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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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Toc

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

Interview #1 – Fanilo Andrianasolo

(Tyler) Hey, Fanilo! Before we get started, do you want to introduce yourself to the readers? What's your background? What do you spend your time doing, and who do you work for?

(Fanilo) Hello, everybody! My name is Fanilo Andrianasolo, I'm from Madagascar, and I work at Worldline, which is one of the leading European companies in digital payments and transactional services. I work there as a data science and business intelligence advocate and tech lead, where I help internal product and development teams to prototype new data science use cases, architect those use cases, and then put them into production. So, most of my work is focused on integrating data analytics inside Worldline, which is a huge task because it covers multiple industries from finance, for example, fraud detection, retail, and customer analysis. And I'm also a data science advocate, so I build and present multiple talks internally or to prospective customers...

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