In the frame of my activities for the Microscopy Imaging Center and Science IT Support at Bern University, I give several courses, mostly based on image processing, but also other more general data science topics. Here's a short summary of these:

Image processing with Python

Course given at Bern University on 25.01.2019, 01.02.2019, 26.06.2019

This course is given over a full day for an audience of people familiar with Python but not necessarily Python's scientific packages stack. It starts with an introduction to Numpy in the context of images and then covers most of the essential parts of the image processing package scikit-image (image import, thresholding, filtering, morphological operations, region properties etc.). It also includes a few more advanced topics that can be adjusted depending on the audience. Those include for example 3D volume rendering, image registration, deep learning.

The whole course is given through interactive Jupyter notebooks developed by me and available here and includes a set of exercises. During the lecture, participants are given access to a remote computing environment (Jupyterhub), but the course can also be run interactively through the binder service, which can be accessed by clicking on the following badge:

Image processing with Python for beginners

Course given at Fribourg University in March 2019 and March 2020

This course is given over a full day for an audience of complete beginners. It starts by introducing the essentials of the Python language needed for the course (variables, list, flow control etc.) and then goes through basics of image processing (thresholding, filtering, measuring). The goal of the course is to give the necessary keys to beginners who wish then to further explore image processing with Python.

The whole course is given through interactive Jupyter notebooks developed by me and available here and includes a set of exercises. During the lecture, participants are given access to a remote computing environment (Jupyterhub), but the course can also be run interactively through the binder service, which can be accessed by clicking on the following badge:

Data science with Python and Pandas

Given at Bern University on 05.09.2019

This course is a half-day introduction to the data analysis package Pandas for an audience familiar with Python. It covers Pandas data structures (series, dataframes) and major operations than can be performed on them (joining, grouping, applying functions) as well as plotting.

The whole course is given through interactive Jupyter notebooks developed by me and available here and includes a set of exercises. During the lecture, participants are given access to a remote computing environment (Jupyterhub), but the course can also be run interactively through the binder service, which can be accessed by clicking on the following badge:

Scalable analytics with Python (Dask)

Course given on 11.02.2020 This half-day course is an introduction to the Python package Dask, a tool which simplifies parallel computing and the handling of very large datasets on various platforms (laptop, cloud, cluster). The course covers the data structures offered by Dask (dask array, dask dataframe) as well as the more general concept of delayed functions. It also presents the dask dashboard tools and how to deploy dask on an HPC cluster.

The whole course is given through interactive Jupyter notebooks developed by me and available here and includes a set of exercises. During the lecture, participants are given access to a remote computing environment (Jupyterhub), but the course can also be run interactively through the binder service, which can be accessed by clicking on the following badge:

Tools for reproducible science

Given at Bern University on 09.09.2019

This course takes the form of a tutorial where participants learn some best practices in science in particular when dealing with data analysis code. It presents tools and services such as GitHub and Zenodo and the participants also get to explore and use them and create a minimal example that can be found here.

Fundamentals of digital image processing

Given at Bern University on 5.09.2019 In the frame of the Advanced microscopy lecture, I give an introduction to digital image processing. The course is a 45min lecture and is partially based on a set of small interactive applications illustrating basics concepts. These applications are available in this gallery or in an editable mode by clicking on this badge:

Introduction to Fiji

Given at Bern University on 12.03.2019, 19.03.2019, 21.01.2020

Fiji is one of the most popular image processing software based on a graphical interface. This introductory course given over a full day, presents all basics features of the software (importing images, filtering, stacks, annotations, object detection) but does not cover scripting. The slides corresponding to this course can be found here.

Summary

2019

Image processing with Python, Full day, 25 January 2019
Image processing with Python, Full day, 01 February 2019
Introduction to Fiji, Full day, 12 March 2019
Introduction to Fiji, Full day, 19 March 2019
Image processing with Python, Full day, 26 June 2019
Data science with Python and Pandas, Fully day, 5 September 2019
Tools for reproducible science, Half day, 9 September 2019
Advanced microscopy lecture: Fundamentals of Digital Image Processing, 45min, 5 September 2019

2020

Image processing with Python, Full day, 21 January 2020
Introduction to Fiji, Full day, 28 January 2020
Scalable analytics with Python (Dask), Half day, 11 February 2020
Fundamentals of wide field microscopy: image processing, Half day, 01 May 2020
Fundamentals of confocal microscopy: image processing, Half day, 06 September 2020

Course exchanges with other universities

Fribourg University: Image processing with Python for beginners, Full day, March 2019
Fribourg University: Image processing with Python for beginners, Full day, March 2020
EPFL: Python for bioimage analysis, 2 days, March 2019 (postponed)