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Zoekresultaten (35)

HBO/WO

MBO

2023

Lecture Notes chapter 3: Uncertainty Analysis

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 3 of the lecture notes introduces Uncertainty Analysis using Python. 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. 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

21 juli 2026

Introduction to Stochastic Hydrology

Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.

Document

Marc F.P. Bierkens

2023

Maker Lab readings and lecture slides for students

This collection of the Maker Lab's readings and lecture slides for students is primarily used in the first course phase on training specific research skills and preparing students for the open inquiry phases. Specifically, students learn about experimental design and uncertainty analysis, and learn and practice some important programming skills for controlling sensors and processing and analyzing data. This material is further divided between: Lecture Notes (student reading material) Lecture Slides and Lecture Announcements (detailing what the instructor and students are doing in class sessions)

Forrest Bradbury

2023

Maker Lab Lecture Notes

This collection of the Maker Lab's Lecture Notes (alongside the Lecture Sldies and several Assignments files) detail some of the learning material and activities that prepare students for the open inquiry phases. Specifically, students learn to use Python for data analysis & visualization and uncertainty analysis, and are given an overview into the processes of empirical research in the natural sciences. Note: These Lecture Notes files are written as Jupyter Notebooks, often containing interactive features (though static PDFs are also provided for the benefit of non-users of Jupyter Notebooks). It is possible and convenient to download all of the Maker Lab's Jupyter Notebook files at once via the JupyterPython-materials zip file - 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

Forrest Bradbury

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 Lecture Slides and Lecture Announcements

This collection of the Maker Lab's Lecture Slides and Lecture Announcements (alongside the Lecture Notes and several Assignments files) detail the learning material and activities that prepare students for the open inquiry phases. Specifically, students learn about experimental design and uncertainty analysis, and learn and practice some important programming skills for controlling sensors and processing and analyzing data. For context of how these lectures fit into the course activities, please see the example course schedule (published as a sub-page of the main "Maker Lab: full course materials" page). The Lecture Slides are .PDF files of the Powerpoint slides, while the .TXT documents are pre- or post-lecture announcements posted to the course's digital learning environment. The course runs over 15 teaching weeks, with two 90 minute class sessions per week ("a" and "b"), which explains the naming system of these files, with names that begin with the week number and session letter. Note that many class sessions (especially after the open inquiry phases begin in session 5b) have no lectures or other materials because they are devoted to students' team presentations or individual meetings between teams and instructor. Additionally, lectures 1b and 2a include exercises and discussions using Arduino code files, with the file extensions .INO, which are also included here. NOTE: These uploads are not yet working. Please reach out if you're interested in the files. NOTE: Instructors interested in having the original powerpoint files of the Lecture Slides are encouraged to reach out to me via forrestbradbury ("AT") gmail.com .

Document

Forrest Bradbury

2023

Lecture Notes chapter 5: Fourier Transform

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 5 of the lecture notes introduces the discrete Fourier Transform using Python. This lecture notes document is presented in the Maker Lab course as optional reading, but sometimes relevant to students with projects involving (for example) signal analyses in the temporal frequency domain. 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. 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

29 februari 2024

Risk and Reliability for Engineers

This book covers a wide range of topics that involve the use of probability to solve problems in engineering design and research. Although it is relevant for a wide range of disciplines, it draws heavily on the fields of civil engineering, environmental engineering and the geosciences. Specific topics include risk analysis, probabilistic design, reliability-based design (component and system reliability). Future versions of this book will include additional applications of probability theory related to continuous distributions, extreme value analysis and expanded chapters on component and system reliability. A number of practical examples and exercises are included, both analytic and numerical, some of which use an interactive feature that allows Python code to be used in the web browser without any additional software installation.

Open (Access) Tekst Boek

Robert Lanzafame

2023

Maker Lab: full course materials

This is an open inquiry natural science lab course utilizing Maker tools (e.g. inexpensive electronic sensors/sensorboards controlled by Arduinos). Students are tasked with conceiving, designing, and carrying out scientific experiments with significant process guidance but little experimental oversight, instead leveraging the Maker movement's online resources and open source hardware and software tools. Target audience: The course is given at Amsterdam University College to a multidisciplinary mix of natural science bachelor's students, usually in their second or third year of a three year bachelor's. Duration / Study load: 6 ECTS course, equivalent to ~168 total hours of student work, evenly spread across a 16 week term Over-arching learning goals: (see also the Learning Objectives subpage for specific, assessable list) students navigate the iterative empirical research process, including conception, experimental design, testing, data & uncertainty analyses, and drawing conclusions students build self-efficacy in conducting quantitative empirical research and creating scientific knowledge students improve science communication skills in an authentic (team) setting Directory: Course materials are published in the following locations (see links to them below) course description (pre-requisites, learning activities, teaching roles, grouping of students, learning environment) learning objectives course schedule readings and lecture slides for students assignments (and rubrics) resource list: facilities, equipment, software JupyterPython-materials zip file This page's linked websites give official course descriptions. Any questions about this Maker Lab course can be addressed to Forrest via: forrestbradbury@gmail.com

Website

Forrest Bradbury