Streamlit data applications for dynamic data visualization

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Jesús López

48:03

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1. Preparation

1. Materials.html

2. Data Visualization

1. Visualize dataset in a chart.mp4

05:05

2. 2 Create Streamlit application.mp4

03:16

3. 3 Publish app in Streamlit Share referencing GitHub repository.mp4

03:15

3. Data Filtering

1. 1 Backend to handle user input.mp4

02:19

2. 2 Frontend to ask for user input.mp4

06:21

4. Time Series Data Aggregation

1. 1 ETL Preprocess raw data to add temporal features.mp4

06:34

2. 2 Data aggregation based on temporal columns.mp4

06:11

3. 3 Refactor code to simplify development.mp4

03:10

4. 4 Adapt notebook to Streamlit web application.mp4

11:52

Description

Transform data visualizations from a Jupyter Notebook into a dynamic web application using Python with Streamlit.

What You'll Learn?

Create Streamlit applications using Python
Sketch and script back and front end code
Refactor code for optimal development
Aggregate time series data for dashboard reporting

Who is this for?

Professionals to automate their data reports programmatically with Python.

Students to learn with hands-on exercises and practical projects.

Job seekers looking to add end-to-end projects to their portfolio.

Data enthusiast who'd like to take a step further by developing data applications to showcase their findings, instead of leaving the visualizations in a notebook.

What You Need to Know?

You'll get the solutions to learn comfortably on your own.

We will explain everything step by step, starting from blank notebooks.

No prior experience is required. However, if you already know Python or data analysis, you will be more comfortable.

More details

Description
This course teaches you how to convert a Jupyter Notebook into a fully functional Streamlit web application. Starting with existing code, you'll see how to organize and migrate it into the Streamlit environment. Youâ€™ll learn to handle data, create interactive visualizations, and develop user-friendly interfaces.

We'll show you step-by-step how to:

1. Set up and run a Streamlit application.
2. Integrate data selection widgets for dynamic updates.
3. Use Plotly for data visualization.
4. Implement multi-select options for data granularity.
5. Style your DataFrames for better visual appeal.
6. Debug common issues like data path errors and improper widget setup.

By the end of this course, you can create robust web applications to explore and present your data, which you can share with colleagues, friends, or customers. You'll gain practical experience to enhance your portfolio, making you a strong candidate for data-driven roles. Join us to transform your data analysis workflow with Streamlit!

Sign up today and elevate your data presentation skills! With our comprehensive guidance, youâ€™ll become proficient in building interactive web applications. This course covers everything you need to know, from setting up your first Streamlit app to mastering advanced visualization techniques. You'll also learn to troubleshoot common problems, ensuring a smooth development process. Our step-by-step approach ensures that even complex concepts are easy to grasp. By the end of the course, youâ€™ll have a powerful, shareable web application and the skills to continue building more. Don't miss this opportunity to advance your data science career!
Who this course is for:
Professionals to automate their data reports programmatically with Python.
Students to learn with hands-on exercises and practical projects.
Job seekers looking to add end-to-end projects to their portfolio.
Data enthusiast who'd like to take a step further by developing data applications to showcase their findings, instead of leaving the visualizations in a notebook.

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Data Visualization

Jesús López

Instructor's Courses

Jesus' curriculum showcases a diverse range of experiences and expertise.His more than 8,000 training hours come from various industries, such as energy, finance, telecommunications, and healthcare. Specifically, some of his notable clients work in Santander, PEPSI, Vodafone, University of Oxford, Hospital Ramón y Cajal, Bankinter, BBVA, Banco de España, IGNIS, Cogen, Telefonica, Galp, and REE.He has trained on data and programming skills more 800 clients 1:1. For whom he developed the analysis of their data projects (more than 200):- Machine Learning models to predict Bladder Cancer and Alzheimer's Disease- Simulation of investment strategies in the energy industry- Trading Algorithms to invest in the Stock Market- Causal statistics to analyze Psychological Factors- Dashboards for Final Thesis Projects using Shiny, Dash, Streamlit, Tableau, and Power BIBased on his vast and intense experience, he has created an educational program that solves most of the problems students face in learning programming for data.His students consider their methodology one of the best and assure that you will always leave his sessions with practical skills acquired and ready to be applied to your problems.

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