DS4B 102-R: Shiny Web Applications (Intermediate)
Salepage : DS4B 102-R: Shiny Web Applications (Intermediate)
Archive : DS4B 102-R: Shiny Web Applications (Intermediate) Digital Download
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Build a predictive web application using Shiny, Flexdashboard, and XGBoost
Build Web Apps with Machine Learning
The web application you learn how to build uses data science to predict new product prices!
Predictive Web Applications Productionalize Data Science
A data scientist generates organizational value by building web apps that take machine learning models into production.
Here’s an example of a predictive web application that you build in this course.
New Product Prediction Application (created in this course)
This web application empowers business people to make data-driven decisions by more consistently pricing products. The application incorporates:
Shiny – A web application framework with UI components that are reactive to user input.
Flexdashboard – A dashboarding framework that is built on top of RMarkdown.
parsnip and XGBoost – Machine learning models used to predict product prices.
Most importantly, business people can use the application to improve the consistency of new product prices based on an existing product portfolio thanks to the power of Machine Learning!
Your Organization Cares About Branding
So give it to them. Learn how to customize the appearance of your application to match your organizations branding.
Final Project
You will build a Sales Dashboard that:
Uses XGBoost to Predict Sales Demand by Customers & Product Categories.
Toggles between Light and Dark Themes – Customized by You and your theme-building skills!
Controls flow using Reactive Programming
Will be distributed via Shinyapps.io
Final Project
Dark Theme
This course is designed for…
Beginner data scientists that have completed the DS4B 101-R course and want to build predictive web applications
Intermediate data scientists familiar with R but want to learn Shiny and Flexdashboard
You build production-ready applications
3-Step System
Follow a 3-step learning path:
Build your knowledge of core concepts with a Sales Dashboard
Extend your knowledge of Machine Learning and advanced techniques into Price Prediction Application
Customize the end product with theme and logos
Course Roadmap
Experience the innovative 3-Step System!
Step 1: You’ll start by creating a Sales Dashboard
Creating a Sales Dashboard exposes you to reactive programming. You will apply complex rules to control how your application functions when users interact with the app.
You gain experience using:
Shiny
Geographic Data
Time Series Data
Interactive Plots
Reactive Programming
Observing Events & Controlling Flow
Step 2: Next, You Create A Predictive Web Application
You will build a new application that integrates Machine Learning (XGBoost) along with a more complex interactive visualization.
You learn how to:
Integrate machine learning (parsnip and XGBoost) into a Web App
Modularize code into functions
Create advanced interactive charts
Step 3: You finish by Customizing Your Web Application
Your company’s brand appearance is important. Make an app theme that is consistent with the look and feel of your organization’s branding.
Create Your Own Theme Using HTML & CSS
Use Google’s Chrome Inspector
You will:
Learn to use Google Inspector for inspecting web pages
Add logos
Adjust the theme with CSS
Business Objective: Use Data Science to More Consistently Price Products
The Business Problem:
Businesses can lose customer confidence and profitability if products are inconsistently priced.
The Solution:
This web application solves the inconsistent pricing problem by using predictive analytics to generate new product prices based on existing products.
The application is easy to use, and best of all, an app like this generates business value for your organization!
Tools & Frameworks We Provide
We provide you:
A Complete Learning Path to taking you from basic knowledge of R to being able to build and deploy interactive, machine-learning powered web apps
A Cohesive Tool Chain that includes shiny, flexdashboard, shinyWidgets, and shinyjs
Comprehensive resources: You are provided a cheat sheet, code templates, and resources that speed up learning and make referring back to materials simple.
Full Life-Time Access: Once you purchase the course, you gain life-time access to content now and any updates in the future.
Access to our Private Slack Community where you can access Matt (the course instructor) and network with other students.
Summary of What You Get!
