Difference between revisions of "Python for Data Science"
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Anaconda is a free and open source distribution of the Python and R programming languages for data science and machine learning related applications (large-scale data processing, predictive analytics, scientific computing), that aims to simplify package management and deployment. Package versions are managed by the package management system conda. https://en.wikipedia.org/wiki/Anaconda_(Python_distribution) | Anaconda is a free and open source distribution of the Python and R programming languages for data science and machine learning related applications (large-scale data processing, predictive analytics, scientific computing), that aims to simplify package management and deployment. Package versions are managed by the package management system conda. https://en.wikipedia.org/wiki/Anaconda_(Python_distribution) | ||
| − | En otras palabras, Anaconda | + | En otras palabras, Anaconda puede ser visto como un paquete (a distribution) que incluye no solo Python (or R) but many libraries that are used in Data Science, as well as its own virtual environment system. It's an "all-in-one" install that is extremely popular in data science and Machine Learning. |
Revision as of 00:56, 6 October 2019
Contents
Anaconda
Anaconda is a free and open source distribution of the Python and R programming languages for data science and machine learning related applications (large-scale data processing, predictive analytics, scientific computing), that aims to simplify package management and deployment. Package versions are managed by the package management system conda. https://en.wikipedia.org/wiki/Anaconda_(Python_distribution)
En otras palabras, Anaconda puede ser visto como un paquete (a distribution) que incluye no solo Python (or R) but many libraries that are used in Data Science, as well as its own virtual environment system. It's an "all-in-one" install that is extremely popular in data science and Machine Learning.
Jupyter
Jupyter comes with Anaconda.
- It is a development environment (IDE) where we can write codes; but it also allows us to display images, and write down markdown notes.
- It is the most popular IDE in data science for exploring and analyzing data.
- Other famoues IDE for Python are Sublime Text and PyCharm.
Udemy - Python for Data Science and Machine Learning Bootcamp
https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
Most popular Python Data Science Libraries
- NumPy
- SciPy
- Pandas
- Seaborn
- SciKit'Learn
- MatplotLib
- Plotly
- PySpartk
