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https://github.com/siboehm/awesome-learn-datascience

:chart_with_upwards_trend: Curated list of resources to help you get started with Data Science
https://github.com/siboehm/awesome-learn-datascience

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:chart_with_upwards_trend: Curated list of resources to help you get started with Data Science

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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)



*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 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.

- [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.



## 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).



### General



- [O'Reilly Data Science from Scratch (Book)](https://amzn.to/2GSjjrK) - Data processing, implementation, and visualization with example code.

- [Coursera Applied Data Science](https://www.coursera.org/specializations/data-science-python) - Online Course using Python that covers most of the relevant toolkits. 



### Learning Python



- [YouTube tutorial series by sentdex](https://www.youtube.com/watch?v=oVp1vrfL_w4&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M)

- [Interactive Python tutorial website](http://www.learnpython.org/)



### numpy

[numpy](http://www.numpy.org/) is a Python library which provides large multidimensional arrays and fast mathematical operations on them.



- [Numpy tutorial on DataCamp](https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.h3DvLnk)



### pandas

[pandas](http://pandas.pydata.org/index.html) provides efficient data structures and analysis tools for Python. It is build on top of numpy.



- [Introduction to pandas](http://www.synesthesiam.com/posts/an-introduction-to-pandas.html)

- [DataCamp pandas foundations](https://www.datacamp.com/courses/pandas-foundations) - Paid course, but 30 free days upon account creation (enough to complete course).

- [Pandas cheatsheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) - Quick overview over the most important functions.



### scikit-learn

[scikit-learn](http://scikit-learn.org/stable/) is the most common library for Machine Learning and Data Science in Python.



- [Introduction and first model application](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb)

- [Rough guide for choosing estimators](http://scikit-learn.org/stable/tutorial/machine_learning_map/)

- [Scikit-learn complete user guide](http://scikit-learn.org/stable/user_guide.html)

- [Model ensemble: Implementation in Python](http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/)



### Jupyter Notebook

[Jupyter Notebook](https://jupyter.org/) is a web application for easy data visualisation and code presentation.



- [Downloading and running first Jupyter notebook](https://jupyter.org/install.html)

- [Example notebook for data exploration](https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-instacart)

- [Seaborn data visualization tutorial](https://elitedatascience.com/python-seaborn-tutorial) - Plot library that works great with Jupyter.



### Various other helpful tools and resources



- [Template folder structure for organizing Data Science projects](https://github.com/drivendata/cookiecutter-data-science)

- [Anaconda Python distribution](https://www.continuum.io/downloads) - Contains most of the important Python packages for Data Science.

- [Spacy](https://spacy.io/) - Open source toolkit for working with text-based data.

- [LightGBM gradient boosting framework](https://github.com/Microsoft/LightGBM) - Successfully used in many Kaggle challenges.

- [Amazon AWS](https://aws.amazon.com/) - Rent cloud servers for more timeconsuming calculations (r4.xlarge server is a good place to start).



## Data Science Challenges for Beginners

Sorted by increasing complexity.



- [Walkthrough: House prices challenge](https://www.dataquest.io/blog/kaggle-getting-started/) - Walkthrough through a simple challenge on house prices.

- [Blood Donation Challenge](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/) - Predict if a donor will donate again.

- [Titanic Challenge](https://www.kaggle.com/c/titanic) - Predict survival on the Titanic.

- [Water Pump Challenge](https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/) - Predict the operating condition of water pumps in Africa.



## More advanced resources and lists



- [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience)

- [Data Science Python](https://github.com/ujjwalkarn/DataSciencePython)

- [Machine Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)



## Contribute



Contributions welcome! Read the [contribution guidelines](contributing.md) first.



## License



[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](http://creativecommons.org/publicdomain/zero/1.0)



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.