatial data models, topology, data encoding, data quality, database management, spatial analysis tools and visualization techniques. Hands-on experience using an advanced vector GIS package. prereq: B or better in 3561 or 5561 or instr consent
GEOG 5564 - Urban Geographic Information Science and Analysis
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Core concepts in urban geographic information science including sources for urban geographical and attribute data (including census data), urban data structures (focusing on the TIGER data structure), urban spatial analyses (including location-allocation models), geodemographic analysis, network analysis, and the display of urban data. prereq: 3561 or 5561
GEOG 5588 - Advanced Geovisualization
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The generation and use of geographic information has become an integral part of our daily life, science, and technology. This has led to increasing interest in the design and development of interactive maps and dynamic geographic visualizations in 2D, 3D, and Web environments. The Advanced Geovisualization course intends to equip students with the knowledge and advanced technical skills needed to design and implement effective maps and create dynamic and interactive visualizations using geospatial data sets.
GIS 5574 - Web GIS and Services
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Plan, design, develop, publish web-based GIS solution. Build websites, prepare data for web. Commercial software, Open Source software, volunteer geographic information, open GIS standards/developing web GIS application. Hands-on experience with variety of web GIS technologies/software. prereq: [GEOG 5561 or equiv, in MGIS program] or instr consent
GIS 5577 - Spatial Database Design and Administration
Credits: 3.0 [max 1.0]
Typically offered: Every Spring
This semi-synchronous online graduate level course is aimed at students who have a foundation in GIS and spatial analysis methods and applications, and are interested in expanding their knowledge into the area spatial database design and spatial analysis. The course covers the following topics: 1) SQL and spatial-SQL queries, database design, and ArcServer Administration. This is an applied course and the objective is to introduce the fundamentals of databases, learn about how spatial data is treated into databases and apply spatial analysis methods. Students taking the class will have moderate to advanced understanding of GIS classes, but do not have much exposure to databases.
GIS 5578 - GIS Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
This Python-focused GIS course is intended for students who have some Python programming experience, or have experience with other programming language(s) and knowledge transferable to Python. Following a review of Python basics, students will use Python modules for spatial data management, mapping, and analysis, including ArcGIS's ArcPy package; work with classes in Python; develop custom modules; and create development environments. A semester-long programming project applying Python skills to a GIS challenge is a significant component of the course. prereq: instr consent
CSCI 8715 - Spatial Data Science Research
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Motivation, models of spatial information, querying spatial data, processing strategies for spatial queries, multi-dimensional storage/access methods, spatial graph datasets, spatial data mining, trends (e.g., spatio-temporal databases, mobile objects, raster databases), research literature, how to pursue research. prereq: 4707 or 5707 or 5715 or GIS 5571 or GIS 5573
FNRM 8205 - Research Problems: Spatial Data Analysis
Credits: 1.0 -5.0 [max 10.0]
Typically offered: Every Fall, Spring & Summer
Independent research under faculty guidance. prereq: instr consent
GEOG 8290 - Seminar in GIS and Cartography
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Selected concepts/methods. Topics, which vary yearly, include spatial analysis methods in GIS; advanced visualization methods; data quality and error propagation in GIS; generalization methods in GIS and cartography; role of time in GIS; interactive/animated cartography; incorporation of uncertainty. prereq: instr consent
GEOG 8291 - Seminar in GIS, Technology, and Society
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Relationships between practice of GIS and political, economic, legal, institutional structures of society. Effects of GIS on society. Nontraditional spaces in GIS. GIS and local decision making. Privacy issues. prereq: instr consent
GEOG 8292 - Seminar in GIS: Spatial Analysis and Modeling
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Overview of Geographic Information Systems (GIS) and spatial analysis/modeling of human/environmental systems. Spatial statistics, modeling spatiotemporal processes, simulation techniques, visualization, complex systems/complexity. Guidance in thesis/dissertation research. prereq: 3511 [or equiv statistics course], [3561 or 5561 or equiv intro GIS course] or instr consent
GEOG 8293 - CyberGIS
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Just as physical infrastructure provides services such as electricity, plumbing, and road networks to communities across the world, cyberinfrastructure has emerged to provide computational services and capabilities to scientific communities. Cyberinfrastructure integrates high-performance computing, digital sensors, virtual organizations, and software tools and services to facilitate computationally-intensive and collaborative scientific research. CyberGIS, broadly defined as cyberinfrastructure-based geographic information systems, integrates cyberinfrastructure, geographic information systems (GIS), and spatial analysis to enable collaborative geographic problem solving. This course will delve into advanced topics within the context of cyberGIS and related technologies. Particular emphasis will be placed on raster data processing including a broad introduction to raster data, cartographic modeling, and raster data manipulation. We will situate raster data processing in the broader context of geographic information science and cyberGIS focusing on the how synthesizing computational thinking and spatial thinking influence methodological approaches. Students will be expected to draw on their own experiences and backgrounds to enhance discussions, labs, and research projects. Students will gain hands-on experience developing methods to analyze and manipulate raster data.
