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 Data Science Tutorials & Resources for Beginners !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) 
 (https://github.com/sindresorhus/awesome)
 Data Science Tutorials & Resources for Beginners !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
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 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/).
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.
- 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.
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. 
- 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.
- Feature engineering (https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb) - Modifying the data to better model 
predictions.
- 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.
- 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.
Data Science using Python
This list covers only Python, as many are already familiar with this language. Data Science tutorials using R (https://github.com/ujjwalkarn/DataScienceR).