In response to the escalating frequency and sophistication of cyber threats such as unauthorized access, data breaches, and DDoS attacks, the Department of Science and Technology (DOST) developed this mandatory crash course to build a robust, empowered workforce capable of safeguarding the agency's digital infrastructure.
Module 1: Cybersecurity 101 aims to provide DOST personnel with a foundational understanding of cybersecurity principles, focusing on common threats and the best practices for protecting personal and organizational information. Ultimately, the training intends to cultivate a "security-first" culture, protect sensitive data, and ensure operational continuity across the entire DOST System.
Coding is becoming an essential skill across many fields, including agriculture, environmental science, social research, business and education. At the same time, new AI tools now allow users to write and improve code by clearly describing what they want to do. This approach, often called vibe coding, focuses on expressing intent in plain language, letting AI draft the code and refining it step by step through inspection and testing.
This course1 introduces vibe coding as a practical and responsible digital skill that is not limited to GIS or advanced programmers. While some examples will involve geospatial analysis, the same principles apply to non-spatial data, statistics, visualization, simple modeling and even basic research apps. The emphasis is on clarity of thinking, structured prompting, checking outputs carefully and understanding what the code is doing.
Participants will gain hands-on experience working in R, Python and especially in Google Colab, where AI tools such as Gemini are integrated directly into the notebook environment. The course demonstrates how interactive, collaborative notebooks can reduce copy–paste workflows and support iterative improvement, while still requiring human validation and reproducible structure.
By the end of the module, participants will be able to use AI-assisted coding to prototype analyses faster, troubleshoot errors more effectively and collaborate through shared notebooks while maintaining transparency, responsibility and methodological soundness. This course is suitable for senior high school students, university students, teachers and researchers who want to build practical AI-assisted coding skills without requiring an advanced programming background.
1 Some images and text refinements are made possible with the help of AI tools i.e., ChatGPT+ 5.2
GIS research is increasingly data-heavy, and manual point-and-click workflows do not scale well across multiple datasets, years, or geographic units. Geoscripting introduces the use of code, primarily in R, to automate spatial analysis, improve reproducibility, and shift from tool-based steps to structured workflows.
This course provides an introductory foundation in geoscripting, focusing on how common GIS operations (buffer, intersect, summarize, clip, distance, raster extraction, etc.) translate into programmable functions. Participants will learn how spatial layers become data objects, how functions operate on those objects, and why coordinate reference systems (CRS), units, and data types must be handled explicitly in scripts. The course features recorded demonstration videos that walk learners through each step and encourage them to practice the workflows on their own.
The course1 emphasizes “just enough” programming for GIS research: reading and writing simple scripts, understanding variables and functions, managing directories, and inspecting outputs before proceeding. It is not a full programming course nor a QGIS/ArcGIS tutorial, but a workflow-oriented introduction that prepares learners for AI-assisted coding (e.g. vibe coding) and more advanced computational work.
This module is suitable for senior high school students, university students, graduate students, and early-career researchers who want to move from manual GIS operations to reproducible, script-based spatial analysis.
1Some images and text refinements are made possible with the help of AI tools, i.e., ChatGPT+ 5.2
Geospatial data now underpins most decisions in the fields of forestry and agriculture, and the supply of open data sources continues to expand, especially those utilized for Geographic Information Systems (GIS). This course describes open data and its licenses, while surveying the open data landscape from data repositories and geoportals, global Earth‑observation catalogues, specialised WebGIS viewers, to crowdsourced platforms while providing a clear guide to accessing each. Good practices in using them are emphasized: reading and using metadata, assessing data quality and uncertainty, and being aware and applying the data FAIR principles. Practical segments are shown on how open GIS datasets can address research problems, from mapping forest conditions and biodiversity to selecting land-uses agricultural data. By the end, participants will be able to not only access but also turn open GIS data into clear, defensible insights for natural‑resource and agri‑environment research. This course is applicable from bachelor’s to PhD students.
The course provides an overview of Geographic Information Systems (GIS) and its application to agriculture, including forestry and environmental sciences. The course covers an introduction to GIS and other geospatial technologies such as Google Maps, Google Earth, satellites, and remote sensing. GIS functionalities and capabilities such as data capture, mapping, and spatial analysis are also described. Applications described include crop suitability analysis and hazard mapping like flooding, buffering, and overlay. Advanced applications such as precision agriculture, smart farming, the Internet of Things, and artificial intelligence are also briefly described.
This course is an interactive and self-paced learning experience. The course introduces the student to a new geographical information systems (GIS) application called ArcGIS StoryMaps. This application is developed by the Environmental Systems Research Institute (ESRI) and is a component of the ArcGIS system.
The course provides an introduction to ArcGIS StoryMaps and the different data inputs. Topics on how to gain access to the application, the advantages from using it, and its many uses are covered in the course. An example story map called a tour map is provided as a demonstration. Detailed descriptions of the processes involved in developing the tour map are provided for the student to follow.
The student is required to create his/her own tour map project. This is a requirement for the completion of the course so that the knowledge gained is applied. The web link to the completed project should be provided so that it can be assessed as part of the student evaluation process. The student can use data provided in this course or his/her own data. During the course, guides and several links to reference materials are provided. It is advisable for the student to consult these reference materials.
The course covers an introduction to Geographic Information Systems (GIS) describing the basic principles, definitions, and components of GIS. The course provides basic concepts such as geographic data including spatial and attribute data, coordinate systems, the different feature types, and two data types, namely; vector and raster data. The two commonly used software, ArcGIS and Quantum GIS (QGIS) are also described. GIS functionalities and capabilities such as data capture, data management, manipulation, mapping, and spatial analysis are also described. The use of Map Algebra and Raster Calculator in spatial analysis is also described briefly, including general applications in ecology, agriculture, forestry, natural resource management, and environmental science.
The course covers primarily two remote sensing platforms, namely, satellites and unmanned aerial vehicles (UAV). The course introduces and describes the different satellites, the different sensors and bands, the process of data collection from satellites, and the types of remote sensing systems. The course also presents the basic concepts and principles of remote sensing tools, including spectral signatures, image analysis, and image classification. In addition to satellites, the course also presents the basic concepts of drones, the different types of UAVs, and the use of light detection or ranging (LiDAR).
Applications of remote sensing and LiDAR are also described, particularly in agriculture, soil and land use change, disease detection and infestation, and others.
