We may earn an affiliate commission when you visit our partners.
Snehan Kekre
Welcome to this hands-on project on building your first data science web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
Read more
Welcome to this hands-on project on building your first data science web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
Read more
Welcome to this hands-on project on building your first data science web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
Prior experience with writing simple Python scripts and using pandas for data manipulation is recommended.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/08cqao/build
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Build a Data Science Web App with Streamlit and Python
Welcome to this hands-on project on building your first data science web app with the Streamlit library in Python. By the end of this project, you are going to get comfortable with using Python and Streamlit to build beautiful and interactive web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces commonly-used Python library Streamlit for the creation of data science web applications
Save this course
Save Build a Data Science Web App with Streamlit and Python to your list so you can find it easily later:
Save
Save
Reviews summary
Beginner-friendly streamlit crash course
Learners say this beginner-friendly course provides a hugely positive introduction to Streamlit. With very little code, you'll be able to build interactive web apps using Python, Pandas, and Numpy. The course uses the Rhyme platform, which allows you to run code in the cloud. While Rhyme is convenient, some learners complain about its lag and connectivity issues. Despite this, many learners recommend this course for its clear explanations, engaging instructor, and practical project.
Clear, engaging, knowledgeable
"The instructor has provided all the information needed and had an engaging informative session and extremely happy that I enrolled for this guided project."
"The instructor has done a great job explaining things."
"The instructor is pretty clear and goes straight to the point. You can see that he knows the subject and gives enough explanation without going too deep on unnecesary details."
Provides a practical project to apply Streamlit skills
"Excellent project and hands on course to get started with streamlit and create a beautiful interactive web app."
"Hands-on Experience is always the best way to build the project."
"The project is explained clear and a nice workout."
Suitable for those with little to no coding experience
"Excellent course for beginners as well as experienced researchers from other fields"
"Great for an explanatory walkthrough into the Streamlit library and its uses in Python."
"Very straight to the point which I appreciated."
Some learners express concerns about code quality
"You can learn a lot on this, but mostly from fixing the bad code and bad practice."
"don't waste your moneygo work through the examples in the streamlit documentation instead"
Convenient, but laggy and prone to connectivity issues
"But Rhyme ( the VM software) was just too slow and laggy."
"Rhyme platform for teaching, but seriously from now onwards I hate this Rhyme"
"Such a creep place where you will stick without any reason, and there is no place for your help"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Build a Data Science Web App with Streamlit and Python with these
activities:
Read 'Introduction to Data Science in Python'
Show steps
Expand your knowledge of data science and Python by reading this introductory book.
View
Working with Network Data
on Amazon
Show steps
-
Read the book and take notes.
-
Complete the exercises and projects in the book.
Review basic Python
Show steps
Review the fundamentals of Python to ensure you have a strong foundation for this course.
Show steps
-
Review Python syntax and data structures.
-
Solve simple Python coding exercises.
Explore the Streamlit documentation
Show steps
Become familiar with the Streamlit library by exploring its documentation and examples.
Show steps
-
Visit the official Streamlit documentation.
-
Review the Streamlit gallery of examples.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve Streamlit coding challenges
Show steps
Reinforce your understanding of Streamlit by solving coding challenges and building simple web apps.
Show steps
-
Find Streamlit coding challenges online.
-
Create a simple Streamlit web app to visualize a dataset.
Explore Streamlit's documentation and tutorials
Show steps
Streamlit's documentation and tutorials will provide you with a solid foundation in using the library and building web apps with Python.
Show steps
-
Visit the Streamlit documentation website
-
Read through the Streamlit tutorial
-
Follow along with the examples provided in the tutorial
Answer questions on a Streamlit forum
Show steps
Answering questions on a Streamlit forum will help you solidify your understanding of the library and help others learn.
Show steps
-
Join a Streamlit forum
-
Answer questions posted by other users
Join a Streamlit study group
Show steps
Connect with other learners and share knowledge by joining a Streamlit study group.
Show steps
-
Find a Streamlit study group online or in your local area.
-
Participate in group discussions and share your experiences.
Build a simple data visualization web app
Show steps
Building a simple data visualization web app will help you apply the concepts you learn in the course and reinforce your understanding of Streamlit.
Show steps
-
Choose a dataset to visualize
-
Create a Streamlit app to visualize the data
-
Deploy your app online
Participate in a Streamlit hackathon
Show steps
Participating in a Streamlit hackathon will give you the opportunity to work on a project with others and showcase your skills.
Show steps
-
Find a Streamlit hackathon to participate in
-
Form a team or work on your own
-
Develop a Streamlit app
-
Submit your app to the hackathon
Build a Streamlit dashboard
Show steps
Demonstrate your proficiency in Streamlit by building a dashboard that visualizes and interacts with data.
Show steps
-
Choose a dataset to visualize.
-
Create a Streamlit dashboard to display the data.
