Ga naar de startpagina
Zoek leermateriaalKennis & expertiseCommunities
NL|EN
Privacy policyDeelnemende instellingen
Creative CommonsCreative Commons AttributionSURFFunded by the European Union

Filteren

Zoekresultaten (28)

HBO/WO

MBO

2 juli 2025

Data Analysis and visualization BML

Learning Outcomes You can explain the fundamental concepts of data, including data types, data structures, and the importance of data quality, and you can apply these concepts in the context of both Excel and R. You can import data from various sources (such as CSV, TXT, Excel files) into both Excel and R, and you are able to select the appropriate methods and functions for different data formats and structures. You can clean and prepare data for analysis in both Excel and R by applying techniques for identifying and handling missing values, outliers, inconsistencies, and duplicates. You can apply and interpret various data analysis functions in both Excel and R, including determining minimum and maximum values, calculating percentiles, selecting specific data based on criteria, applying conditional formatting to visually identify patterns, and sorting data to gain insights. You can create effective data visualizations in both Excel and R (such as bar charts, line charts, pie charts, and box plots) to communicate patterns, trends, and relationships in data, and you can customize the visualizations for a clear and purpose-driven presentation. Duration: 18 lessons (5EC).

Website

Jurre Hageman

2 juli 2025

Data Analysis and Visualization using R

Course is in English Learning outcomes: Apply Core R Programming Skills Learners will be able to write and execute R scripts using basic and complex data types, utilize built-in functions, and implement flow control structures to automate data analysis tasks. 2. Import, Clean, and Manipulate Data Learners will demonstrate the ability to read various data file formats, tidy and transform dataframes using both traditional methods and modern packages such as tidyr and dplyr, and handle date-time and text data efficiently. 3. Visualize Data Effectively Learners will create and customize data visualizations using the ggplot2 package, including revisiting advanced plotting techniques and adding interactivity to graphical outputs. 4. Perform Exploratory Data Analysis (EDA) Learners will conduct EDA using R, applying appropriate statistical summaries and visualizations to uncover patterns, trends, and insights from datasets. 5. Utilize R Packages and Resources Independently Learners will identify, install, and apply relevant R packages to extend functionality, troubleshoot code, and solve data analysis problems, including working through exercises and interpreting provided solutions. Duration Consists of 18 lessons Study points: 5 EC

Website

Michiel Noback

6 november 2021

Julia Data Science

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization. You will learn to: Read CSV and Excel data into Julia Process data in Julia, that is, learn how to answer data questions Filter and subset data Handle missing data Join multiple data sources together Group and summarize data Export data out of Julia to CSV and Excel files Plot data with different Makie.jl backends Save visualizations in several formats such as PNG or PDF Use different plotting functions to make diverse data visualizations Customize visualizations with attributes Use and create new plotting themes Add LaTeX elements to plots Manipulate color and palettes Create complex figure layouts

Open (Access) Tekst Boek

Jose Storopoli, Rik Huijzer, Lazaro Alonso

18 augustus 2025

Geoscripting

The course is not a pure programming course, but has a rather strong focus on programming and scripting to solve geographical or spatial applied problems. During the course you will learn how to deal with both vector spatial data and raster data; such as satellite imagery used in remote sensing studies. The programming languages dealt with during the course are R and python. Professionals in private companies and research institutes are increasingly using scripting languages for applied spatial analysis. We notice that students still have a kind of 'scripting' barrier to overcome. Some students are afraid of scripting and others stick very closely to the examples given. We like to relax this attitude. From the perspective of an advanced course we like to change the cookbook approach in favor of a more explorative approach. Becoming more relaxed towards scripting and a more explorative approach means that some generic scripting principles must be known to create a 'core' script. On the other hand students have to learn which libraries are available for spatial data analysis and visualization and how these libraries can be used within the 'core' script.

Website

Dainius Masiliunas, Arend Ligtenberg, Corné Vreugdenhil, Nandika Tsendbazar

1 april 2020

Visualizing a learning trajectory

This document can be used to visualize your BC learning trajectory consisting of several courses in your educational program. After visualization you can find some reflective questions and a sheet to identify the strengths and weaknesses of your trajectory thus far.4

Document

Judith Gulikers

2023

Visualisatie leerlijn Information Literacy

Een visuele weergave van een leerlijn information literacy. Information Literacy - Attainment level: 1, 2, 3

Afbeelding

Leonie Kamminga

26 januari 2021

COVID-19 data analysis using a parameterized R Markdown and Shiny

This repo is a demo on how to use parameters and an embedded Shiny app in R Markdown

Website

Marc Teunis

2023

Visualisation learning trajectory information literacy

A visualisation of a learning trajectory for information literacy. Information Literacy - Attainment level: 1, 2, 3

Afbeelding

Leonie Kamminga

7 februari 2024

Data-analysis is not statistics: working on your data-analysis plan

The clip is aimed to support MSc thesis students in writing their data-analysis plan, which is part of the MSc thesis proposal.

Video

lenie.vanrossem@wur.nl

9 september 2018

Duke Wired

The Duke Wired! Lab conducts projects allowing the visualization of data on material culture and urban history, including student-generated projects as well as workshops and tutorials. Past and current projects include Mapping and Modeling Religious Communities in Medieval Europe; Old Stones and New Technologies: Computer Vision and Medieval Stone Carving; the Venice Interactive Visual Atlas (VIVA); and the Kingdom of Sicily Database, among many others.

Website

Hannah L. Jacobs, Paul Jaskot