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https://github.com/vanesterik/awesome-applied-data-science
A curated list of awesome things related to Applied Data Science
https://github.com/vanesterik/awesome-applied-data-science
List: awesome-applied-data-science
Last synced: 3 months ago
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A curated list of awesome things related to Applied Data Science
- Host: GitHub
- URL: https://github.com/vanesterik/awesome-applied-data-science
- Owner: vanesterik
- License: mit
- Created: 2023-12-05T11:10:25.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-02-13T09:13:58.000Z (9 months ago)
- Last Synced: 2024-05-22T00:00:35.751Z (6 months ago)
- Size: 60.5 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-applied-data-science - A curated list of awesome things related to Applied Data Science. (Other Lists / PowerShell Lists)
README
Awesome Applied Data Science
A curated list of awesome things related to Applied Data Science
## Articles
- [A Bayesian Way of Choosing a Restaurant](https://towardsdatascience.com/a-bayesian-way-of-choosing-a-restaurant-87905a745854)
- [ChatGPT and Bing AI might already be obsolete, according to a new study](https://www.windowscentral.com/software-apps/chatgpt-and-bing-ai-might-already-be-obsolete-according-to-new-study)
- [Density Kernel Depth for Outlier Detection in Functional Data](https://www.kdnuggets.com/density-kernel-depth-for-outlier-detection-in-functional-data)
- [How To Study](https://cse.buffalo.edu/~rapaport/howtostudy.html)
- [Kop of munt: puur toeval?](https://www.uva.nl/content/nieuws/nieuwsberichten/2023/10/kop-of-munt-puur-toeval.html)
- [Lasso Regression Fundamentals and Modeling in Python](https://medium.com/analytics-vidhya/lasso-regression-fundamentals-and-modeling-in-python-ad8251a636cd)
- [Mathematics for Data Science](https://towardsdatascience.com/mathematics-for-data-science-e53939ee8306)
- [My Life Stats: I Tracked My Habits for a Year, and This Is What I Learned](https://towardsdatascience.com/my-life-stats-i-tracked-my-habits-for-a-year-and-this-is-what-i-learned-4f9c3d374889)
- [PCA and kernel PCA explained](https://nirpyresearch.com/pca-kernel-pca-explained/)
- [Peeking inside SVM’s box of pandora](https://tijsvandervelden.medium.com/peeking-inside-svms-box-of-pandora-b67108668124)
- [Principal Component Analysis](https://medium.com/@denizgunay/principal-component-analysis-pca-d8edf2bb6620)
- [Quadratic Programming in SVM](https://tijsvandervelden.medium.com/quadratic-programming-in-svm-9247aafa4054)
- [Running demand forecasting machine learning models at scale](https://blog.picnic.nl/running-demand-forecasting-machine-learning-models-at-scale-bd058c9d4aa7)
- [Stochastic Gradient Descent: Math and Python Code](https://towardsdatascience.com/stochastic-gradient-descent-math-and-python-code-35b5e66d6f79)
- [Understanding Data Drift and Model Drift](https://www.datacamp.com/tutorial/understanding-data-drift-model-drift)
- [Untangling Why Knots Are Important](https://www.quantamagazine.org/why-knots-matter-in-math-and-science-20220406/)
## Books
- [99 Variations on a Proof](https://press.princeton.edu/books/hardcover/9780691158839/99-variations-on-a-proof)
- [Hands-On Machine Learning with Scikit-Learn & TensorFlow](https://github.com/yanshengjia/ml-road/blob/master/resources/Hands%20On%20Machine%20Learning%20with%20Scikit%20Learn%20and%20TensorFlow.pdf)
- [Practical Statistics for Data Scientists](https://github.com/Chandra0505/Data-Science-Resources/blob/master/machine-learning/Practical%20Statistics%20for%20Data%20Scientists.pdf)
- [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/)
## Clips
- [10 weird algorithms](https://www.youtube.com/watch?v=SmyPTnlqhlk)
- [Accept-Reject Sampling: Data Science Concepts](https://youtu.be/OXDqjdVVePY?si=lzSzeB5kglWltU4T)
- [Casual DAG's 101](https://www.youtube.com/watch?v=vZdNrKyd4xI&ab_channel=EllieMurray)
- [Decision and Classification Trees, Clearly Explained!](https://www.youtube.com/watch?v=_L39rN6gz7Y&list=PLblh5JKOoLUKAtDViTvRGFpphEc24M-QH)
- [Frequentism and Bayesianism: What's the Big Deal?](https://www.youtube.com/watch?v=KhAUfqhLakw)
- [Gaussian Mixture Models](https://www.youtube.com/watch?v=q71Niz856KE)
- [Gradient Descent, Step-by-Step](https://youtu.be/sDv4f4s2SB8?si=1mrwpcyclVFssxq5)
- [Happy Halloween (Neural Networks Are Not Scary)](https://www.youtube.com/watch?v=zxagGtF9MeU&list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1)
- [How to systematically approach truth - Bayes' rule](https://www.youtube.com/watch?v=4hHA-oqpNig)
- [Maximum Likelihood, clearly explained](https://www.youtube.com/watch?app=desktop&v=XepXtl9YKwc)
- [Overview of Quadratic Programming (QP)](https://youtu.be/GZb9647X8sg?si=oyfHcEl-EssIHOMN)
- [ROC and AUC, Clearly Explained!](https://youtu.be/4jRBRDbJemM?si=HznskAI5D01_KJuh)
- [Singular Value Decomposition](https://www.youtube.com/watch?v=rYz83XPxiZo)
- [StatQuest: Logistic Regression](https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe)
- [StatQuest: Random Forests Part 1 - Building, Using and Evaluating](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ&list=PLblh5JKOoLUIE96dI3U7oxHaCAbZgfhHk)
- [Stochastic Gradient Descent, Clearly Explained!!!](https://www.youtube.com/watch?v=vMh0zPT0tLI)
- [Support Vector Machines Part 1 (of 3): Main Ideas!!!](https://www.youtube.com/watch?v=efR1C6CvhmE&list=PLblh5JKOoLUL3IJ4-yor0HzkqDQ3JmJkc)
- [The Bayesian Workflow: Building a COVID-19 Model](https://www.youtube.com/watch?v=ZxR3mw-Znzc&feature=youtu.be)
- [The Most Useful Curve in Mathematics](https://youtu.be/OjIwCOevUew?si=f7ZqDIy96lVU5z_x)
- [The Normal Distribution, Clearly Explained](https://www.youtube.com/watch?v=rzFX5NWojp0)
- [Why do we multiply matrices the way we do?](https://www.youtube.com/watch?v=cc1ivDlZ71U)
- [Frequentism and Bayesianism: What's the Big Deal?](https://www.youtube.com/watch?v=KhAUfqhLakw)
## Papers
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://arxiv.org/pdf/1912.09363.pdf)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/pdf/2302.04761.pdf)
## Repositories
- [datascience-cookie-cutter](https://github.com/raoulg/datascience-cookiecutter)
- [polars-cheat-sheet](https://github.com/FranzDiebold/polars-cheat-sheet)
## Sites
- [Advent of Code](https://adventofcode.com/)
- [Seeing Theory](https://seeing-theory.brown.edu/)
- [Zettelkasten](https://zettelkasten.de/)
## Tools
- [Consensus](https://chat.openai.com/g/g-bo0FiWLY7-consensus)
- [Desmos](https://www.desmos.com/calculator)
- [GeoGebra - Graphing Calculator](https://www.geogebra.org/graphing?lang=en)
- [Hugging Face - The AI community building the future](https://huggingface.co/)
- [Quizizz](https://quizizz.com/admin/quiz/58e3b327442d5ea61e159d49/discrete-probability-distributions?fromSearch=true&source=)
## Wikipedia
- [Empiricism](https://en.m.wikipedia.org/wiki/Empiricism)
- [Fréchet inception distance](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance)
- [No free lunch theorem](https://en.wikipedia.org/wiki/No_free_lunch_theorem)
- [Paradigm](https://en.m.wikipedia.org/wiki/Paradigm)
- [Theory](https://en.m.wikipedia.org/wiki/Theory)
---
## Todo's
- [ ] Process [#general](https://masterapplied-cru8364.slack.com/archives/C05QYHZA9QC) channel
- [x] Process [#statistics](https://masterapplied-cru8364.slack.com/archives/C05UDL5GSEN) channel (timestamp: 2024-02-13 10:13:00)
- [ ] Process [#tech-python](https://masterapplied-cru8364.slack.com/archives/C05R8Q2CCKB) channel