-
Deploy the dashboard to a hosting platform.
Develop a data science web app for a specific use case
Show steps
Developing a data science web app for a specific use case will challenge you to apply your skills and knowledge to a real-world problem.
Show steps
-
Identify a specific use case for your web app
-
Design and develop the app
-
Deploy your app online
-
Share your app with others
Contribute to the Streamlit community
Show steps
Deepen your understanding of Streamlit by contributing to its open-source codebase.
Show steps
-
Find a Streamlit project on GitHub.
-
Submit a bug report or feature request.
-
Contribute code to the project.
Read 'Introduction to Data Science in Python'
Show steps
Expand your knowledge of data science and Python by reading this introductory book.
View
Working with Network Data
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes.
- Complete the exercises and projects in the book.
Review basic Python
Show steps
Review the fundamentals of Python to ensure you have a strong foundation for this course.
Show steps
Show steps
Show steps
- Review Python syntax and data structures.
- Solve simple Python coding exercises.
Explore the Streamlit documentation
Show steps
Become familiar with the Streamlit library by exploring its documentation and examples.
Show steps
Show steps
Show steps
- Visit the official Streamlit documentation.
- Review the Streamlit gallery of examples.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve Streamlit coding challenges
Show steps
Reinforce your understanding of Streamlit by solving coding challenges and building simple web apps.
Show steps
Show steps
Show steps
- Find Streamlit coding challenges online.
- Create a simple Streamlit web app to visualize a dataset.
Explore Streamlit's documentation and tutorials
Show steps
Streamlit's documentation and tutorials will provide you with a solid foundation in using the library and building web apps with Python.
Show steps
Show steps
Show steps
- Visit the Streamlit documentation website
- Read through the Streamlit tutorial
- Follow along with the examples provided in the tutorial
Answer questions on a Streamlit forum
Show steps
Answering questions on a Streamlit forum will help you solidify your understanding of the library and help others learn.
Show steps
Show steps
Show steps
- Join a Streamlit forum
- Answer questions posted by other users
Join a Streamlit study group
Show steps
Connect with other learners and share knowledge by joining a Streamlit study group.
Show steps
Show steps
Show steps
- Find a Streamlit study group online or in your local area.
- Participate in group discussions and share your experiences.
Build a simple data visualization web app
Show steps
Building a simple data visualization web app will help you apply the concepts you learn in the course and reinforce your understanding of Streamlit.
Show steps
Show steps
Show steps
- Choose a dataset to visualize
- Create a Streamlit app to visualize the data
- Deploy your app online
Participate in a Streamlit hackathon
Show steps
Participating in a Streamlit hackathon will give you the opportunity to work on a project with others and showcase your skills.
Show steps
Show steps
Show steps
- Find a Streamlit hackathon to participate in
- Form a team or work on your own
- Develop a Streamlit app
- Submit your app to the hackathon
Build a Streamlit dashboard
Show steps
Demonstrate your proficiency in Streamlit by building a dashboard that visualizes and interacts with data.
Show steps
Show steps
Show steps
- Choose a dataset to visualize.
- Create a Streamlit dashboard to display the data.
- Deploy the dashboard to a hosting platform.
Develop a data science web app for a specific use case
Show steps
Developing a data science web app for a specific use case will challenge you to apply your skills and knowledge to a real-world problem.
Show steps
Show steps
Show steps
- Identify a specific use case for your web app
- Design and develop the app
- Deploy your app online
- Share your app with others
Contribute to the Streamlit community
Show steps
Deepen your understanding of Streamlit by contributing to its open-source codebase.
Show steps
Show steps
Show steps
- Find a Streamlit project on GitHub.
- Submit a bug report or feature request.
- Contribute code to the project.
Career center
Learners who complete Build a Data Science Web App with Streamlit and Python will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visualizations that communicate data insights to a variety of audiences. This course on building web apps with Streamlit may be useful to Data Visualization Specialists, as it can help them to create more interactive and engaging visualizations.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models, which can be used to automate tasks, make predictions, and provide insights from data. This course on building web apps with Streamlit can be useful to Machine Learning Engineers, as they may use these skills to create interactive interfaces for their models, allowing users to easily interact with and experiment with them.
UX Designer
UX Designer
UX Designers are responsible for the design of user interfaces for websites and applications. This course on building web apps with Streamlit may be useful to UX Designers, as it can help them to create more interactive and engaging user interfaces.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course on building web apps with Streamlit may be useful to Software Engineers, as it can help them to build more interactive and engaging user interfaces for their software applications.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. This course on building web apps with Streamlit may be useful to Web Developers, as it can help them to build more interactive and engaging web applications.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and features. This course on building web apps with Streamlit may be useful to Product Managers, as it can help them to create interactive prototypes and demos of their products, and to get feedback from users.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns, and to provide insights that can help businesses make better decisions. This course on building web apps with Streamlit can be useful to Data Analysts, as they may use these skills to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that is used to store and process data, and they develop the tools and processes that are used to manage and analyze data. This course on building web apps with Streamlit may be useful to Data Engineers, as they may use these skills to create interactive interfaces for their data management and analysis tools.
