Programming in Python

Programming plays an important role in many domains. In business and science writing or adapting computer programs to process, analyse and visualize data in a suitable format has become common practice. This course aims to help students to understand the underlying principles of programming and equip them with basic skills to create computer programs. The programming language Python serves broad application domains. Furthermore, Python is the most commonly used programming language in Machine Learning and Artificial Intelligence. The course also gives an introduction to libraries of available components, and how to use these for building your own programs.

Program/Minor

BSc Minor Bioinformatics (2025) BSc Minor Data Science (2025) BSc Minor Earth and Biosphere (2025) BSc Minor Physical Hydrology & Meteorology (2025) BSc Minor Supply Chain Analytics (2025) Bachelor Biosystems Engineering (2025) Bachelor Biotechnology (2025) Bachelor Data Science for Global Challenges (2025) Bachelor Soil, Water, Atmosphere (2025) Master Bioinformatics (2025) Master Data Science for Food and Health; Master Environmental Sciences; Master Molecular Life Sciences; Master Plant Biotechnology; Master Plant Sciences; Master Urban Environmental Management;

Learning Outcomes

Create a computer program based on a given basic algorithm expressed in plain English; Adapt and combine standard algorithms to solve a given problem; Apply standard programming constructs for a given goal: repetition, selection, functions, composition, modules, aggregated data (arrays, lists, etc.), object-oriented concepts; Explain what a given piece of programming code (in Python) does; Detect and repair coding errors in a given piece of programming code; Use existing libraries taught during the course in programs, e.g., for data manipulation and visualization (Numpy, Pandas and Matplotlib)

Prerequisites

Elementary computer skills.

Details

Institution: WUR

Period: Period 1, Period 2, Period 5

Level: Intermediate

EC: 6 ec

Regulation of Plant Development

A unique feature of plants is the fact that environmental factors play a crucial role in their development. In this course, molecular, cell biological, and physiological aspects of plant developmental processes will be discussed. We will cover several conceptual frameworks for the development of plant cell types and organs. The focus will be on how plant endogenous signals are directing plant organ development, how these signaling networks can be modified by environmental factors, and how such networks evolved. Different examples in the lectures, tutorials, and literature discussions will be used to illustrate how plants can adapt to their environment at the cellular, tissue, and whole plant levels by integrating environmental cues into developmental processes.lants can adapt at the cellular, tissue and whole plant level to their environment by integrating environmental cues into developmental processes.

Program/Minor

Master Plant Biotechnology BSc Minor Experimental Plant Sciences Master Biology Master Molecular Life Sciences

Learning Outcomes

Summarize and compare plant developmental processes, including embryogenesis, meristem initiation and maintenance, root, shoot and leaf development, and flower development; Understand the regulation of plant development at the physiological, cellular, and molecular levels; Explain how plant endogenous signals and abiotic and biotic environmental signals are perceived and integrated into the initiation, proceeding, and control of developmental processes, and argue how this regulation evolved; Recognize basic commonalities in underlying plant developmental concepts and mechanisms, and use these to argue for their involvement in other biological processes; Digest research articles in this research field and identify weak and strong points;

Prerequisites

EZO22306 Key concepts in Developmental Biology GEN20806 Plant Biotechnology MOB20306 Gene Technology, or equivalent

Details

Institution: WUR

Period: Period 5

Level: Advanced

EC: 6 ec

Reproduction of Plants

Sexual reproduction is one of the major characteristics of eukaryotes and involves a regular alternation between meiosis and fertilization. This course focuses on the basic principles of plant reproduction and the evolution of reproductive strategies in mosses, ferns, gymnosperms and angiosperms. This knowledge is used as basis to understand the concepts that underlie sexual reproduction in dicot and monocot angiosperm species. Topics including flower induction, flower development, fertilization, embryogenesis, seed development and dispersal, and fruit-development are covered.
This course aims to develop insight into evolutionary, regulatory and structural aspects of the reproductive strategies of plants. During the practicals experimental techniques used to obtain insight in structural aspects and developmental processes associated with plant reproduction are taught and practiced. These activities aim to prepare students to independently interpret reproductive strategies of any plant species.

