Scientific Computation and Data Analysis

Docklet supports scientific computation and data analysis mainly by two languages Python and R, as well as plentiful packages. Most work can be done in Jupyter Notebook, including data visualization, with only a browser.

There are many articles in the WEB about how to use Pyton and R do big data processing, scientific computing, machine learning and deep learning, natural language processing, statistics, data mining, graph processing, data visualization, etc. In these articles, what is valuable to novice users is what packages can be used in what kind of tasks.

ANACONDA from CONTINUUM is a computation and analysis platform. It integrates both useful open source Python packages and R packages.

The Canopy from ENTHOUGHT is a platform similar to CONTINUUM. It also integrates many useful Python packages for computation and analysis.

RStudio introduces RStudio IDE and several useful R packages.

Though many visualization and IDE packages need to run in local OS, most packages in Docklet can run in Jupyter Notebook as Web app. Users can use pip or other tools to install those packages needed.

The list of Python and R packages in Docklet:

About the tutorials and examples about using Jupyter Notebook in activities like scientific computation, please referer nbview and ipython wiki.

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