Data analysis and analytics. Uncover insights and transform your organisation.

This comprehensive short course is designed to introduce you to Streamlit, a powerful and user-friendly Python library for creating interactive, data-focused web applications. Streamlit is particularly beneficial for data analysts, scientists, machine learning engineers, and Python users who aspire to quickly and effortlessly build and share data-driven applications without the need for extensive web development skills. Throughout this course, you will gain hands-on experience and practical knowledge, enabling you to leverage Streamlit’s capabilities to their fullest potential. 

Aims

The aim of this course is to enhance your skills in creating custom, interactive data applications using the Streamlit package in Python. By the end of the day, you will be able to effortlessly develop powerful data products that your clients will find highly valuable. 

Outcomes

By the end of this course, you should be able to:

• understand the fundamentals of Streamlit and its applications
• install and set up Streamlit in a Python environment
• create interactive web applications using Streamlit
• store information in Streamlit Session States
• integrate Streamlit with popular Python libraries such as Pandas and Matplotlib
• deploy and share Streamlit applications.

Content

• Benefits of using Streamlit for data applications
• Installing Streamlit
• Basic Streamlit commands and structure
• Creating and displaying widgets
• Handling user inputs and interactions
• Displaying data and visualisations
• Storing information in Session States
• Using Pandas for data manipulation
• Creating visualisations with Matplotlib
• Integrating machine learning models
• Best practices for deploying applications
• Sharing applications with others

Intended audience

This course is ideal for data scientists, machine learning engineers, and Python developers who want to enhance their skills in building interactive web applications. No prior web development experience is required.

Prerequisites

It is assumed you have completed the Python Programming 1B course or have equivalent knowledge of Python and Pandas programming.

Delivery style

This course is an interactive workshop that includes lectures, individual exercises and discussion.

Delivery mode

• Face-to-face, presenter-taught training using your own device
• Online training via the platform Zoom

Materials

All course materials are provided electronically (via Dropbox). Printing services are not provided.

Materials

Please download and install Visual Studio Code (or your preferred Python IDE like PyCharm or Spyder) before class. A Python extension is available through the VS Code interface. No separate Python download is required.

Other required Python packages will be downloaded and installed during class.

Recommended reading

Khorasani, M.  Abdou, M., & Fernandez, J. (2024) Web Application Development with Streamlit: Develop and Deploy Secure and Scalable Web Applications to the Cloud Using a Pure Python Framework. Apress.

Richards, T. (2021). Getting Started with Streamlit for Data Science. Pakt publishing.

Richards, T. (2023). Streamlit for Data Science - Second Edition: Create interactive data apps in Python. Packt Publishing.