Program/Minor

Bachelor biologie en plant sciences

Learning Outcomes

Recognize and describe the reproductive structures of mosses, ferns, gymnosperms and angiosperms (dicots, eudicots and monocots); Describe the main evolutionary steps in reproductive strategy, plant structure and anatomy from mosses to angiosperms; Make and interpret (microscopical) preparations of plants from different evolutionary groups with a focus on their reproductive structures and document the findings; Apply methods of investigation in cell biological and structural studies in the field of plant reproduction; Explain how the reproductive strategies of plants constrain their habitats; Design, carry out, interpret and document experiments in the field of reproductive strategies of plants

Prerequisites

Assumed: Structure and Function of Plants; Mandatory: Laboratory Safety & Fieldwork Safety

Details

Institution: WUR

Period: Period 1

Level: Introductory

EC: 3 ec

Research in Bioinformatics

Bioinformatics is at the heart of many modern systems biology analyses, and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. In essence, bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role in finding the genetic origins of various diseases, such as cancer, diabetes or Alzheimer.

In this course we study some key examples of relevant data-driven bioinformatics methods by reading and discussing a set of selected papers that present interesting DSAIT solutions with significant biological conclusions.

Program/Minor

MSc Data Science and Artificial Intelligence Technology

Learning Outcomes

Explain common high-throughput bioinformatics data types, such as DNA/RNA sequencing, along with experiments for their acquisition. Discuss the benefits and limitations of these methods; Discuss data-driven bioinformatics problem formulations in the literature, and their relationship with real-world challenges, assessing the alignment between them and identifying assumptions inherent to the problem definition; Explain and execute solutions for data-driven bioinformatics problems (methods, algorithms, and models) in own words to both experts and non-experts based on descriptions provided in the literature; Reason about and argue on the merits and limitations of solutions proposed for data-driven bioinformatics problems concerning both general DSAIT theory and specific alignment with the problem formulation; Assess and debate the evaluation strategy accompanying solutions for data-driven bioinformatics problems proposed in the literature. Identify merits and potential caveats of such evaluation strategy; Communicate effectively on the understanding and critical reflection of data-driven bioinformatics literature;

Details

Institution: TU Delft

Period: Period 4

Level: Master

EC: 5 ec

Science, Technology and Society

Do you think science and technology are neutral tools in gaining economic and social prosperity? Do you think innovation is always a good thing? In this course, we will question such assumptions by studying the relation between science, technology, and society. This relationship is both complex and ambiguous. For example from a societal perspective, self-driving cars may bring profits to car companies and gains in car safety, but they also raise questions on individual autonomy and responsibility of drivers; genetically modified crops may increase yields, but may also increase the power of multinational corporations over smallholder farmers; and contraceptive pills may enable family planning but also put the responsibility for contraceptive measures with women instead of men. In short: science and technology can be highly political and innovation can have consequences whose desirability can be contested. This course aims to provide students with the tools and perspectives to explore and reflect on such politics and controversies.

By drawing on the field of Science and Technology Studies (STS), students will learn to critically reflect on the relation between science, technology, and society. The students will learn a range of theoretical frameworks for understanding the relation between science, technology, and society, such as ‘large technological systems’, ‘actor-network theory’, and ‘social construction of technology’. And the students will learn to apply these frameworks to different controversial innovations.

Program/Minor

Natuurwetenschap & Innovatiemanagement (INMB) (B) Liberal Arts and Sciences (LASB) (B)

Learning Outcomes

Is familiar with central frameworks from the field of Science and Technology Studies (STS); Can apply these frameworks to concrete cases of scientific and technological developments; Can critically reflect on the role of science and technology in society; Can clearly articulate findings in a range of communication formats.

