Understanding GIS data - You will learn how to distinguish vector, raster, and attribute data and choose the right format for each task, so you can prepare analyses faster and avoid misreading spatial results.
Preparing data for analysis - You will master importing data from Excel, CSV files, and databases, then cleaning, joining, and transforming it, so you can build reliable datasets for further GIS work on your own.
Interpreting statistics better - You will learn how to read data distributions, detect outliers, and examine correlations, helping you assess spatial analysis results more accurately and support decisions with evidence.
Creating clear maps - You will learn to design thematic maps with effective classification, legends, and color choices, so you can present indicators and analysis results in a way your audience can quickly understand.
Location and accessibility analysis - You will practice analysing object distribution, distance, accessibility, and buffers, so you can assess site potential, coverage areas, and spatial relationships between different locations.
Maps for reports and presentations - You will create professional map layouts for reports and presentations, add legends, scales, and labels, and export polished outputs to PDF and graphic formats ready for use.
Publishing maps online - You will explore how to build interactive maps with layers, filters, and search tools, so you can share analysis results conveniently with your team, clients, or other stakeholders.
Automating GIS workflows - You will see how to automate data updates, report generation, and map creation, allowing you to reduce time spent on repetitive tasks and make your analytical workflow more efficient.
Computer literacy - You should be comfortable working on a computer, saving and organising files, and using basic system and office application features while handling data for analysis.
Working with spreadsheets - You should know how to open and edit Excel or CSV files, understand table structure, and work correctly with columns and records, because data will be prepared for analysis.
Basic data analysis - You should understand basic analytical concepts such as averages, data distribution, and correlation, so you can interpret statistical and spatial results more easily.
Analytical thinking - You should be able to analyse relationships between datasets and draw conclusions from observations, because the course includes interpreting results and working with layers.
