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Zoekresultaten (39)

HBO/WO

MBO

1 september 2023

Practical: Presenting statistical data

Understand and demonstrate the data representation for AI and ML modelling. Apply a range of plotting techniques to visualise data. Be able to check for fairness, handle outliers and missing data.

Document

HCAIM Consortium

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

1 augustus 2026

Introduction to data management as part of Open Science

Data management is part of the research cycle. It is crucial to think about it in the design phase of any experiment. This self-study module gives you hands on advice on what to do (and what NOT) with research data. It is not about (statistical) data analysis. This self-study module helps students get familiar with setting up their data management plan. As it’s an introduction to this topic, it is for students at beginner level. After completing this self-study module, you should be able to answer the following questions: What do I store? Where do I store? How can others understand what I stored? Why should I care?

Website

Susanne Kirchhoff

8 mei 2018

10-01: Data analysis (descriptive)- micro lecture A DATA MATRIX IN SPSS

In this micro lecture the following items will be explained regarding this lecture: A basic introduction to the data matrix in SPSS.

Website

Henk van der Kolk

8 mei 2018

10-23: Data analysis (descriptive)- ASSIGNMENT 4: HOW TO USE SPSS?

In this assignment the following items will be explained regarding this assignment: The aim of this assignment is to get to know SPSS and practice some basic aspects of this statistical program.

Document

Henk van der Kolk

8 mei 2018

07-02: Measurement - micro lecture A DATA MATRIX IN SPSS

In this micro lecture the following items will be explained regarding this lecture: A basic introduction to the data matrix in SPSS

Website

Henk van der Kolk

23 augustus 2023

Lecture: Data Preparation and Exploration

To be able to chose the most suited data preparation method based on the case prepare data in practice (handle missing values, create new derived features) Data enrichment Ethical: anonymisation and problems with this (identification possible in indirect ways) - again, there should be some examples out there Imputation – mention that it can introduce bias and that this needs to be kept in mind New feature creation – loss of proper semantics Ethical: remove bias from the dataset Parallels and differences between sampling of data in statistics and acquisition of data (including big data) for ML and AI

Website

HCAIM Consortium

1 februari 2023

Introduction to data science

This e-learning series is intended as a basic introduction to data science. It is aimed particularly at veterinary undergraduates, but may also be useful for others. The topics are: Topic 1: basic principles of entering, storing and using data Topic 2: introduction to reading a dataset into R Topic 3: introduction to working with datasets in R Topic 4: introduction to creating summary statistics and plots in R The videos provided here serve as an introduction to the online exercises and self-assessment exercises found at https://www.costmodds.org/teaching/voc_data_science/ Installation of the freely available software R (https://cran.r-project.org) is required to complete practical exercises 2-4.

Website

Matt Denwood

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

2025

A very short introduction to R

Being able to use a programming environment such as R is a valuable skill for our students. R provides opportunities for using larger and more complex datasets in practicals, case studies and theses. Compared to other programming languages, it is relatively easy to learn, which makes it suitable for education. Because R is open access, graduates can keep using it when they can no longer use the university’s software licences. “A (very) short introduction to R” teaches the basics in 1-2 hours with explanations and short exercises. This tutorial is used by lecturers worldwide. We expanded this tutorial to a series of self-study modules of 1-2 hours each. The modules can be used by: Lecturers who would like to use R in their computer practicals, but are afraid that learning R will consume time from the course content. The self-study modules enable these lecturers to use R with little time investment, since they can simply assign students to do (some of) the modules as preparation for their own computer practical. Students who lag behind in programming skills. The background of students in courses becomes increasingly diverse, which sometimes leads to discontent between advanced and less advanced students. With these modules the less advanced students can mend their deficits independently before they come to class, thereby levelling the differences between students and improving the learning experience of all. Students who are writing their BSc, MSc or PhD thesis. Without programming skills, students often fall back on Excel for processing and analysing data (which is often not suitable for the task) and their supervisors spend much time teaching them how to deal with the data. With the self-study modules, the supervisor’s time can be used more effectively to interact with the student about the scientific question to be tackled. Researchers and professionals who want to start using R. The modules are short and free of charge, which lowers the threshold for beginners.

Website

Claudia Brauer