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3 changes: 3 additions & 0 deletions README.md
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- ['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 Bookcamp (Book)](https://www.manning.com/books/data-science-bookcamp) - Learn data science with Python by building five real-world projects!
- [Regression, a Friendly Guide (Book)](https://www.manning.com/books/regression-a-friendly-guide) - A friendly, fun guide to making accurate predictions and revealing relationships in your data using linear and logistic regression.

## Common Algorithms and Procedures

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- [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.
- [Pandas Workout](https://www.manning.com/books/pandas-workout) - Work out your pandas skills against dozens of real-world challenges, each carefully designed to build an intuitive knowledge of essential pandas tasks.

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