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Streamlit Dashboards

Published by O'Reilly Media, Inc.

Beginner to intermediate content levelBeginner to intermediate

Easy dashboarding in Python

This live event utilizes interactive environments

Dashboarding is crucial in any organization to help data scientists and ML engineers make decisions, analyze data, and report results of projects to the business. Streamlit is a popular open source dashboarding framework that allows you to turn data scripts into shareable web apps in Python in minutes. You don’t need any other languages, and you don’t need any frontend experience to work with this dashboard.

Join Stijn Van Hijfte to learn how you can apply your Python skill to make the move toward a beautiful report and dashboard with Streamlit. You’ll get hands-on with tasks such as integrating libraries and visualizing your entire data story to make your machine learning projects and analysis more understandable for nontechnical professionals.

Hands-on learning with interactive labs

All exercises are provided as O'Reilly interactive labs—complete development environments that are preconfigured with everything you need. There's nothing to install or configure; just click a link and get started!

Interactive labs are sandboxed, so you can explore, experiment, and tinker without fear of breaking anything. And you can revisit them anytime after class ends to practice and refine your skills.

What you’ll learn and how you can apply it

By the end of this live online course, you’ll understand:

  • The importance of dashboarding and how Streamlit can meet your needs
  • How you can communicate data analysis between data scientists and business users
  • How Streamlit works
  • How to use Streamlit for standard dashboarding, combining the dashboard with machine learning, data discovery and preprocessing, and model discovery
  • How to use Streamlit in NLP, scientific, computer vision, and finance use cases
  • How to combine industry expertise with data analysis and dashboarding
  • How to combine machine learning and Streamlit to clearly visualize your work for learners, business professionals, or yourself

And you’ll be able to:

  • Use Streamlit to create your own dashboards

This live event is for you because...

  • You’re a data scientist or a machine or deep learning engineer who is interested in learning Streamlit.
  • You’re an IT student looking to apply dashboarding.
  • You’re responsible for reporting within your organization.

Prerequisites

  • Basic Python skills
  • Basic ML and DL skills

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Introduction (10 minutes)

  • Presentation: What is Streamlit and how can you use it to help solve data science problems?; What are the main challenges data scientists face?

Importance of dashboarding (25 minutes)

  • Presentation: What solutions are there?; What can you do in Python?; the importance of dashboarding in the organization; Why use Streamlit?
  • Q&A

Streamlit introduction (35 minutes)

  • Presentation and demos: Building simple data apps; deploying the different features; best practices; applying the library on a simple use case; demonstrating the results (a first ML integration)
  • Interactive scenario: Create a simple dashboard with a provided dataset
  • Q&A
  • Break

Streamlit next steps (35 minutes)

  • Presentation: How to connect to external data sources; applying different libraries within the dashboard (Plotly, Matplotlib); adding machine learning capabilities
  • Q&A
  • Break

Some more complex use cases (65 minutes)

  • Presentation and demos: Simplifying and explaining a machine learning project to business users; use case in NLP; creating a finance dashboard; demonstrating the results; creating a geospatial data dashboard
  • Interactive scenarios: Create a Social Data Dashboard

Wrap-up and Q&A (10 minutes)

Your Instructor

  • Stijn Van Hijfte

    Stijn Van Hijfte has a background in economics, artificial intelligence, and cybersecurity. He has worked for years as a lecturer at Howest Applied University College and as a consultant in the field of automation, AI, and IT.

Skill covered

Dashboards