May 1, 2024
Updated June 25, 2025
20 minute read
Streamlit: Rapid Web App Development for Data Professionals
Streamlit is an open-source Python library that has significantly changed how data-driven web applications are created. Its primary purpose is to enable data scientists, machine learning engineers, and analysts to transform their data scripts into interactive and shareable web applications with remarkable speed and minimal code. If you've ever worked on a data project and wished for an easy way to present your findings or models to a broader audience without getting bogged down in traditional web development, Streamlit aims to be your solution.
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Find a path to becoming a Streamlit. Learn more at:
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Reading list
We've selected 26 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Streamlit.
Provides a comprehensive introduction to Streamlit, covering the fundamentals of creating interactive data applications. It's an excellent resource for beginners to gain a broad understanding of Streamlit's core features and capabilities. The book includes practical examples and covers deploying apps, making it valuable for those looking to quickly build and share data projects.
Offers a comprehensive overview of building web applications with Streamlit, progressing from introductory to advanced topics. It's suitable for those who want to deepen their understanding of Streamlit beyond basic data apps and explore areas like database integration, security, and cloud deployment. It serves as a useful reference for building more sophisticated Streamlit applications.
Provides a comprehensive guide to using Streamlit for web application development, emphasizing simplicity and efficiency. It's a great resource for gaining a broad understanding of building web apps with Streamlit, covering fundamental concepts and progressing to more advanced techniques. The book includes hands-on examples and guidance on deployment.
Focuses on the crucial aspect of deploying machine learning models, which common use case for Streamlit. It covers using Streamlit alongside other tools like Flask, Docker, and Kubernetes for putting models into production. This book is highly relevant for those looking to deploy their Streamlit-based ML applications.
As an earlier edition of 'Streamlit for Data Science', this book still offers valuable insights into the fundamentals of building data applications with Streamlit. While the second edition is more current, this book can still be a helpful resource for understanding the core concepts and getting started with Streamlit.
Many Streamlit applications are built to showcase or interact with machine learning models. very popular and practical guide to machine learning with Python. It provides the necessary background in building and understanding ML models that can be integrated into Streamlit apps.
Given the increasing integration of AI and LLMs in applications, this book covers building AI-intensive applications with Python. While not exclusively about Streamlit, the concepts and techniques for integrating AI models discussed are highly relevant for building contemporary Streamlit applications that leverage LLMs and vector databases, as seen in some of the course titles.
This handbook comprehensive reference for essential Python libraries for data science, including NumPy, Pandas, Matplotlib, and Scikit-learn. These libraries are foundational for most data applications built with Streamlit. is an excellent resource for deepening understanding of the data science ecosystem around Streamlit.
Given the mention of NLP and LLMs in the course titles, this book provides a strong foundation in text analysis using Python. The techniques and concepts covered are directly applicable to building Streamlit applications that involve processing and analyzing text data or integrating with language models.
Streamlit is often used for creating interactive data visualizations. delves into various Python libraries for data visualization, such as Matplotlib, Bokeh, and Plotly. Understanding these libraries will significantly enhance the ability to create compelling visuals within Streamlit applications, making it a valuable supplementary read.
Streamlit for Data Science shows you how to use Streamlit to build interactive dashboards and data visualizations for your data science projects.
While not solely focused on Streamlit, this book classic for anyone working with data in Python. It provides a strong foundation in data manipulation and analysis using libraries like Pandas and NumPy, which are essential prerequisites for building data-driven Streamlit applications. It's a valuable reference for data handling techniques often used in Streamlit apps.
Provides an overview of data visualization in both R and Python, covering key libraries like Seaborn and Plotly. For Streamlit developers, the Python visualization chapters are particularly relevant for creating compelling charts and graphs within their applications. It can serve as a good reference for visualization techniques.
For those looking to build Streamlit applications that involve deep learning models, this book comprehensive theoretical reference. While challenging, it provides a deep understanding of the underlying concepts of deep learning, which is valuable for developing more complex AI applications with Streamlit.
To write efficient and maintainable Streamlit code, a strong understanding of intermediate to advanced Python concepts is beneficial. dives deep into Python's features and best practices, making it an excellent resource for experienced Python developers looking to deepen their language skills for more complex Streamlit projects.
Creating effective Streamlit applications often involves telling a story with data through visualizations. focuses on the principles of data storytelling and creating visualizations that resonate with an audience. It's a valuable read for improving the communication aspect of Streamlit data apps.
While not tied to a specific programming language, this book covers the principles of effective data visualization. Understanding these principles is crucial for creating impactful visualizations in Streamlit apps. It's a valuable resource for anyone serious about presenting data clearly and effectively.
This classic book provides a broad introduction to NLP concepts and techniques using Python and the NLTK library. It's a good resource for gaining foundational knowledge in NLP, which is relevant for Streamlit applications dealing with text and language, as highlighted by some course titles.
This widely recommended book for learning the fundamentals of Python programming. A solid understanding of Python necessary prerequisite for learning Streamlit. provides a project-based approach that helps build a strong programming foundation.
Builds data science tools and algorithms from the ground up using Python. While it doesn't cover Streamlit, the foundational understanding of data science concepts and the ability to implement them in Python is highly valuable for building robust Streamlit data applications. It provides a deeper understanding of the underlying mechanics.
Focuses on integrating AI, particularly LLMs, into web applications using JavaScript frameworks. While Streamlit is Python-based, this book provides valuable context on the types of AI applications being built on the web and the integration patterns, which can inspire and inform the development of AI-powered Streamlit apps.
Offers a beginner-friendly introduction to using Python for data science. It covers essential libraries and concepts in a straightforward manner, making it a good starting point for those new to both Python and data science before diving into Streamlit specifics.
Streamlit for Machine Learning teaches you how to use Streamlit to build interactive machine learning models and applications.
Another excellent book for Python beginners, this resource focuses on using Python for practical tasks. While not directly about Streamlit, the programming skills and problem-solving approaches learned from this book are highly transferable and beneficial for anyone starting with Streamlit.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/jannpq/streamli
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