rhetenor/awesome-awesomeness
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update lists

Browse Source
This commit is contained in:
Jonas Zeunert
2025-07-18 22:22:32 +02:00
parent 55bed3b4a1
commit 5916c5c074
3078 changed files with 331679 additions and 357255 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
readmes/learndatascience.md
View File

@@ -2,19 +2,20 @@
*If you want to know more about Data Science but don't know where to start this list is for you!* :chart_with_upwards_trend:
No previous knowledge required but Python and statistics basics will definitely come in handy. These ressources have been used successfully for many beginners at my local Data Science student group [ML-KA](http://ml-ka.de/).
No previous knowledge is required but Python and statistics basics will definitely come in handy. These resources have been used successfully for many beginners at my local Data Science student group [ML-KA](http://ml-ka.de/).
## What is Data Science?
- ['What is Data Science?' on Quora](https://www.quora.com/What-is-data-science)
- [Explanation of important vocabulary](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1?share=1) - Differentiation of Big Data, Machine Learning, Data Science.
- [Data Science for Business (Book)](https://amzn.to/2voPJUi) - An introduction to Data Science and its use as a business asset.
- [Data Science Process: A Beginner’s Comprehensive Guide](https://www.scaler.com/blog/data-science-process/) - Technical Skills for the Data Science: This emphasizes the practical skills needed throughout the data science process.
## Common Algorithms and Procedures
- [Supervised vs unsupervised learning](https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning) - The two most common types of Machine Learning algorithms.
- [9 important Data Science algorithms and their implementation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb)
- [Cross validation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb) - Evaluate the performance of your algorithm / model.
- [Cross validation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb) - Evaluate the performance of your algorithm/model.
- [Feature engineering](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb) - Modifying the data to better model predictions.
- [Scientific introduction to 10 important Data Science algorithms](http://www.cs.umd.edu/%7Esamir/498/10Algorithms-08.pdf)
- [Model ensemble: Explanation](https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/) - Combine multiple models into one for better performance.
@@ -94,3 +95,6 @@ Contributions welcome! Read the [contribution guidelines](contributing.md) first
To the extent possible under law, Simon Böhm has waived all copyright and
related or neighboring rights to this work. Disclaimer: Some of the links are affiliate links.
[learndatascience.md Github](https://github.com/siboehm/awesome-learn-datascience
)