> For the complete documentation index, see [llms.txt](https://www.parapathology.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.parapathology.com/appendix/courses-and-moocs.md).

# Courses and MOOCs

* This list contains the MOOCs that I find useful.
* edX <https://www.edx.org> Introduction to Biology - The Secret of Life <https://www.edx.org/course/introduction-biology-secret-life-mitx-7-00x-4>
* OsakaUx MED101x Introduction to Applied Biostatistics Statistics for Medical Research

## Courses

<https://www.canvas.net/browse/osu/courses/science-of-cancer>

<https://console.bluemix.net/dashboard/apps>

<https://cognitiveclass.ai/learn/deep-learning/>

<https://www.coursera.org/learn/python-data-analysis>

<http://www.open.edu/openlearn/free-courses/full-catalogue>

<https://www.coursera.org/learn/developer-nodered>

**Moleküler Biyoloji ve Deneysel Teknikler**

<http://www.acikders.org.tr/course/view.php?id=49>

* Community created content for the Data Science Specialization:

<http://datasciencespecialization.github.io/>

* **KAUST Academy Artificial Intelligence Courses**

<https://github.com/KAUST-Academy/Artificial-Intelligence-Courses>

Open-source repository of 5 university-level AI courses (Computer Vision, Natural Language Processing, Reinforcement Learning, Introduction to AI, and Generative AI) including 64 LaTeX/Beamer lecture slide decks (\~4,800 pages), compiled PDFs, and 291 Jupyter lab exercises/solution notebooks.

* [**Statistical Rethinking (2026 Edition)**](/appendix/clippings/statistical-rethinking-2026-edition.md) — Richard McElreath

<https://github.com/rmcelreath/stat_rethinking_2026>

A course on Bayesian data analysis, scientific modeling, causal DAGs, and computational workflows using R, Stan (`cmdstanr`), and the `rethinking` package. Features 10-week dual tracks (Beginner and Experienced) with recorded video lectures on YouTube.

* [**From Samples to Knowledge 2025: QuPath Training Course**](broken://pages/dZVwb5gdMZy87gyJVDGY) — Zbigniew Mikulski & Sara McArdle (La Jolla Institute for Immunology)

<https://www.youtube.com/playlist?list=PLlGXRBscPbCCA1yGCThNqdYKgTPOvjigp>

Hands-on 2-day workshop covering high-plex tissue imaging (RareCyte Orion 18-plex), QuPath v0.6.0+ project workflows, deep learning segmentation (InstanSeg, StarDist, Cellpose, SAM), Groovy scripting automation, composite object phenotyping, multimodal image registration (Warpy), spatial metrics, and Python clustering (QuBylab / Paquo). Step-by-step training book at [saramcardle.github.io/FS2K](https://saramcardle.github.io/FS2K/README.html).

## Tutorials

r-statistics.co

<http://r-statistics.co/>

Run bash from R <http://rpubs.com/yihui/bash-knitr>

knitr in a knutshella minimal tutorial <http://kbroman.org/knitr_knutshell/>

Use other languages in knitr <https://yihui.name/knitr/demo/engines/>

Run bash scripts

<https://github.com/yihui/knitr-examples/blob/master/027-engine-bash.Rmd>

Creating Dynamic Documents with RMarkdown and Knitr <https://rstudio-pubs-static.s3.amazonaws.com/180546_e2d5bf84795745ebb5cd3be3dab71fca.html#561_inline_r_code>

<https://www.datacamp.com/community/blog/jupyter-notebook-r>

<https://www.datacamp.com/community/open-courses/plotly-tutorial-plotly-and-r>

<http://allennlp.org/tutorials/installation>

<https://www.datacamp.com/community/tutorials/r-formula-tutorial>

<https://www.datacamp.com/community/tutorials/make-histogram-ggvis-r>

<https://www.datacamp.com/community/tutorials/sql-tutorial-query>

<https://www.datacamp.com/community/tutorials/pipe-r-tutorial>

<https://www.datacamp.com/datachats/datachat-number-one>

<https://www.r-bloggers.com/image-classification-on-small-datasets-with-keras/>

<https://jasp-stats.org/2017/12/14/new-video-perform-network-analysis-jasp/>

<http://www.listendata.com/2017/12/k-nearest-neighbor-step-by-step-tutorial.html>

<https://www.spss-tutorials.com/spss-clone-variables-tool/>

<https://www.rstudio.com/resources/webinars/>

<https://open.nasa.gov/open-data/>

<https://open.nasa.gov/explore/>

<https://www.dataiku.com/learn/>

<http://imagejdocu.tudor.lu/doku.php?id=video:beginner_help:imagej_beginner_s_tutorial>

<https://www.nlm.nih.gov/pubs/techbull/nd17/brief/nd17_ncbi_webinar_new_api_keys.html>

[https://www.r-bloggers.com/how-happy-is-your-country - happy-planet-index-visualized/?utm\_source=feedburner\&utm\_medium=email\&utm\_campaign=Feed%3A+RBloggers+(R+bloggers)](https://www.r-bloggers.com/how-happy-is-your-country%20-%20happy-planet-index-visualized/?utm_source=feedburner\&utm_medium=email\&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29)

<https://www.ncbi.nlm.nih.gov/home/coursesandwebinars/>

<https://rviews.rstudio.com/2017/10/23/the-seaclass-r-package/>

<https://ropensci.org/tutorials/>

<https://www.salford-systems.com/resources/webinars-tutorials/how-to/how-to-build-a-model>

<https://www.ibm.com/analytics/us/en/watson-data-platform/>

<https://blog.prezi.com/demand-webinar-visualizing-data-story-create-stunning-infographics/>

<https://www.datacamp.com/community/tutorials/five-tips-r-code-improve>

<https://cran.r-project.org/web/packages/broom/vignettes/broom.html>

<https://www.rplumber.io/>

<https://www.datacamp.com/community/blog/titanic-kaggle-live-coding>

<https://analyticsdefined.com/mining-enron-emails/>

<https://rviews.rstudio.com/2017/12/04/how-to-show-r-inline-code-blocks-in-r-markdown/>

<https://www.facebook.com/726282547396228/videos/1834105493280589/>

The PROCESS macro for SPSS and SAS

<http://www.processmacro.org/workshops.html>

Non-Normal Data: Shapiro Test and Box-Cox Transformation

<http://www.michaeljgrogan.com/non-normal-box-cox-transformation/>

Open Stats Lab

<https://sites.trinity.edu/osl>

R interface to Keras

<https://keras.rstudio.com/>

* [eR-BioStat](https://er-biostat.github.io/Courses/)

<https://er-biostat.github.io/Courses/abouterbiostat1/>

* Functional programming and unit testing for data munging with R

<https://b-rodrigues.github.io/fput/>