Business Analyst
Business Analyst
Business Analysts use data to identify business problems and opportunities, and to develop solutions that can help businesses improve their performance. This course on building web apps with Streamlit may be useful to Business Analysts, as they may use these skills to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses and organizations. This course on building web apps with Streamlit may be useful to Risk Analysts, as it can help them to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
Data Scientist
Data Scientist
Data Scientists build and maintain comprehensive databases, and use them for advanced statistical modeling and analysis in order to support the decision-making of their employer or clients. Those working in this career role may use Streamlit to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights. The skills and knowledge learned in this course may therefore be useful to a Data Scientist.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course on building web apps with Streamlit may be useful to Statisticians, as it can help them to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. This course on building web apps with Streamlit may be useful to Operations Research Analysts, as it can help them to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course on building web apps with Streamlit may be useful to Quantitative Analysts, as it can help them to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations and provide advice to businesses and individuals. This course on building web apps with Streamlit may be useful to Financial Analysts, as it can help them to create interactive visualizations and dashboards, making it easy to explore and interact with data, and generate insights.
For more career information including salaries, visit:
OpenCourser.com/course/08cqao/build
Reading list
We've selected 12 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
Build a Data Science Web App with Streamlit and Python.
Provides a comprehensive overview of the techniques and tools used to create interactive data visualizations for the web. It covers everything from data preparation and cleaning to creating visualizations and dashboards using popular libraries such as D3.js and Plotly.
Provides a comprehensive overview of the Python programming language and its کاربرد in data analysis. It covers everything from data manipulation and cleaning to data visualization and machine learning.
Provides a hands-on introduction to data science using Python. It covers everything from data cleaning and wrangling to machine learning and data visualization.
Provides a comprehensive overview of natural language processing using Python. It covers everything from the basics of NLP to more advanced topics such as machine translation and text classification.
Provides a comprehensive overview of deep learning using Python. It covers everything from the basics of deep learning to more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of TensorFlow, a popular open-source machine learning library. It covers everything from the basics of TensorFlow to more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of scikit-learn, a popular open-source machine learning library for Python. It covers everything from the basics of scikit-learn to more advanced topics such as model selection and hyperparameter tuning.
Provides a comprehensive overview of the Python programming language and its کاربرد in data science. It covers everything from data manipulation and cleaning to data visualization and machine learning.
Provides a comprehensive overview of machine learning using Python. It covers everything from the basics of machine learning to more advanced topics such as deep learning.
Save
Provides a gentle introduction to machine learning. It covers everything from the basics of machine learning to more advanced topics such as deep learning.
Save
Provides a comprehensive overview of deep learning. It covers everything from the basics of deep learning to more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of Keras, a popular high-level neural networks API, written in Python. It covers everything from the basics of Keras to more advanced topics such as convolutional neural networks and recurrent neural networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/08cqao/build
Share
Similar courses
Here are nine courses similar to
Build a Data Science Web App with Streamlit and Python.
Create Interactive Dashboards with Streamlit and Python
OpenCourser.com/course/9wfjqa/create
Create Interactive Dashboards with Streamlit and Python
Most relevant
Build a Machine Learning Web App with Streamlit and Python
OpenCourser.com/course/v51c57/build
Build a Machine Learning Web App with Streamlit and Python
Most relevant
Data Visualization with ChatGPT: Python for Dashboarding
OpenCourser.com/course/cfetk4/data
Data Visualization with ChatGPT: Python for Dashboarding
Most relevant
Build Web Apps in Python with Streamlit 0.8
OpenCourser.com/course/3z70oj/build
Build Web Apps in Python with Streamlit 0.8
Most relevant
Deploying a Python Data Analytics web app on Heroku
OpenCourser.com/course/p4pcfa/deploying
Deploying a Python Data Analytics web app on Heroku
Most relevant
Create digit recognition web app with Streamlit
OpenCourser.com/course/vcdrq8/create
Create digit recognition web app with Streamlit
Most relevant
GenAI Summarization with Langchain: Summarize Text Documents
OpenCourser.com/course/co3l8p/genai
GenAI Summarization with Langchain: Summarize Text...
Most relevant
Deploy A Microsoft Azure Speech To Text Web App
OpenCourser.com/course/rv5nzk/deploy
Deploy A Microsoft Azure Speech To Text Web App
Most relevant
Neural Network Visualizer Web App with Python
OpenCourser.com/course/uwpyx8/neural
Neural Network Visualizer Web App with Python
Most relevant
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Introduces commonly-used Python library Streamlit for the creation of data science web applications
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser