Complete Guide to Data Science Applications with Streamlit

Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether.

This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don’t need any experience in building front-end applications for this. Here are some of the things you can expect to cover in this course:

• Python Crash Course

• NumPy Crash Course

• Introduction to Streamlit

• Integrating Matplotlit and Seaborn in Streamlit

• Using Altair and Vega-Lite in Streamlit

• Understand all Streamlit Widgets

• Upload and Process Files

• Build an Image Processing Application

• Develop a Natural Language Processing Application

• Integrate Maps with Streamlit

• Implement Plotly Graphs

• Authenticate Your Applications

• Laying Out your Application in Streamlit

• Developing with Streamlit Components

• Deploying Data Applications

Why Streamlit

There are several other libraries that can be used for building data applications. That said, why should you consider Streamlit:

• No front-end experienced required

• Write everything in what you already know — Python

• Easy to weave in interaction with widgets such as sliders

• Quick and easy to deploy

• Compatible with most data science frameworks

No front-end experienced required

If you were to build a data app with Flask and or Django, then knowledge in front-end tools such as HTML & CSS as well as Javascript is a must. However, in Streamlit, all this is done using Streamlit widgets. For example, a drop-down can easily be achieved using the selectbox widget. Other HTML tags such as input boxes and buttons are also achieved using simple Streamlit widgets.

Python Scripting

When building data applications in Streamlit, you never leave your Python editor. This is because is scripted in Python. It is, therefore, very advantageous since you keep working in a language that you are already familiar with. If this was done in other Python frameworks, then writing HTML, CSS, and Javascript code would be unavoidable.

Interactivity

Adding interaction to Streamlit applications is very simple. Streamlit provides widgets that one can use to weave interactivity to your application. For example, one can use the date input widget to filter their data. Select boxes and sliders can also be used to achieve the same.

Deployment

Sharing Streamlit applications is very easy. One can easily deploy to the likes of Heroku and AWS. However, one can also deploy their app on Streamlit Sharing by the click of just two buttons. All you have to do is to request access. Your Github email address will then be linked to Streamlit Sharing. Once this is done, you can deploy any Streamlit project available on your Github account.

Compatibility

Streamlit is compatible with the most popular data science libraries. For example, you can perform visualizations in Streamlit with the tools that you are already used to. The visualizations libraries supported include:

• Matplotlib

• Seaborn

• Altair

• Plotly

• Bokeh

You definitely need to perform data cleaning and wrangling before visualizing your results. Pandas and NumPy are supported so that you can achieve this.

When it comes to machine learning, you can deploy models built with the popular libraries that you are already used to. This is because Keras, TensorFlow, and PyTorch are supported out-of-the-box.

Streamlit Components

In the event that you need a functionality that is not supported by Streamlit the first place to look is the Streanmlit Components page. Streamlit Components are third-party functionalities that have been built by the community. The components can be installed via pip and used immediately in your project.

Streamlit Components

The beauty of it is that you can also write your own components and share them with the community.

At the end of the course, you will have built several applications that you can include in your data science portfolio. You will also have a new skill to add to your resume.

The course also comes with a 30-day money-backguarantee. Enroll now and if you don’t like it you will get your money back no questions asked.

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!