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Learn Python by Building Data Science Applications

You're reading from  Learn Python by Building Data Science Applications

Product type Book
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Pages 482 pages
Edition 1st Edition
Languages
Authors (2):
Philipp Kats Philipp Kats
Profile icon Philipp Kats
David Katz David Katz
Profile icon David Katz
View More author details

Table of Contents (26) Chapters

Preface Section 1: Getting Started with Python
Preparing the Workspace First Steps in Coding - Variables and Data Types Functions Data Structures Loops and Other Compound Statements First Script – Geocoding with Web APIs Scraping Data from the Web with Beautiful Soup 4 Simulation with Classes and Inheritance Shell, Git, Conda, and More – at Your Command Section 2: Hands-On with Data
Python for Data Applications Data Cleaning and Manipulation Data Exploration and Visualization Training a Machine Learning Model Improving Your Model – Pipelines and Experiments Section 3: Moving to Production
Packaging and Testing with Poetry and PyTest Data Pipelines with Luigi Let's Build a Dashboard Serving Models with a RESTful API Serverless API Using Chalice Best Practices and Python Performance Assessments Other Books You May Enjoy

Summary

In this chapter, we learned how to form our code into production-level data pipelines that can be scheduled and re-run on demand. Building good pipelines is an important skill, as it enables you to have the data up to date and work on your business logic (for example, parsing the information), rather than running and re-running pipeline scripts or building your own bicycle solution. This reliable and robust solution is a good way to deploy and schedule your code as a deliverable. In the later part of this chapter, we learned about the different output formats and custom templates in luigi.

In the next chapter, we'll build on top of the pipeline we set up. We will use the data we collected to build a couple of interactive dashboards, allowing us to monitor the process and analyze ongoing trends in the data.

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