Complete Guide to Data Science Applications with Streamlit

Complete Guide to Data Science Applications with Streamlit, available at $64.99, has an average rating of 4.5, with 152 lectures, based on 52 reviews, and has 723 subscribers.

You will learn about Building Data Applications with Streamlit Integrating Matptlotlib & Seaborn in Streamlit Plotly Visualizations in Streamlit Authenticating Streamlit Applications Deploying Streamlit Applications Using Streamlit Components Altair Visualizations in Streamlit This course is ideal for individuals who are Individuals interested in building data science and machine learning applications in Python It is particularly useful for Individuals interested in building data science and machine learning applications in Python.

Enroll now: Complete Guide to Data Science Applications with Streamlit

Summary

Title: Complete Guide to Data Science Applications with Streamlit

Price: $64.99

Average Rating: 4.5

Number of Lectures: 152

Number of Published Lectures: 152

Number of Curriculum Items: 152

Number of Published Curriculum Objects: 152

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Building Data Applications with Streamlit

Integrating Matptlotlib & Seaborn in Streamlit

Plotly Visualizations in Streamlit

Authenticating Streamlit Applications

Deploying Streamlit Applications

Using Streamlit Components

Altair Visualizations in Streamlit

Who Should Attend

Individuals interested in building data science and machine learning applications in Python

Target Audiences

Individuals interested in building data science and machine learning applications in Python

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Introduction to Streamlit

Lecture 3: Download all the files

Lecture 4: Assignment

Chapter 2: Python Crash Course

Lecture 1: Section Intro

Lecture 2: Install Anaconda

Lecture 3: The Data Science Process

Lecture 4: Python operations & Comments

Lecture 5: Python Types

Lecture 6: Lists and Indexing

Lecture 7: List – Negative Indexing

Lecture 8: Python Dictionaries

Lecture 9: Python Tuples

Lecture 10: Python Sets

Lecture 11: Python Boolean Operators

Lecture 12: Conditional Statements

Lecture 13: Python Functions

Lecture 14: Python For Loop

Lecture 15: Python While Loop

Lecture 16: Python Map Function

Lecture 17: Python Range Function

Lecture 18: Python Exercise

Lecture 19: Python Solutions

Chapter 3: Package Management in Python

Lecture 1: Section Intro

Lecture 2: Virtual Environment

Lecture 3: Pip Practical

Lecture 4: Anaconda Package Installation

Chapter 4: NumPy Crash Course

Lecture 1: NumPy Introduction

Lecture 2: NumPy Zeros, Ones, and Linspace

Lecture 3: Checking NumPy Documentation

Lecture 4: One Dimensional Indexing

Lecture 5: Multi Dimensional Indexing

Lecture 6: Broadcasting in NumPy

Lecture 7: Operations in NumPy

Lecture 8: NumPy Exercise

Lecture 9: NumPy Solutions

Chapter 5: Pandas Crash Course

Lecture 1: Section Intro

Lecture 2: Introduction to Pandas

Lecture 3: Pandas DataFrames

Lecture 4: Resetting the Index

Lecture 5: Dropping Columns

Lecture 6: Dealing with Null Values

Lecture 7: Creating New Columns

Lecture 8: Selecting in Pandas

Lecture 9: Grouping Data

Lecture 10: Exporting Data Frames

Lecture 11: Loading Data

Lecture 12: Pivot Tables

Lecture 13: Pandas Project

Lecture 14: Solutions: Part 1

Lecture 15: Solutions: Part 2

Lecture 16: Solutions: Part 3

Lecture 17: Solutions: Part 4

Lecture 18: Solutions: Part 5

Lecture 19: Solutions: Part 6

Lecture 20: Solutions: Part 7

Chapter 6: Visualization Guide

Lecture 1: Visualization Guide

Chapter 7: Matplotlib with Streamlit

Lecture 1: Section Intro

Lecture 2: Intro

Lecture 3: Matplotlib Intro

Lecture 4: Set Up Environment

Lecture 5: First Visual

Lecture 6: Markdown

Lecture 7: Bar Plot

Lecture 8: Create Horizontal Bar

Lecture 9: Create Scatter Plot

Lecture 10: Histogram

Lecture 11: Pie Chart

Lecture 12: Make Sub Plots

Lecture 13: Create Four Sub Plots

Lecture 14: Figure & Axes

Lecture 15: Four Plots With Figure & Axes

Chapter 8: Streamlit with Seaborn

Lecture 1: Section Intro

Lecture 2: Data Introduction

Lecture 3: App Introduction

Lecture 4: Create Count Plot

Lecture 5: Stripplot & Violin Plot

Lecture 6: Exercise

Lecture 7: Show Trend

Lecture 8: Figure & Axes

Lecture 9: Word Cloud

Chapter 9: Extras

Lecture 1: Extras – Page Title, Favicon etc

Chapter 10: File Upload

Lecture 1: File Upload

Chapter 11: Mapping

Lecture 1: Map

Chapter 12: Image Processing Application

Lecture 1: Section Intro

Lecture 2: Show Image

Lecture 3: Rotate Image

Lecture 4: Create Thumbnail

Instructors

Derrick Mwiti
Data Scientist | Author | Mentor

Namespace Labs
Data Science Instructor

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 4 votes

4 stars: 13 votes

5 stars: 35 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!