Details

Institution: UU

Period: Period 2

Level: Intermediate

EC: 7.5 ec

Seed Science and Technology

Seeds are not only the world’s major source of human and animal nutrition but also provide the basis for improving agricultural practices and managing genetic resources. High-quality seeds are required for successful crop production, propagation and breeding. Seed quality comprises a multitude of processes and events that occur during the successive stages of seed development, of which dormancy, germination, longevity and desiccation tolerance are the most important. These seed traits represent key ecological and agronomical traits that determine plant establishment in both natural and agricultural ecosystems:
– seeds are both the start and end of the plant life cycle. they are ‘time capsules’ containing the full genetic information for the survival of a species. they are the only mobile phase and therefore determine when and where the plant exists;
– seed behaviour (dormancy and germination timing) are major determinants of plant fitness in natural populations;
– seeds are ‘extremophytes’ that can tolerate desiccation to 2% moisture content, enabling long-term storage for genetic conservation and the establishment of an agricultural seed industry;
– seeds are the major currency of the biotechnology revolution;
– seed quality for plant establishment is the cornerstone of agricultural production and the seed is often the key yield component;
– the seeds of just three crops, rice, maize and wheat, constitute 2/3 of the world’s food.
The course will focus on seeds as starting materials, therefore nutrition related aspects are not part of this course. The course topics are based on transitions during the seed life cycle, which include the switch from embryogenesis to seed maturation, from seed maturation to storage (including shelf life (longevity in controlled conditions; commercially and in gene banks), from dormancy to germination until seedling establishment.
The course focuses primarily on mechanisms and regulation of these phase transitions that are strongly affected by the environment, both in an ecological and agricultural context, but will also address seed technological aspects (including priming, storage, coating), such as seed quality and its enhancement.

Program/Minor

Master Plant Biotechnology; Master Plant Sciences

Learning Outcomes

Recognize and describe the phase transitions in the seed life cycle; Explain and discuss how the different seed stages are regulated, and how these are influenced by the environment; Discuss the seed traits that represent (industrial) seed quality and give examples of the various methods to enhance seed quality Identify and select relevant information (literature) on a given or chosen topic within the course framework; Formulate a research question based on a given assignment, execute the relevant experiment(s) and present the results to the peer group

Prerequisites

Plant physiology, molecular biology and genetics. For example CLB10803 Reproduction of Plants, GEN11806 Fundamentals of Genetics and Molecular Biotechnology, GEN20806 Plant Biotechnology or PPH10806 Structure and Function of Plants. Mandatory: ZSS06100 Laboratory Safety

Details

Institution: WUR

Period: Period 3

Level: Advanced

EC: 6 ec

Signal Processing

Digital signal processing is used in many modern computer sciences systems and applications. Examples are machine learning, artificial intelligence, and big data analysis; content recommenders in information systems; speech, music and image content based retrieval and searching; music and video compression; sensor data processing in embedded systems; bioinformatics and medical data analysis. This course deals with the foundations and principles of digital signal processing. The first part concentrates on acquiring digital signals (sampling) and the basic linear filtering operations and convolution. The second part of the course introduces the concept of frequency or Fourier description of signals and systems. This concept is the foundation of many of today’s computer science-based systems and applications, and has found wide applications in the processing of a variety of sound, music, sensor, image, video and other multimedia information

Program/Minor

BSc Computer Science and Engineering

Learning Outcomes

Explain, compute and apply the properties of time-discrete linear time-invariant systems and the input-output relation; Explain and use the principle of superposition; Write arbitrary signals in terms of the sum of impulse signals; Explain, determine and apply the impulse response of a time-discrete LTI system such as FIR or IIR filter; Explain the relation between impulse response, input-output relation and convolution; Convolve input and impulse response manually or numerically using Python; Explain and apply properties of convolution, including linear vs. circular convolution; Recursively compute the output of an IIR filter and visualize convolution in Jupyter Notebook; Implement FIR filters in practical exercises and interpret results; Explain and apply sampling, Shannon’s theorem, aliasing, oversampling, and interpolation; Make and interpret audible examples of original and processed audio using Jupyter Notebook; Write a periodic signal as the sum of complex exponentials; Explain and interpret Fourier series, DTFT and DFT, and their spectra; Compute and visualize DTFT and DFT coefficients; Explain the difference between DFT (finite signals) and DTFT (infinite signals); Explain the FFT; Apply Fourier methods to practical exercises; Compute and interpret the frequency response of LTI systems; Evaluate convolution via frequency domain methods; Determine FIR impulse responses from desired frequency responses using inverse DFT; Apply theory to simplified signal processing problems, such as music compression and recognition;

Prerequisites

Required: Linear algebra: vector and matrix manipulations. Strongly advised: Calculus: series, integration, complex numbers, complex exponents, Euler's formula. Special attention is asked for complex numbers. Self-check exercises and study material is provided via Brightspace