GEOG 8294 - Spatiotemporal Modeling and Simulation
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Many geographic, societal, and environmental phenomena as well as biological and ecological systems involve dynamic processes that are changing in space and time. Examples include hurricanes, animal migrations, spread of diseases, human mobility and population dynamics. Movement is a key to understanding the underlying mechanisms of these dynamic processes. Today, the availability of an unprecedented amount of movement observations at ne spatial and temporal granularities has resulted in substantial advances in GISciences approaches for the analysis, modeling, and simulation of movement and its patterns. Spatiotemporal models and simulation techniques are often used to analyze and better understand the patterns of spatiotemporal processes, and to assess their behavioral responses in varying environmental conditions. This seminar introduces students to the concepts of spatiotemporal processes and patterns. We review existing methods for modeling and simulation of spatiotemporal phenomena, especially movement. Students will develop computational skills to model a phenomena of their choice and create simulations.
GIS 8990 - Research Problems in GIS
Credits: 1.0 -6.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Project of sufficient scope/complexity to document student's ability to apply spatial analysis and visualization techniques to real-world problems. Supervised by faculty member. prereq: MGIS student, instr consent
GEOG 5511 - Principles of Cartography
Credits: 4.0 [max 4.0]
Course Equivalencies: Geog 3511/Geog 5511
Typically offered: Every Fall
GEOG 3511/5511 is a basic introduction to cartography?the art, science, and technology of maps and map making. Our primary emphasis will be on map making, with lesser emphasis on cartographic research and the history of cartography. Lectures will focus on modern cartographic design principles, how they were developed, and how they might be changing. Lab assignments help develop skills using digital tools for producing effective maps. The course has several specific learning objectives: ? use software to create maps that communicate their subjects appropriately and effectively using sound cartographic design principles ? acquire or produce a base map that is appropriate in scale, projection, and generalization ? select and aggregate data appropriately to represent on a map using a suitable symbolization strategy ? gain an understanding of how current changes in technology impact maps and map making ? understand how fundamental design decisions might differ for printed maps and web/mobile maps ? understand how contemporary GIS and cartography are interrelated, including the use of GIS becoming ubiquitous and map making becoming increasingly available to anyone ? gain an appreciation for the 3,500+ year history of maps!
GEOG 5531 - Numerical Spatial Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: Geog 3531/5531
Typically offered: Every Fall
Applied/theoretical aspects of geographical quantitative methods for spatial analysis. Emphasizes analysis of geographical data for spatial problem solving in human/physical areas.
