Learn & Deploy Data Science Web Apps with Streamlit

Learn & Deploy Data Science Web Apps with Streamlit, available at $79.99, has an average rating of 4.5, with 74 lectures, 1 quizzes, based on 198 reviews, and has 1686 subscribers.

You will learn about Create powerful streamlit apps Create beautiful web app in minutes Build Web App without knowing anything on HTML, CSS, Javascrip Develop Web Apps in Python Develop data science web app This course is ideal for individuals who are Data Scientist who want to present Data Analysis and machine learning models It is particularly useful for Data Scientist who want to present Data Analysis and machine learning models.

Enroll now: Learn & Deploy Data Science Web Apps with Streamlit

Summary

Title: Learn & Deploy Data Science Web Apps with Streamlit

Price: $79.99

Average Rating: 4.5

Number of Lectures: 74

Number of Quizzes: 1

Number of Published Lectures: 74

Number of Published Quizzes: 1

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 75

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Create powerful streamlit apps
Create beautiful web app in minutes
Build Web App without knowing anything on HTML, CSS, Javascrip
Develop Web Apps in Python
Develop data science web app

Who Should Attend

Data Scientist who want to present Data Analysis and machine learning models

Target Audiences

Data Scientist who want to present Data Analysis and machine learning models

Welcome to the course Learn Streamlit for Data Science

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. In just a few minutes you can build and deploy powerful data apps.

On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.

In this course, we start out with the Streamlit basics. We will learn how to download and run demo Streamlit apps, how to edit demo apps using our own text editor, how to organize our Streamlit apps, and finally, how to make our very own. Then, we will explore the basics of data visualization in Streamlit. We will learn how to accept some initial user input, and then add some finishing touches to our own apps with text. At the end of this course, you should be comfortable starting to make your own Streamlit applications.

In particular, we will cover the following topics:

Why Streamlit?
Installing Streamlit
Organizing Streamlit apps
Streamlit
Text Elements
Display Data
Layouts
Widgets
Data Visualization
- Integrating Widgets to Visualizations
- Plotly
- Bokeh
- Streamlit
Data Science Project
Deploy Data Science Web App in Cloud

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is streamlit ?

Lecture 2: Flask vs Django vs Streamlit

Lecture 3: Download the resourses

Lecture 4: Install Python

Lecture 5: Install Streamlit

Lecture 6: Install required libraries

Lecture 7: Install VS Code

Lecture 8: Install VS Code Extensions

Chapter 2: Getting Started with Streamlit

Lecture 1: Getting Started with Streamlit

Lecture 2: Run Streamlit

Lecture 3: Create Streamlit App in 24 Lines

Chapter 3: Streamlit APIs

Lecture 1: Download resourses

Lecture 2: Display almost anything in streamlit

Lecture 3: st.write

Lecture 4: magic

Lecture 5: Streamlit Text Elements

Lecture 6: st.markdown

Lecture 7: st.markdown part-2

Lecture 8: st.markdown part-3

Lecture 9: Text elements : title, header, subheader, code, latex

Lecture 10: Data Elements: dataframe, table, json

Lecture 11: Status Elements: Progress Bar

Lecture 12: Status Element: Spinner

Lecture 13: Status Elements: Display message like Success, Error, Warning, Info

Lecture 14: Media Elements: Display Image, Video and Audio

Lecture 15: Layouts

Lecture 16: Layouts: Columns

Lecture 17: Layouts: Expanders

Lecture 18: Layouts: Containers

Lecture 19: Layouts: Empty

Chapter 4: Streamlit Input Widgets

Lecture 1: Button

Lecture 2: Checkbox

Lecture 3: Radio button

Lecture 4: Select Box

Lecture 5: Multiselect

Lecture 6: Slider

Lecture 7: text_input, number_input, date_input

Lecture 8: File upload

Lecture 9: Assignment-1

Lecture 10: Assignment -1 Solution

Chapter 5: Practice test – 1

Chapter 6: Visualizations with Streamlit

Lecture 1: Download the Recourses

Lecture 2: Load Data and Data Analysis Questions

Lecture 3: Matplotlib Figure in Streamlit: Pie Chart

Lecture 4: Matplotlib Figure in Streamlit: Bar Chart

Lecture 5: Integrate Matplotlib and Input Widgets

Lecture 6: Seaborn Figure in Streamlit: Box plot

Lecture 7: Seaborn Figure + Streamlit Widgets: (Histogram, Violin, Kdeplott)

Lecture 8: Pandas Plot in Streamlit

Lecture 9: Plots controlled by multiple widgets

Lecture 10: Scatter Plot + Widgets

Chapter 7: Interactive Visualizations in Streamlit

Lecture 1: Introduction to interactive visualizations

Lecture 2: Streamlit Bar Chart | Area Chart | Line Chart

Lecture 3: About Plotly

Lecture 4: Plotly Chart in Streamlit part – 1

Lecture 5: Plotly Chart in Streamlit part -2

Lecture 6: Plotly Chart in Streamlit part – 3

Lecture 7: Install Bokeh

Lecture 8: Bokeh Chart in Streamlit part -1

Lecture 9: Bokeh Chart in Streamlit part -2

Chapter 8: Project – 1: Develop & Deploy Automatic Data Profiling App

Lecture 1: What will you develop

Lecture 2: Warning: DONT SKIP THIS LECTURE

Lecture 3: Updated code GitHub Repository

Lecture 4: Install Packages and Virtual Environment

Lecture 5: Upload CSV file and load data from backend

Lecture 6: Generate Data Profile Report and display in Streamlit

Lecture 7: Add input widgets to the App

Lecture 8: Validation on File type (Allow only csv and excel files)

Lecture 9: Validation on File size (Max allowed file size is 10 MB)

Lecture 10: Deploy Stream App : Install Git for Windows

Lecture 11: Deploy: Push Codes to GitHub Repository

Lecture 12: Deploy the Streamlit App

Lecture 13: Logs (No Audio)

Lecture 14: Deployed Streamlit App sucessfully

Chapter 9: Bonus

Lecture 1: Bonus Lecture

Instructors

G Sudheer
Instructor
datascience Anywhere
Team of Engineers
Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 13 votes
4 stars: 62 votes
5 stars: 118 votes

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!

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