Details

Institution: TU Delft

Period: Period 1

Level: Intermediate

EC: 5 ec

Societal Challenges & Innovation Theory

The analysis of innovation processes is much more powerful when using theories of socio-technical change and innovation. Theories of socio-technical change and innovation can be a powerful tool to understand and help solve some of the grand challenges that our society faces. The course focuses on the UN Sustainable Development Goals (SDGs), like SDG 3 on Good Health and Wellbeing, SDG7 on Affordable Clean Energy, SDG9 on Industry, Innovation and Infrastructure, SDG12 on Responsible Production and Consumption and SDG13 on Climate Action. There is, however, a lack of clear vision and understanding about how science, technology and innovation can contribute to achievement of these goals. This course uses innovation theories to tackle these grand societal challenges, and teaches students to apply these theories in a sensible, logical and practical way.
During this course we will reflect on pros and cons of different theories and assess the applicability of theories for specific research questions. We will do so by first by reflecting on what theories are and how solid theoretical models are built . Next, we review the main strands of theorizing in innovation studies and their role in innovation and transition policy.
After having reflected on the usefulness of applying theories in a research setting students practice with choosing and applying theories to a practical research question in the area of sustainable energy and transport innovations or food and health related innovations. Different theories that build on the content of Innovation Systems and Processes are central in this. After thoroughly reflecting on the usefulness, the applicability, the basic assumptions and the effects on outcomes of these theories, students need to use these theories to solve a concrete society oriented research problem.

Program/Minor

Innovation Sciences (NWIM) (M); Energy Science (ENSM); Science-based Entrepreneurship Profile GSNS (PROFILE-SBE); Sustainable Business and Innovation (SBIM); Sustainable Development (SUSD); Water Science and Management (H2OM)

Learning Outcomes

has advanced knowledge and understanding of theories of socio-technical change and innovation in the context of societal challenges (IS degree qualification 1); has the ability to apply knowledge, and problem-solving abilities related to societal challenges and innovation (IS degree qualification 3); has insight into the complex interactions between science, innovation and society and is able to reflect critically upon the roles of science and technology in organizations and society; (IS degree qualification 4); has professional and academic skills (Concise writing, Valuing literature, Argumentation and reasoning, Reflection on science and society, Selection of theories and presentation skills), in particular in relation to societal challenges and innovation; (IS degree qualification 5); is able to apply knowledge to concrete societal problems (IS degree qualification 6); is able to communicate conclusions, as well as the knowledge, reasons and considerations underlying these conclusions, to an audience of specialists and non-specialists alike. (IS degree qualification 7).

Prerequisites

Recommended prerequisites: Innovation Systems and Processes (GEO4-2257), and Innovation Management (GEO4-2268) Knowledge about Innovation Theory is a prerequisite, because GEO4-2258 is a very theoretically-oriented course. For elective students without theoretical knowledge in the field of innovation studies the course is very difficult.

Details

Institution: UU

Period: Period 3

Level: Master

EC: 7.5 ec

Soil-Plant Relations

Plant roots absorb water and nutrients from the root medium. The bio-availability of these growth factors is determined by a variety of biological, physical and chemical processes and properties. The plant itself affects these processes and soil properties in its rhizosphere. The interactions between the plant and its root environment represent the core of theory and practicals. Basic soil science is taught to be able to understand soil-plant interactions. In addition, practical consequences for maintenance of soil fertility are addressed.

Program/Minor

BSc Minor Concepts in Crop Production; Bachelor Biosystems Engineering; Bachelor Plant Sciences

Learning Outcomes

Define and characterize the three phase soil system, the properties of the soil constituents and the role of soil life; Explain the processes and factors determining bio-availability of nutrients in the soil, processes and factors determining uptake of water and nutrients and of losses of nutrients from the soil; Understand root growth, root distribution and root functioning in relation to abiotic factors in the biosphere; Use a model predicting nitrogen mineralisation; Explain the basic principles of the ecology of the rhizosphere; Execute some basic lab methods frequently used in soil-plant interactions research;

Prerequisites

Biology of plants: Students are expected to have followed one of the courses PPH10306 Biology of plants or PPH10806 Structure and function of plants or the equivalent basic Biology of plants through another course e.g. at another university. Chemistry at introductory level Computer literacy PSG biosafety laboratory and greenhouse ZSS06100 laboratory Safety (mandatory)

Details

Institution: WUR

Period: Period 3

Level: Intermediate

EC: 6 ec

Statistical Learning

Statistical learning provides a probabilistic and statistical understanding of topics in machine learning.

