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HBO/WO

MBO

30 oktober 2025

Programming Foundations

This introductory programming book offers a hands-on approach to learning Python, designed specifically for students of natural sciences who wish to enter the world of coding. Covering fundamental programming concepts - from variables and data types to functions and loops - the book presents examples and exercises rooted in applications across biology, physics, and mathematics. In addition to Python fundamentals, readers also build good practices of programming and working with computers, such as using the terminal, navigating an integrated development environment, tracking changes with Git, and writing pseudocode. Moreover, the book addresses how to usage generative AI tools effectively when learning the basics of programming. Through hands-on learning, each chapter builds both confidence and competence, guiding students from simple scripts to more complex code. By the end of the book, students will not only understand Python’s core principles but also have the skills and tools to tackle real coding challenges within their scientific disciplines in the age of AI.

Open (Access) Tekst Boek

Nikolina Šoštarić

2023

Lecture Notes chapter 2: Introduction to Python Programming

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 2 of the lecture notes introduces the Python programming language. It consists of four sub-chapters, each published as separate files. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize these .ipynb files. But the attached PDFs will give an idea of their content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Maker Lab Assignment: Programming and Data Analysis: Python Exercises

Before the Maker Lab students begin the open inquiry phases, they first learn and practice some important skills, supported by small assignments. In this assignment they are tasked with using a worksheet in Jupyter Notebook and the Python programming language for data analysis and visualization and uncertainty analysis of a provided data set. The analyzed data set is generated quickly via an online survey completed by all students. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Contents: student worksheet in Jupyter Notebook: Programming-and-DataAnalysis_Python-Exercises.ipynb (and .pdf) instructor link to Google Forms survey for data collection: https://docs.google.com/forms/d/e/1FAIpQLSfWsdadhq-v3ubt1p3EIkzxAVyAyQ-1hBYst_RodV7PEp9E7A/viewform?usp=sharing example of data file: example_Data_Collection_For_Python_Exercises.csv

Document

Forrest Bradbury

2 juli 2025

Intro to python programming

Learning Outcomes for Python Programming Modules 1. Develop and Document Python Code in Jupyter Notebooks Learners will create, document, and execute Python code using Jupyter Notebooks, effectively utilizing Markdown for readable and well-structured analysis. 2. Understand and Apply Python Data Types and Operations Learners will identify and manipulate core Python data types, perform data operations, and utilize comprehensions for efficient data processing. 3. Implement Program Logic and Functions Learners will control program flow using conditionals and loops, encapsulate logic within functions, and handle errors using Python’s exception mechanism. 4. Read, Write, and Process Data Files Learners will read from and write to files, apply regular expressions for text processing, and leverage core library functions for common data tasks. 5. Apply Object-Oriented Programming and Prepare for Data Analysis Learners will design and use classes, including data classes, and set up a Python environment with (Ana)conda, preparing to work with data analysis libraries such as pandas. Consists of 18 lessons (5EC).

Website

Michiel Noback

25 augustus 2025

Python handbook for engineering students

This book offers a concise and practical introduction to Python programming, specifically tailored for Electrical Engineering students. It begins with the fundamentals to help readers get started, covering essential concepts such as variables, operations, control structures, and plotting. As the material progresses, students are introduced to core topics in numerical computation and the basics of signal processing using powerful Python libraries such as NumPy and SciPy. To support learning, the book provides numerous clear examples and an optional step-by-step guide designed to help readers work through the material at their own pace. This structured approach makes it easy for beginners to build confidence and develop practical coding skills. Designed as a fast and accessible entry point, the book equips students with the programming foundation needed to support and enhance their learning in other engineering courses.

Open (Access) Tekst Boek

Bahareh Abdi

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

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

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

22 september 2017

Anna Watts - Numerical Integration and Neutron Stars

Video about the way programming skills are used in work situations

Video

Auteur onbekend

22 juli 2021

SERF Java Exercise Bank

Exercises for students programming in Java. Exercises are searchable by an extensive hierarchical topic structure. Solutions can be submitted and will be tested and automatic feedback will be given to the student.

Website

Kees Huizing