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Getting Started with Streamlit for Data Science

You're reading from   Getting Started with Streamlit for Data Science Create and deploy Streamlit web applications from scratch in Python

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800565500
Length 282 pages
Edition 1st Edition
Languages
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Author (1):
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Tyler Richards Tyler Richards
Author Profile Icon Tyler Richards
Tyler Richards
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Creating Basic Streamlit Applications
2. Chapter 1: An Introduction to Streamlit FREE CHAPTER 3. Chapter 2: Uploading, Downloading, and Manipulating Data 4. Chapter 3: Data Visualization 5. Chapter 4: Using Machine Learning with Streamlit 6. Chapter 5: Deploying Streamlit with Streamlit Sharing 7. Section 2: Advanced Streamlit Applications
8. Chapter 6: Beautifying Streamlit Apps 9. Chapter 7: Exploring Streamlit Components 10. Chapter 8: Deploying Streamlit Apps with Heroku and AWS 11. Section 3: Streamlit Use Cases
12. Chapter 9: Improving Job Applications with Streamlit 13. Chapter 10: The Data Project – Prototyping Projects in Streamlit 14. Chapter 11: Using Streamlit for Teams 15. Chapter 12: Streamlit Power Users 16. Other Books You May Enjoy

Chapter 10: The Data Project – Prototyping Projects in Streamlit

In the previous chapter, we discussed how to create Streamlit applications that are specific to job applications. Another fun application of Streamlit is to try out new and interesting data science ideas and create interactive apps for others. Some examples of this include applying a new machine learning model to an existing dataset, carrying out an analysis on some data uploaded by users, or creating an interactive analysis on a private dataset. There are numerous reasons for making a project like this, such as personal education or community contribution.

In terms of personal education, often, the best way to learn a new topic is to observe how it actually works by applying it to the world around you or a dataset that you know closely. For instance, if you are trying to learn how Principal Component Analysis works, you can always learn about it in a textbook or watch someone else apply it to a dataset. However...

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