GEOG 5541 - Principles of Geocomputing
Credits: 3.0 [max 3.0]
Course Equivalencies: Geog 3541/Geog 5541
Grading Basis: A-F or Aud
Typically offered: Every Spring
The availability of computing infrastructures such as high-performance and cloud computing, high-speed networks, and rich data has led to a new scientific paradigm using computational science. Geocomputation is the "application of a computational science paradigm to study a wide range of problems in geographical and earth systems (the geo) contexts" (Openshaw, 2014). This course will introduce students to geocomputation as well as related areas including big spatial data, and cyberinfrastructure. Students will engage in hands-on-exercises learning principles and best-practices in geocomputing. The ability to program is an essential skill for GIScientists. Learning to program takes time and a lost of practice, and in this course students will learn how to develop programs in the Python programming language to solve geospatial problems.
GEOG 5543 - Advanced Geocomputing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The availability of computing infrastructures such as high-performance and cloud computing, highspeed networks, and rich data has led to a new scientific paradigm using computational approaches, termed computational science. Geocomputation is the "application of a computational science paradigm to study a wide range of problems in geographical and earth systems (the geo) contexts" (Openshaw, 2014). This course will delve into advanced topics in geocomputation as well as related areas ranging from geographic information and spatial big data to cyberinfrastructure and parallel computation. Students will engage in hands-on exercises learning principles and best practices in geocomputing while using cutting-edge computational infrastructures.
GEOG 5562 - GIS Development Practicum
Credits: 3.0 [max 3.0]
Prerequisites: GIS 5571 or #
Typically offered: Periodic Fall
Algorithms/data structures for digital cartographic data, topological relationships, surface modeling, and interpolation. Map projections, geometric transformations, numerical generalization, raster/vector processing. Hands-on experience with software packages. prereq: GIS 5571 or instr consent
GEOG 5563 - Advanced Geographic Information Science
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Advanced study of geographic information systems (GIS). Topics include spatial data models, topology, data encoding, data quality, database management, spatial analysis tools and visualization techniques. Hands-on experience using an advanced vector GIS package. prereq: B or better in 3561 or 5561 or instr consent
GEOG 5564 - Urban Geographic Information Science and Analysis
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Core concepts in urban geographic information science including sources for urban geographical and attribute data (including census data), urban data structures (focusing on the TIGER data structure), urban spatial analyses (including location-allocation models), geodemographic analysis, network analysis, and the display of urban data. prereq: 3561 or 5561
GEOG 5588 - Advanced Geovisualization
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The generation and use of geographic information has become an integral part of our daily life, science, and technology. This has led to increasing interest in the design and development of interactive maps and dynamic geographic visualizations in 2D, 3D, and Web environments. The Advanced Geovisualization course intends to equip students with the knowledge and advanced technical skills needed to design and implement effective maps and create dynamic and interactive visualizations using geospatial data sets.
GEOG 8290 - Seminar in GIS and Cartography
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Selected concepts/methods. Topics, which vary yearly, include spatial analysis methods in GIS; advanced visualization methods; data quality and error propagation in GIS; generalization methods in GIS and cartography; role of time in GIS; interactive/animated cartography; incorporation of uncertainty. prereq: instr consent
GEOG 8291 - Seminar in GIS, Technology, and Society
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Relationships between practice of GIS and political, economic, legal, institutional structures of society. Effects of GIS on society. Nontraditional spaces in GIS. GIS and local decision making. Privacy issues. prereq: instr consent
GEOG 8292 - Seminar in GIS: Spatial Analysis and Modeling
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Overview of Geographic Information Systems (GIS) and spatial analysis/modeling of human/environmental systems. Spatial statistics, modeling spatiotemporal processes, simulation techniques, visualization, complex systems/complexity. Guidance in thesis/dissertation research. prereq: 3511 [or equiv statistics course], [3561 or 5561 or equiv intro GIS course] or instr consent
GEOG 8293 - CyberGIS
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Just as physical infrastructure provides services such as electricity, plumbing, and road networks to communities across the world, cyberinfrastructure has emerged to provide computational services and capabilities to scientific communities. Cyberinfrastructure integrates high-performance computing, digital sensors, virtual organizations, and software tools and services to facilitate computationally-intensive and collaborative scientific research. CyberGIS, broadly defined as cyberinfrastructure-based geographic information systems, integrates cyberinfrastructure, geographic information systems (GIS), and spatial analysis to enable collaborative geographic problem solving. This course will delve into advanced topics within the context of cyberGIS and related technologies. Particular emphasis will be placed on raster data processing including a broad introduction to raster data, cartographic modeling, and raster data manipulation. We will situate raster data processing in the broader context of geographic information science and cyberGIS focusing on the how synthesizing computational thinking and spatial thinking influence methodological approaches. Students will be expected to draw on their own experiences and backgrounds to enhance discussions, labs, and research projects. Students will gain hands-on experience developing methods to analyze and manipulate raster data.
GEOG 8294 - Spatiotemporal Modeling and Simulation
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Many geographic, societal, and environmental phenomena as well as biological and ecological systems involve dynamic processes that are changing in space and time. Examples include hurricanes, animal migrations, spread of diseases, human mobility and population dynamics. Movement is a key to understanding the underlying mechanisms of these dynamic processes. Today, the availability of an unprecedented amount of movement observations at ne spatial and temporal granularities has resulted in substantial advances in GISciences approaches for the analysis, modeling, and simulation of movement and its patterns. Spatiotemporal models and simulation techniques are often used to analyze and better understand the patterns of spatiotemporal processes, and to assess their behavioral responses in varying environmental conditions. This seminar introduces students to the concepts of spatiotemporal processes and patterns. We review existing methods for modeling and simulation of spatiotemporal phenomena, especially movement. Students will develop computational skills to model a phenomena of their choice and create simulations.
GIS 5530 - GIS Internship
Credits: 1.0 -3.0 [max 6.0]
Grading Basis: S-N only
Typically offered: Every Fall & Spring
Practical hands-on experience using GIS to solve problems in a real-world work environment. prereq: instr consent, strong GIS/mapping skills
GIS 5555 - Basic Spatial Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
How to use spatial data to answer questions on a wide array of social, natural, and information science issues. Exploratory data analysis/visualization. Spatial autocorrelation analysis/regression. prereq: [STAT 3001 or equiv, MGIS student] or instr consent
GIS 5573 - Introduction to Digital Mapping: ArcGIS Basics
Credits: 2.0 [max 2.0]
Course Equivalencies: Geog 3573/GIS 5573
Prerequisites: [GEOG 5561 or equiv, in MGIS program] or #
Grading Basis: A-F only
Typically offered: Every Fall
Desktop mapping functions using ArcGIS software. Application of systems to display/analysis of geographical data. prereq: [GEOG 5561 or equiv, in MGIS program] or instr consent
GIS 5574 - Web GIS and Services
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Plan, design, develop, publish web-based GIS solution. Build websites, prepare data for web. Commercial software, Open Source software, volunteer geographic information, open GIS standards/developing web GIS application. Hands-on experience with variety of web GIS technologies/software. prereq: [GEOG 5561 or equiv, in MGIS program] or instr consent
GIS 5576 - Spatial Digital Humanities
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to Spatial Digital Humanities GIS 5576 is a basic overview of desktop GIS (both Esri and open source), as well as an introduction to a number of other mapping techniques (such as Esri Maps for Office, ArcGIS Online, web mapping basics, georeferencing historical maps, etc) in addition to digital scholarship techniques. Course objectives include: understanding the basics of mapping and geospatial information using GIS; documenting and managing spatial data using coherent/standardized methods; understanding several spatial analysis methods that are relevant to student research area; and applying spatial research methods into student research.
GIS 5577 - Spatial Database Design and Administration
Credits: 3.0 [max 1.0]
Typically offered: Every Spring
This semi-synchronous online graduate level course is aimed at students who have a foundation in GIS and spatial analysis methods and applications, and are interested in expanding their knowledge into the area spatial database design and spatial analysis. The course covers the following topics: 1) SQL and spatial-SQL queries, database design, and ArcServer Administration. This is an applied course and the objective is to introduce the fundamentals of databases, learn about how spatial data is treated into databases and apply spatial analysis methods. Students taking the class will have moderate to advanced understanding of GIS classes, but do not have much exposure to databases.
GIS 5578 - GIS Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
This Python-focused GIS course is intended for students who have some Python programming experience, or have experience with other programming language(s) and knowledge transferable to Python. Following a review of Python basics, students will use Python modules for spatial data management, mapping, and analysis, including ArcGIS's ArcPy package; work with classes in Python; develop custom modules; and create development environments. A semester-long programming project applying Python skills to a GIS challenge is a significant component of the course. prereq: instr consent
GIS 8990 - Research Problems in GIS
Credits: 1.0 -6.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Project of sufficient scope/complexity to document student's ability to apply spatial analysis and visualization techniques to real-world problems. Supervised by faculty member. prereq: MGIS student, instr consent
ANTH 5980 - Topics in Anthropology
Credits: 3.0 [max 6.0]
Typically offered: Periodic Fall, Spring & Summer
Topics specified in Class Schedule.
CSCI 4041 - Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 4041/CSci 4041H
Typically offered: Every Fall & Spring
Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables and hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees. prereq: [(1913 or 1933) and 2011] or instr consent; cannot be taken for grad CSci cr
CSCI 4131 - Internet Programming
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4131/CSci 5131
Typically offered: Every Fall & Spring
Issues in internet programming. Internet history, architecture/protocols, network programming, Web architecture. Client-server architectures and protocols. Client-side programming, server-side programming, dynamic HTML, Java programming, object-oriented architecture/design, distributed object computing, Web applications. prereq: 4061, 4211 recommended, cannot be taken for grad CSci cr
CSCI 4707 - Practice of Database Systems
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4707/CSci 5707/INET 4707
Typically offered: Every Fall & Spring
Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications. prereq: 4041 or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5561 - Computer Vision
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: CSci 5511, 5521, or instructor consent.
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc. prereq: Familiarity with Java, C++, or Python
CSCI 8715 - Spatial Data Science Research
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Motivation, models of spatial information, querying spatial data, processing strategies for spatial queries, multi-dimensional storage/access methods, spatial graph datasets, spatial data mining, trends (e.g., spatio-temporal databases, mobile objects, raster databases), research literature, how to pursue research. prereq: 4707 or 5707 or 5715 or GIS 5571 or GIS 5573
ESPM 5031 - Applied Global Positioning Systems for Geographic Information Systems
Credits: 3.0 [max 3.0]
Course Equivalencies: ESPM 3031/ESPM 5031
Grading Basis: A-F or Aud
Typically offered: Every Spring
GPS principles, operations, techniques to improve accuracy. Datum, projections, and coordinate systems. Differential correction, accuracy assessments discussed/applied in lab exercises. Code/carrier phase GPS used in exercises. GPS handheld units, PDA based ArcPad/GPS equipment. Transferring field data to/from desktop systems, integrating GPS data with GIS. prereq: Grad student or instr consent
ESPM 5295 - GIS in Environmental Science and Management
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Application of geographic information science and technologies (GIS) in complex environmental problems. Students gain experience in spatial data collection, database development, and spatial analysis, including GNSS and field attribute collection, image interpretation, and existing data fusion, raster/vector data integration and analysis, information extraction from LiDAR data, DEM conditioning and hydrologic analysis, neighborhood analysis, bulk processing and automation, and scripting. Problems vary depending on topics, often with extra-University partners. *Please note that students should have completed a semester-long, introductory lab/lecture GIS course at the graduate or undergraduate level before enrolling in this course, e.g., FNRM 5131. We do not require any given course because students come from varied universities and backgrounds. That said, we assume a knowledge commensurate with a comprehensive introductory course. Students seeking a first course are directed to FNRM 5131. If you have questions regarding your capabilities, please contact the instructor prior to enrolling.
FNRM 5114 - Hydrology and Watershed Management
Credits: 3.0 [max 3.0]
Course Equivalencies: FNRM 3114/FNRM 5114
Typically offered: Every Fall
Hydrologic cycle and water processes in upland/riparian systems. Applications of hydrological concepts to evaluate impacts of forest and land management activities on water yield, streamflow, groundwater erosion, sedimentation, and water quality. Concepts, principles, and applications of riparian/watershed management. Regional/national/global examples. Forest ecosystems.
FNRM 5228 - Advanced Topics in Assessment and Modeling of Forests
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even Year
Application of recently developed mathematics, computer science, and statistics methodologies to natural resource functioning, management, and use problems. Specific topics, software, and methodologies vary. prereq: 3218, Math 1272, Stat 5021
FNRM 5262 - Remote Sensing and Geospatial Analysis of Natural Resources and Environment
Credits: 3.0 [max 3.0]
Course Equivalencies: FNRM 3262/FNRM 5262
Typically offered: Every Fall & Spring
Introductory principles and techniques of remote sensing and geospatial analysis applied to mapping and monitoring land and water resources from local to global scales. Examples of applications include: Land cover mapping and change detection, forest and natural resource inventory, water quality monitoring, and global change analysis. The lab provides hands-on experience working with satellite, aircraft, and drone imagery, and image processing methods and software. Prior coursework in Geographic Information Systems and introductory Statistics is recommended. prereq: Grad student or instr consent
FNRM 5362 - Drones: Data, Analysis, and Operations
Credits: 3.0 [max 6.0]
Course Equivalencies: FNRM 3362/FNRM 5362
Grading Basis: A-F only
Typically offered: Every Spring
This course explores principles and techniques of Unmanned Aircraft Systems (UAS, also "drones"), applied to natural resource and environmental issues. The course provides hands-on experience with UAS vehicles, sensors, imagery, and software. Course topics include: UAS flight characteristics, regulations/safety, mission planning, flight operations, data collection, image analysis, and applications. Examples of UAS applications to be explored include: forest and natural resource inventory, wetland monitoring, and land cover mapping. Prereq: grad student or instr consent
FNRM 5462 - Advanced Remote Sensing and Geospatial Analysis
Credits: 3.0 [max 6.0]
Course Equivalencies: FNRM 3462/FNRM 5462
Typically offered: Every Spring
This course builds on the introductory remote sensing class, FNRM 3262/5262. It provides a detailed treatment of advanced remote sensing and geospatial theory and methods including Object-Based Image Analysis (OBIA), lidar processing and derivatives, advanced classification algorithms (including Random Forest, Neural Networks, Support Vector Machines), biophysics of remote sensing, measurements and sensors, data transforms, data fusion, multi-temporal analysis, and empirical modeling. In-class and independent lab activities will be used to apply the course topics to real-world problems. Prior coursework in Geographic Information Systems, remote sensing, and statistics is necessary. Prereq: grad student or instr consent
FNRM 5562 - Field Remote Sensing
Credits: 1.0 [max 1.0]
Course Equivalencies: FNRM 3562/FNRM 5562
Typically offered: Every Fall
This course is intended to be taken with, or after, the introductory remote sensing class, FNRM 3262/5262. It builds on the introductory course by providing a field context to the remote sensing discipline. We will focus on field methods and associated analyses that are typical in using and applying imagery and other spatial data. We will use a variety of remote sensing imagery, maps, field data collection tools, and software. Students will learn in an active, hands-on, way through multiple small-group field exercises. This course includes two eight-hour weekend field sessions. Prerequisite: grad student
FNRM 8205 - Research Problems: Spatial Data Analysis
Credits: 1.0 -5.0 [max 10.0]
Typically offered: Every Fall, Spring & Summer
Independent research under faculty guidance. prereq: instr consent
GDES 5341 - Interaction Design
Credits: 3.0 [max 3.0]
Course Equivalencies: DHA 4384/GDES 5341
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Design of interactive multimedia projects. Interactive presentations and electronic publishing. Software includes hypermedia, scripting, digital output. prereq: [[2334 or 2342], design minor] or graphic design major or grad student or instr consent
GDES 5342 - Advanced Web Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Internet-based design. Static web pages, embedded media, cascading style sheets. Design and usability of interface between humans and technology. Evaluation of visual elements that control and organize dealings with computers to direct work. Students develop designs, do usability testing. prereq: [[2334 or 2342], design minor] or graphic design major or grad student or instr consent
GDES 5371 - Data & Information Visualization
Credits: 3.0 [max 3.0]
Course Equivalencies: GDes 4371/GDes 5371
Grading Basis: A-F only
Typically offered: Every Spring
Visual articulation of data. Expansive research, meticulous gathering of data, analysis. Develop cohesive graphical narratives/build solid foundation in craft of presenting data.
