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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

Using Streamlit for proof of skill data projects

Proving to others that you are a skilled data scientist is notoriously difficult. Anyone can put Python or machine learning on their résumé or even work in a research group at a university that might do some machine learning. But often, recruiters, professors you want to work with, and data science managers rely on things on your résumé that are proxies for competence, such as having attended the "right" university or already having a fancy data science internship or job. 

Prior to Streamlit, there were not many effective solutions to this problem. If you put a Python file or Jupyter Notebook on your GitHub profile, the time it would take for someone to understand whether the work was impressive or not was too much of a risk to take. If the recruiter has to click on the right repository in your GitHub profile and then click through numerous files until they find the Jupyter notebook with unreadable...

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