Dan Toomey - Learning Jupyter 5

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Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

Juan Toms Oliva Ramos is an environmental engineer from University of Guanajuato, Mexico, with a master's degree in administrative engineering and quality. He has more than 5 years of experience in management and development of patents, technological innovation projects, and technological solutions through the statistical control of processes. He has been a teacher of statistics, entrepreneurship, and technological development since 2011. He has developed prototypes via programming and automation technologies for the improvement of operations, which have been registered for patents.

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Learning Jupyter discusses using Jupyter to record your scripts and produce results for data analysis projects. Jupyter allows data scientists to record their complete analysis process, much in the same way that other scientists use a lab notebook for recording tests, progress, results, and conclusions. Jupyter works in a variety of operating systems, and this book covers the use of Jupyter in Windows and macOS, along with the various steps necessary to enable your specific needs. Jupyter supports a variety of scripting languages by the addition of language engines, so the user can use their particular script in a native fashion.

This book is written for readers who wants to portray software solutions to others in a natural programming context. Jupyter provides a mechanism to execute a number of different languages and stores the results directly for display, as if the user ran those scripts on their own machine.

, Introduction to Jupyter , investigates the various user interface elements available in a notebook. We will learn how to install the software on a macOS or a PC. We will expose the notebook structure. We will see the typical workflow used when developing a notebook. We will walk through the user interface operations available in a Notebook. And lastly, we will see some of the configuration options available to advanced users for their notebook.

, Jupyter Python Scripting , walks through a simple notebook and the underlying structure. Then, we will see an example of using pandas and looked at a graphics example. Finally, we will look at an example using random numbers in a Python script.

, Jupyter R Scripting , adds the ability to use R scripts in our Jupyter Notebook. We will add an R library that's not included in the standard R installation, and we will make a Hello World script in R. We will then see R data access built-in libraries and some of the simpler graphics and statistics that are automatically generated. We will use an R script to generate 3D graphics in a couple of different ways. We will then perform a standard cluster analysis (which I think is one of the basic uses of R) and use one of the forecasting tools. We will also build a prediction model and test its accuracy.