IDSC 6041 - Information Technology Management
Credits: 2.0 [max 2.0]
Course Equivalencies: IDSc 6040/MBA 6241
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Management of information systems, information technology (IT) in global organization. Strategic uses of IT. Alignment of IT, organizational strategy, internet/Web technologies, e-commerce customer services. Integration of e-business applications, interorganizational systems, systems implementation. Management of information as resource. Lecture, case analysis, classroom discussion. Prereq MBA student.
IDSC 6423 - Enterprise Systems
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Enterprise Systems are the information core of diverse organizations and play a major role in their management and performance. This course provides the context of Enterprise Systems role in organization's journey of Digital Transformation. It examines Enterprise System's structural aspects such as governance, program & change management, sourcing, development (programming), testing, operations, and regulatory compliance. Business cases provide real world examples across these subjects and focus on specifics such as labor multi-sourcing and A/B testing strategies.
INET 4061 - Data Science I: Fundamentals
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to data science. Design strategies for business analytics: statistics for machine learning, core data mining models, data pipeline, visualization. Hands-on labs with data mining, statistics, and in-memory analytics software. prereq: Basic statistics and programming skills, laptop
INET 4707 - Introduction to Databases
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 4707/CSci 5707/INET 4707
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course prepares students to make decisions regarding the database technologies that should be included in an organization?s information technology portfolio. To that end, it covers: 1. The theory and concepts of relational and NoSQL databases, the two predominant families of database technology. 2. How to represent data in technology-independent, relational, and NoSQL data models. 3. How to query relational and NoSQL databases, including hands-on experience with relational and NoSQL databases. 4. How to determine which categories of relational and/or NoSQL databases are appropriate for a given application. 5. Research into current and emerging database technology trends. Recommended prerequisites: INet 4001 or CSci 4061, at least 45 cr completed; CSci majors contact CSci Dept before registering.
INET 4062 - Data Science II: Advanced
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
This course is a follow-up to INET 4061: Data Science Fundamentals. It covers the tools required to apply and implement data science techniques such as mathematical programming libraries, cloud resources, and big data databases. It also gives an overview of advanced data science methodologies such as deep learning, reinforcement learning, recommendation systems, and linear programming. Previously offered as INET 4710. prereq: Basic programming knowledge (Java, Python, R). Linear algebra and calculus strongly recommended (e.g. MATH 2243 and 2263). INET 4061 strongly recommended.
MOT 5001 - Technological Business Fundamentals
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Provides scientists and engineers with a working knowledge of the broader business context in which science and technology ideas are translated into solutions that address market needs and generate economic value. This two-unit course will broaden students? business knowledge and project leadership abilities, enabling technical professionals to increase their business impact and career success. The three modules of the course will build practical knowledge and skills in (1) project leadership, professionalism, teamwork, and effective communication, (2) the process of innovation (i.e., transforming technical ideas into value-creating solutions) and (3) business acumen fundamentals. prereq: Degree seeking or non-degree graduate students
MOT 5002 - Creating Technological Innovation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This hands-on, project-based course provides students the perspective of a Technology Leader of an organization or product team. Details the innovation process, from an idea's inception through impact in the economy, regardless of organizational setting. Explores how solutions are developed to become ready for broader market deployment. Includes testing and development of the problem-solution fit, probing of solutions for robustness, and testing of both technical and operational scaling of proposed solutions. Examines the human aspects of innovation, specifically issues of team building and readiness. Considers the broader system for innovation, including the role of key stakeholders in shaping its success in order to arrive at an impactful solution. Addresses intellectual property, the effect of regulations and social and cultural differences across varied global markets, and the personal skills necessary to align and manage these issues. prereq: Degree seeking or non-degree graduate students.
