Build & Deploy Apps like a (pro) Data Scientist using Streamlit

Do you ever find it complicated to learn the complexities of a traditional web framework to push your data science work online? Worry no more! Streamlit might help speed things up as it is designed for the required purpose - creating beautiful data-related web apps that can be deployed in minutes.

In the hands-on tutorial, we’ll go through various features of Streamlit and build a small lyric fetcher app based on the available curated dataset of around 24K Billboard top-100 songs.

Basic understanding of HTML, Python, and libraries such as Numpy, Matplotlib, and Pandas should be good.

0:01-0:05 minutes: In the first section, I will discuss with you the basics of Streamlit and some examples of applications made through it. I will also show you the expected final version of what we’ll create during the tutorial.

00:05 - 0:15 minutes: In the second section, I will run a small “Hello World” code on the local server, to give you the initial feel of streamlit.

00:15 - 0:50 minutes: In the third section, I will build our application step-by-step by creating a layout and adding the required elements. These elements would include two drop-down buttons for selecting the song & artist for which we want lyrics, A lyric showcasing column, and a word cloud visualization of the respective lyrics.

In the last 5 minutes, I will touch on how we can deploy the app online using Heroku and Streamlit, which you can further attempt after the talk on your own.