With R Big Data, organizations can efficiently process large volumes of sensor, mobility, climate, or geographic data without getting bogged down in traditional desktop analyses. In this blended learning course, you’ll learn how to analyze large datasets at scale using R, Spark, Arrow, and parallel computing. You’ll work with packages such as sparklyr, arrow, multidplyr, future, furrr, and targets for fast data processing,
R Big Data focuses on processing, analyzing, and automating large datasets using the R programming language. Within Geo-ICT, this is important because geographic data increasingly consists of large amounts of sensor data, mobility data, remote sensing data, climate data, and other large-scale spatial data sources.
With R, large datasets can be efficiently imported, distributed for processing, and converted into actionable insights. Think of analyzing millions of measurement points, processing large tables, combining data sources, or building scalable data pipelines. This creates a powerful environment for data science, GIS, and modern Geo-ICT workflows.
What makes R so powerful is the combination of programmability, statistics, and a growing ecosystem for scalable data processing. This allows analyses to be accelerated, automated, and performed in a reproducible manner. Within Geo-ICT, R is increasingly being used for large-scale spatial analyses, data streams, and complex processing workflows.
In this blended learning course, you will work with key big data packages such as sparklyr, arrow, multidplyr, future, furrr, and targets. You will learn to work with Spark, efficient file formats, parallel computing, and reproducible data pipelines.
In addition, R offers extensive capabilities for combining big data with analysis, visualization, databases, and spatial workflows. This makes this blended learning course particularly relevant for GIS specialists, data analysts, researchers, and Geo-ICT professionals who want to process larger datasets more quickly and reliably.
In this blended learning course, you’ll be introduced to the key capabilities of R for big data processing. You’ll learn how to efficiently load, process, and analyze large datasets without unnecessary manual steps. You’ll work with packages such as sparklyr, arrow, multidplyr, future, and furrr.
Attention is given to scalable data processing with Spark, the use of efficient file formats, and accelerating analyses with parallel computing. You will learn how to split, process, and combine large tables and geographic datasets within reproducible R workflows.
You will also learn how to set up structured data pipelines with targets. This allows you to organize complex analyses clearly, manage dependencies, and re-run only the parts that have actually changed. This makes your workflows faster, more reliable, and easier to maintain.
During the blended learning program, you’ll work with practical datasets and learn how to apply big data workflows to Geo-ICT challenges. Upon completion, you’ll be able to process larger datasets more efficiently and set up scalable analyses for data science, GIS, and spatial projects.
Do you already have experience with R Databases, R Data Science, or R Visualization? Then this blended learning program is a logical next step toward scalable data analysis and professional data engineering within Geo-ICT.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the technical foundations and the practical application of big data in R. In the online modules, you’ll learn how to process large datasets, perform parallel analyses, and set up reproducible data pipelines using modern R packages.
You’ll discover how to work with Spark, Arrow, parallel computing, and pipeline management. You’ll also learn how to combine large datasets with analysis, visualization, and reporting within R. Thanks to unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll immediately apply the theory to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to execute scalable workflows using packages such as sparklyr, arrow, multidplyr, future, furrr, and targets.
The combination of online learning and interactive hands-on experience ensures that you not only learn how to process large datasets, but also how to organize these processes efficiently and reproducibly. After completing the blended learning program, you will be able to set up big data workflows in R for modern Geo-ICT, data science, and spatial analysis projects.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic understanding of R is helpful, but experience with Spark or big data technologies is not required. During the blended learning course, scalable data workflows and parallel computing are explained step by step using practical examples.
R allows large datasets to be processed, analyzed, and automated efficiently without having to break everything down manually. This makes analyses faster, more reproducible, and more scalable for Geo-ICT and data science projects.
The blended learning program involves working with large geographic datasets, sensor data, mobility data, climate data, remote sensing data, and other data sources commonly used in Geo-ICT and spatial data science.
In conventional computing, calculations are typically performed sequentially. Parallel computing distributes tasks across multiple processors or cores, allowing analyses of large datasets to be performed much more quickly.
During the blended learning program, you will work with tools such as sparklyr, arrow, multidplyr, future, furrr, and targets for scalable data processing, parallel computing, and reproducible data pipelines within R.
