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Streamlit

Learn to build and deploy interactive data apps with Streamlit—turn your Python scripts into powerful, shareable web applications.
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Course Duration: 10 Hours
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Streamlit: Data App Development with Python is a hands-on, self-paced course designed for data scientists, Python developers, and analysts who want to create interactive dashboards and applications without needing deep web development skills. This course guides you step by step from building simple apps to deploying advanced data-driven applications.

At its core, Streamlit is a Python-based open-source framework that allows developers to quickly turn Python scripts into fully functional web apps. It eliminates the complexity of front-end development by providing simple APIs for UI elements, charts, and interactive features.

This course starts with the fundamentals of Streamlit, then progresses into integrating machine learning models, working with real datasets, and deploying applications to the cloud. By the end, you’ll be able to build and share professional-grade data apps in just a few lines of Python code.

What You Will Gain

  • Build interactive dashboards and apps with Streamlit

  • Use Python for visualizations, widgets, and forms

  • Integrate machine learning models into live apps

  • Connect apps to APIs and databases

  • Deploy Streamlit apps to the cloud for sharing

Who This Course Is For

  • Data Scientists who want to showcase models with interactive apps

  • Python Developers building lightweight web apps

  • Business Analysts creating dashboards without web coding

  • Students & Researchers presenting data projects interactively

  • Startups & Entrepreneurs prototyping data-driven tools quickly

Course Objectives Back to Top

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

  • Install and set up Streamlit in Python

  • Build dashboards with charts, maps, and interactive widgets

  • Create forms and filters for user input

  • Deploy apps on Streamlit Cloud, Heroku, or AWS

  • Share real-time interactive data apps with stakeholders

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Streamlit
Module 2: Setting Up Your First App
Module 3: Layouts, Widgets & Interactivity
Module 4: Data Visualization with Python (Matplotlib, Plotly, Altair)
Module 5: Connecting APIs & Databases
Module 6: Deploying Machine Learning Models with Streamlit
Module 7: Real-World Projects (Stock Dashboard, Sentiment Analyzer, Sales Tracker)
Module 8: Deployment on Cloud Platforms
Module 9: Interview Questions & Answers

Certification Back to Top

Upon completion, learners will receive a Certificate of Completion from Uplatz validating their expertise in data app development with Streamlit.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • Data Scientist

  • Machine Learning Engineer

  • Business Intelligence Analyst

  • Python Developer

  • Research Analyst

Interview Questions Back to Top

  1. What is Streamlit and why is it popular among data scientists?
    Answer: Streamlit is an open-source Python library that allows fast creation of interactive apps without front-end coding, making it popular for data science presentations.

  2. How do you run a Streamlit app?
    Answer: By saving a Python script with Streamlit code and running streamlit run filename.py in the terminal.

  3. Can Streamlit be used for machine learning apps?
    Answer: Yes, it can integrate ML models and allow users to interact with predictions through input widgets.

  4. What are Streamlit widgets?
    Answer: Interactive UI elements such as sliders, text inputs, checkboxes, and dropdowns that allow user interaction.

  5. How does Streamlit handle data visualization?
    Answer: It supports libraries like Matplotlib, Plotly, Altair, and Seaborn to create interactive charts.

  6. Can Streamlit apps be deployed to the cloud?
    Answer: Yes, via Streamlit Cloud, Heroku, AWS, GCP, or Docker.

  7. What are the advantages of Streamlit over Flask or Django?
    Answer: Streamlit is faster for prototyping data apps, while Flask/Django are more robust for full web development.

  8. What is caching in Streamlit and why is it important?
    Answer: Caching (st.cache_data) stores computation results to speed up app performance by avoiding redundant processing.

  9. What are common limitations of Streamlit?
    Answer: Limited support for complex front-end customization and high scalability compared to traditional frameworks.

  10. How would you use Streamlit in a business setting?
    Answer: To build dashboards for sales tracking, customer insights, or to demonstrate machine learning models to stakeholders.

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