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Jupyter for Data Science

You're reading from Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

Product type Paperback

Published in Oct 2017

Publisher Packt

ISBN-13 9781785880070

Length 242 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

Dan Toomey

View More author details

Table of Contents (11) Chapters

Preface

1. Jupyter and Data Science FREE CHAPTER

2. Working with Analytical Data on Jupyter

3. Data Visualization and Prediction

4. Data Mining and SQL Queries

5. R with Jupyter

6. Data Wrangling

7. Jupyter Dashboards

8. Statistical Modeling

9. Machine Learning Using Jupyter

10. Optimizing Jupyter Notebooks

Versioning a notebook

A common practice in the programming world is to maintain a history of the changes made to a program. Over time the different versions of the program are maintained in a software repository where the programmer can retrieve prior versions to return to an older, working state of their program.

In the previous section we mentioned placing your notebook on GitHub. Git is a software repository in wide use. GitHub is an internet-based instance of Git. Once you have any software in Git it will automatically be versioned. The next time you update your notebook in GitHub. Git will take the current instance, store it as a version in your history, and place the new instance as the current—where anyone accessing your GitHub repository will see the latest version by default.

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Authors (1)

Toomey

Toomey

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.

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