The MAGIST Academic Experience

The UCLA Master of Applied Geospatial Information Systems and Technologies (MAGIST) prepares students to solve advanced problems using GIS and spatial data science.

MAGIST students will complete a curriculum consisting of:

  • Seven quarter-length courses (28 units) focusing on spatial analysis, geospatial programming, data models, image analysis, and Web/cloud GIS

  •  A capstone project (8 units with required research methods seminar), optionally developed with an external partner/collaborator, designed to develop GIS problem-solving skills

Total: 36 quarter units

Pacing and Flexibility

The MAGIST program is designed to be flexible and to give students the freedom to learn following the pacing that works best for them.

MAGIST can be completed in as little as one academic year, or in as long as three years depending on how many courses a student wants to take concurrently.

The curriculum is thoughtfully paced and expertly structured, with all courses integrating asynchronous lectures and lab demonstrations that you can view on your own schedule with meaningful and engaging GIS and geospatial project work.

Course Planner

MAGIST students are free to enroll in one, two, or three courses in each term of enrollment.  Students are considered enrolled full-time if enrolled for two or more courses concurrently.  In the interest of ensuring maximum flexibility for student schedules, part-time and full-time enrollment are allowed for MAGIST students.

Completion of the MAGIST degrees require that students complete this complete curriculum, without any substitutions or alterations to the course requirements.  All courses carry four units of graduate credit.

Fall Quarters (September – December)

  • GEOG 401: Applied Spatial Data Science

    This course is a project-based exploration of essential methods and techniques in GIS with a focus on modeling, spatial analysis and geoprocessing, spatial data manipulation, geocomputation, and data visualization.  Students apply advanced spatial analysis and data visualization methods to solving real-world problems and answering geographic research questions.

    Software used: ArcGIS Pro, R, Python

  • GEOG 410: Geospatial Databases and Data Management

    This course provides advanced instruction in theory and methods associated with geospatial data management and spatial databases.  Students learn essential database design theories and techniques, and popular data management tools interoperability methods are introduced.  Students also learn to engineer and administer enterprise spatially enabled relational database management systems.

    Software used: PostgreSQL, PostGIS, QGIS, ArcGIS Pro (optional)

  • GEOG 411: Geospatial Imagery Analysis

    This course offers instruction in advanced programmatic image processing and analysis operations applicable to remotely sensed (satellite and aerial platforms) geographic data. Students learn to build multi-step image analysis processes, and other specific techniques covered in the course include classification, filtering, change detection, spatial modeling, image correction, and terrain analysis.

    Software used: ArcGIS Pro

Winter Quarters (January – March)

  • GEOG 412: Programming for Spatial Data Science I

    This course provides conceptual and practical instruction in the use of scripting, automation, and computer programming within the geospatial sciences.  Students use the Python programming language to develop data geospatial processing scripts and applications, making use of popular geospatial data manipulation libraries.  Computer programming concepts and theory are introduced in a practical context.

    Software used: Python (geopandas, pandas, rasterio, folium, and more)

  • GEOG 413: Applied Spatial Statistics

    This course introduces the concepts and techniques fundamental to spatial statistics and the analysis and visualization of data with a geographic dimension. Beginning with the course’s introduction to statistical computing and tabular data processing and analysis techniques, students learn to apply common spatial analysis methods in a practical context. Essential concepts in spatial statistics are emphasized, including spatial relationships, spatial autocorrelation analysis, cluster analysis, spatial regression analysis, point pattern analysis, and space-time modeling.

    Software used: R Studio

  • GEOG 498: Capstone I – Geospatial Research Methods

    The purpose of this course is to (1) provide instruction in core research design techniques and geospatial technology research methods; (2) provide a structured environment for students to propose and begin a capstone project; and (3) ensure the appropriate and ethical application of geospatial methods and technology. Projects should be original analyses of geospatial data that solve a pressing problem, optionally developed conjunction with an industry partner.

Spring Quarters (April – June)

  • GEOG 414: Programming for Spatial Data Science II

    This course provides an introduction to the technologies and techniques that support the growing field of interactive Web-based GIS and mapping.  Students learn the theory and concepts underlying this rapidly growing field and applied training is provided in Web map design, development, and programming.  Students learn to develop sophisticated interactive Web maps and applications both by using existing Web mapping platforms and also by coding custom Web maps integrating HTML, CSS, the JavaScript programming language, and Web mapping code libraries.

    Software used: JavaScript programming language, Leaflet, GeoServer, D3.js

  • GEOG 415: Spatial Data Science Futures

    This course is a variable topics course that introduces students to emerging topics in geospatial data science.  Currently, this course is an introduction to cloud-based image analysis using Google Earth Engine.  The main objective of this course is to promote the transition from GUI and/or code based remote sensing software that runs on PCs and/or local servers to code based remote sensing APIs that runs in the cloud to better facilitate research and idea sharing. Currently, one of the most prominent cloud based remote sensing and spatial analysis platforms is Google Earth Engine (GEE).  This course introduces state-of-the-art techniques for imagery analysis in GEE, including applications of machine learning and artificial intelligence to remote sensing.

    Software used: Google Earth Engine

  • GEOG 499: Capstone II – Geospatial Capstone Project

    Restrictions: Completion of GEOG 498 required; faculty advisor consent required

    The purpose of this course is to facilitate completion of the program’s required capstone research project.  Students meet weekly with faculty advisor to discuss progress, learn technical writing skills, and chart goals for timely completion of the project.  Successful completion and approval of the capstone project is required for satisfactory completion of this course.

Students who wish to complete the MAGIST degree in one academic year would enroll following the above course planner.  Program courses will be offered following this consistent schedule every year to allow students to plan their timeline to completion.  Prospective students are encouraged to contact a program advisor to develop a personalized timeline for completion.

Please note: Course titles, pacing, and any descriptions of course content are subject to change.