Academics

Fall 2023, Texas A&M University- College Station 

This course focused on fundamental cartographic principles such as design, orientation, and data presentation. The course also gave a broad overview of the geodetic principles that impact the process of creating maps.

Skills: ArcGIS Pro, ArcGIS Online, ArcGIS Storymaps

Coursework

• Final Project: Cancer Risk Storymap

• Hurricane Katrina: Basic Storymap

• Other coursework: Google Drive

Spring 2024, Texas A&M University- College Station 

This course focuses on the structure and applications of GNSS and provides more detailed and hands-on descriptions of geodetic concepts. It also delves into the specifics of Geographic reference systems and overviews many of the skills and tools used by Land Surveyors.

Lab projects focused on the collection and preparation of GNSS and Survey data, and included a wide variety of digital and physical tools.

Skills: GNSS Applications, ArcGIS FIeldmaps, ArcGIS Online, DroneDeploy

Coursework

• Final Project: Survey of Campus Printers

• Horizontal Well Plat

• Civil3D Building Pad 

• Sample TXDOT Geodetic Control Report

Spring 2024, Texas A&M University- College Station 

This course provides a thorough overview of the science, technology, and processes of remote sensing in Geoscientific applications.

Lab projects mainly consisted of image preparation and presentation through ENVI and ArcGIS Pro.

Skills: ENVI, Remote Sensing Technology

Coursework

• Louisiana Isodata Image

• Georectified Image of Atlanta

• Myrtle Beach Georectified Color-Composite Image

• Orthorectified Image of Miami

• Yellowstone Image-Map

Fall 2024 - Texas A&M University, College Station

This course serves as an introduction to GIS as a broad subject. Topics include the applications and processes of GIS, as well as foundational geographic and data science subjects including geodesy, cartography, databases, and data manipulation.

Lab projects were primarily focused on exploring the capabilities of the ArcGIS Suite, including ArcGIS Pro most prominently. Many projects involved being given a real-world problem (site-selection, trend analysis, environmental risk interpretation, etc.) and a dataset, and being asked to provide a GIS-based solution.

Skills: ArcGIS Pro, Vector & Raster Data Analysis, GeoProcessing

Coursework

• Crater Lake Map Layout

Fall 2024 - Texas A&M University, College Station

This course provided an overview of GeoDatabases as a format for storing and modeling both geographic and non-geographic datasets. The course also covered the process of interacting with file and server-based data models as well as the use of relational databases to create efficient data for clients. 

Lab projects were primarily focused on creating file and server-based GeoDatabases and exploring structure and manipulation of said databases to enhance data efficiency.

Skills: SQL (Structured Query Language), ESRI File GeoDatabases, Microsoft SQL Server, Relational Databases

Fall 2024 - Texas A&M University, College Station

This course went into depth onto various methods of image preprocessing, classification, and visualization of Remotely Sensed Images, primarily tailored to geoscientific applications and environmental change monitoring. 

Spring 2025 - Texas A&M University, College Station

This course covered the fundamentals of creating WebGIS applications, including through ArcGIS products (Dashboards, WebApp Builder, Experience Builder) and Leaflet.

Lab projects primarily covered coding basic applications in HTML/CSS/JavaScript and use of ArcGIS WebApp Builder applications.

Coursework:

• Final Project: Tornado Dashboard

Skills: HTML, CSS, JavaScript, HighCharts, Leaflet.js, ArcGIS Maps SDK for JavaScript, ArcGIS Experience Builder, ArcGIS Dashboards 

Spring 2025 - Texas A&M University, College Station

This course covered statistical methods related to Spatial Analysis and GIS, including Spatial Interpolation, Nearest Neighbor Analysis, and Machine Learning.

Class projects were primarily conducted using the R coding language and involved statistical tests using provided datasets.

Skills: R & RStudio, Spatial Statistics, Machine Learning

Spring 2025 - Texas A&M University, College Station

This course served as a further in-depth study of the processes to classify and extract information from remotely sensed images (both orbital and sub-orbital). Topics included Electromagnetic & Reflective theory, Image classification methods, and commonly used Image Processing Algorithms

Class projects primarily involved the processing of remotely sensed images using ENVI

Skills: ENVI, Electromagnetic Theory, Remote Sensing, Image Classification, Raster Analysis