MSBA 6311 - Programming for Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
According to recent industry surveys, Python is one of the most popular tools used by organizations data analysis. We will explore the emerging popularity of Python for tasks such as general purpose computing, data analysis, website scraping, and data visualization. You will first learn the basics of the Python language. Participants will then learn how to apply functionality from powerful and popular data science-focused libraries. In addition, we will learn advanced programming techniques such as lambda functions and closures. We will spend most of our class time completing practical hands-on exercises.
MSBA 6321 - Data Management, Databases, and Data Warehousing
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of database modeling/design, normalization. Extract, transform, load. Data cubes/setting up data warehouse. Data pre-processing, quality, integration/stewardship issues. Advances in database/storage technologies.
MSBA 6331 - Big Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Exploring big data infrastructure and ecosystem, ingesting and managing big data, analytics with big data; Hadoop, MapReduce, Hive, Spark, scalable machine Learning, scalable real-time streaming analytics, NoSQL, cloud computing, and other recent developments in big data.
MSBA 6411 - Exploratory Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of exploratory business analytics. Solving real-world business problems using appropriate data analysis techniques and effective technical/managerial communication. Foundational methods allow for the detection of relationships and patterns in structured and unstructured data through clustering, dimensionality reduction, probabilistic graphical models, anomaly detection, and deep neural networks.
PA 5231 - Transit Planning and Management
Credits: 3.0 [max 3.0]
Course Equivalencies: CEGE 5213/PA 5231
Typically offered: Fall Even Year
Principles/techniques related to implementing transit systems. Historical perspective, characteristics of travel demand, demand management. Evaluating/benchmarking system performance. Transit-oriented development. Analyzing alternative transit modes. System design/finance. Case studies, field projects. prereq: Grad student or instr consent
PA 5271 - Geographic Information Systems: Applications in Planning and Policy Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Introduction to GIS. Applications in public planning and policy analysis. Operational skills in GIS software. Mapping analysis of U.S. Census material. Local/state government management/planning. Spatial statistical analysis for policy/planning. prereq: Major in urban/regional planning or instr consent
PA 5928 - Data Management and Visualization with R
Credits: 1.5 [max 1.5]
Typically offered: Every Fall
Introduction to R Studio software. Use of R Studio to carry out R file and related database management functions. Tools and techniques for data analysis and statistical programming in quantitative research or related applied areas. Topics include data selection, data manipulation, and data and spatial visualization (including charts, plots, histograms, maps, and other graphs). Prerequisite knowledge: Introductory statistics; ability to create bar graphs, line graphs, and scatter plots in MS Excel; and familiarity with principles of data visualization.
PA 5929 - Data Visualization: Telling Stories with Numbers
Credits: 2.0 [max 2.0]
Typically offered: Every Fall & Spring
Tools for communicating quantitative information in an intelligent, effective and persuasive way. Topics covered include 1) writing and speaking about data; 2) data management in Excel in order to prepare data for charting; 3) understanding and ability to deploy core concepts in of design, layout, typography and color to maximize the impact of their data visualizations 4) determining which types of statistical measures are most effective for each type of data and message; 5) determining which types of design to use for communicating quantitative information; and 6) designing graphs and tables that are intelligent and compelling for communicating quantitative information.
VMED 5181 - Spatial Analysis in Infectious Disease Epidemiology
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Spatial distribution of disease events. Exposures/outcomes. Factors that determine where diseases occur. Analyzing spatial disease data in public health, geography, epidemiology. Focuses on human/animal health related examples. prereq: Intro to epidemiology, statistics,