The overarching goal of the course is to develop methods for estimating (or `learning’) an unknown function from data or making predictions for unseen function outputs. The course aims to empower the student to make a justified decision in adopting machine learning approaches for practical problems and even design their own machine learning methodology using sound mathematical principles. This is achieved by studying key ideas, models, algorithms and theories related to the subject of statistical learning.

In the first half of the course a collection of essential models, algorithms and techniques is introduced. In the second half of the course we adopt a Bayesian/hierarchical models perspective on machine learning and study associated methods for analysing models and developing computational methodology.

Program/Minor

MSc Applied Mathematics

Learning Outcomes

Explain the challenges of statistical learning (e.g., curse of dimensionality, overfitting, bias-variance trade-off); Implement machine learning algorithms and computational methods on artificial and real-world data sets (e.g., linear regression, logistic regression, sparse regression, neural networks, decision trees); Compare the theoretical properties and practical aspects of machine learning approaches; Validate machine learning approaches on arbitrary data sets; Design tailor-made computational approaches for new learning problems by adapting, combining and/or extending methods and/or by employing a (Bayesian) hierarchical modelling approach.

Prerequisites

Required: Bachelor level knowledge and ability in calculus, probability, statistics, optimization, linear algebra; Programming skills in Python, including familiarity with the Numpy and Matplotlib packages. Recommended: Linear Algebra and Optimization for Machine Learning; Statistical Inference; Knowledge of measure theory

Details

Institution: TU Delft

Period: Period 3- 4

Level: Master

EC: 6 ec

Structural Bioinformatics & Modelling

Computational structural biology is a mature field of research whose contribution to life sciences is becoming increasingly more appreciated. The aim of this course is to provide a solid basis of computational structural biology methods, with an emphasis on practical protein modelling and simulation. Further, this course is designed to have a smooth learning curve regarding the GNU/Linux environment and its command-line interface. By the end of the course, the students are expected to master the three major computational structural biology methods – homology modelling, molecular dynamics, and protein docking – not only from a user perspective but also from a theoretical standpoint.

Program/Minor

Bio-informatics Profile (PROFILE-BIOINF)

Learning Outcomes

Molecular modelling and its applications in life sciences; The choices one has to make to model a system properly and how to describe and model interactions between particles; The modelling techniques used in this field of research, in particular homology modelling, molecular dynamics simulations and biomolecular docking; Modelling of biochemical systems on computers using different software; To choose the best modelling method for a given application; To generate 3D models of proteins from sequence information; To study the conformational landscape of molecules with molecular simulations; To model the interaction between biomolecules; To use relevant modelling software under a Linux environment; To critically analyse modelling results;

Prerequisites

Students should have a BSc-level knowledge of chemistry and/or biology (or related field), as well as a working knowledge of mathematics. In addition, students should be proficient with computers, from a user perspective.

Details

Institution: UU

Period: Period 2

Level: Master

EC: 4.5 ec

Structure and function of plants

In this course, the structure, development and physiology of plants is studied at the level of cells, tissues and the whole plant. The focus is on the form and function relationships with respect to the main functions of the plant. The course consists of a cell biological/structural part (with emphasis on cell structure and organisation, cell division, plant morphogenesis and differentiation) and a physiological part (focussing on e.g. photosynthesis, respiration, water and ion transport, regulation of growth by plant hormones and light, and interaction between plants and micro-organisms). Next to general principles also plant developmental flexibility gets attention, as well as the diversity in the plant kingdom, e.g. differences between monocots and dicots, plants with and without secondary growth, and plants with C3-, C4- or CAM-photosynthesis.

Aims of the course are the creation of a good knowledge of the relationship between structure and function of the various parts of the plant body, including processes that regulate growth and development of plants and their interrelationships. Students get experience in the methods used to investigate and analyse the structural and physiological phenomena involved.

Program/Minor

Bachelor Biology; Bachelor Plant Sciences

Learning Outcomes

Explain and report on the relation between structure and function of plants in a poster and in a group discussion Describe plant structures, growth and development by making and interpreting microscopical preparations of plant parts; Recognize and describe the various types of differentiated cells and tissues of a plant; Explain the basal mechanisms of the physiological processes of plants; Translate the effects of environmental changes on plant growth and development, both regarding structural and physiological aspects; Recognize and apply methods of investigation in cell biological, anatomical and physiological studies; Set up, execute and interpret basic physiological experiments

Prerequisites

Laboratory Safety

Details

Institution: WUR

Period: Period 5

Level: Introductory

EC: 6 ec