Methodical training program that teaches you how to build web applications using Shiny & Flexdashboard
2 Web Apps That You Can Productionalize ($5000 value)
Sales Dashboard – Exposes you to Geographic and Time Series data along with learning reactive programming with Shiny
Product Prediction Application – Integrates Machine Learning (XGBoost) and advanced visualizations
Hundreds of Resources($1000 value):
ULTIMATE R CHEAT SHEET – The New & Improved Version 2.0
100+ Video Coding Lessons
7 Key Resources
2 Challenges(Level 1)
PreviewBuilding Web Applications that Deliver Business Value! (2:15)
PreviewCourse Roadmap – Building Production-Ready Web Apps Fast! (1:54)
StartPrivate Slack Channel: How to Join
StartCourse Certificate – Instructions
Prerequisites
PreviewPrerequisites
Getting Help
StartGetting Help (IMPORTANT!!!)
1.0 Getting Started
StartOverview
1.1 Business Case & Course Roadmap
PreviewWhy Pricing Products Consistently Is Important (0:57)
PreviewCourse Objective – Product Price Prediction App with Shiny & Flexdashboard (1:17)
1.2 Tools In Our Toolbox
StartResource #1: The Ultimate R Cheat Sheet – Version 2.0 (File Download) (2:51)
1.3 Data Science Project Setup
StartInstalling R (Optional) (3:06)
StartInstalling RStudio IDE (Optional) (3:03)
StartSetting Up The Project (File Download) (2:34)
StartInstalling R Packages (File Download) (3:03)
1.4 Transactional Data Introduction – Bike Sales (Recap from 101)
StartTransactional Data – What Is It? (1:41)
StartOrders: The Building Blocks of Transactional Data (3:53)
StartData Model: Entity Relationship Diagram (2:14)
StartUnderstanding Database Relationships (6:18)
Part 1 – Sales Dashboard
PreviewPart 1 – Learning Shiny By Building A Sales Dashboard! (2:02)
2.0 Making A Sales Dashboard with Flexdashboard
PreviewWhat You Build In This Section (0:54)
2.1 Flexdashboard Primer
StartResource #2: Flexdashboard Documentation & Key Resources (6:30)
StartFlexdashboard: Introduction & Layout Basics (3:05)
StartOrientation: Column vs Row (1:24)
StartVertical Layout: Fill vs Scroll (3:49)
StartTabsets (2:50)
StartMultiple Pages (4:01)
StartCode Checkpoint
2.2 Sales Dashboard – Integrating a Plotly Chloropleth Map
StartFlexdashboard Setup (1:35)
StartLibraries (1:23)
StartDatabase Connection (4:02)
StartJoining Data Using The SQLite Backend – Part 1 (5:00)
StartJoining The Data Using The Database Backend – Part 2 (4:12)
StartProcessing Data: Final Preparations for the Map (2:20)
StartAdding A Section To The App (1:59)
StartMaking the Plotly Map, Part 1: Plotly Chloropleth Maps (1:39)
StartMaking The Plotly Map, Part 2: Aggregation By State (3:01)
StartMaking The Plotly Map, Part 3: plot_geo() (2:37)
StartMaking the Plotly Map, Part 4: add_trace() (3:27)
StartMaking the Plotly Map, Part 5: layout() (3:14)
StartCode Checkpoint
3.0 Adding Shiny Reactive Components to the Sales Dashboard
PreviewWhat You Build In This Section (0:41)
StartSetup (File Download) (1:47)
3.1 Shiny Tutorial
StartResources #3: Shiny Cheat Sheet (8:18)
StartResource #4: Shiny Widgets Gallery (1:40)
StartResource #5: HTML Widgets Showcase (4:47)
StartResource #6: shinyjs (2:05)
StartShiny Tutorial App – Overview (5:18)
StartCheckbox – checkboxGroupInput() (5:37)
StartCheckbox – renderPrint() & textOutput() (7:19)
StartDate Range – dateRangeInput() (5:12)
StartDate Range – renderPrint() & textOutput() (2:41)
StartSlider – sliderInput() (3:49)
StartSlider – renderPrint() & textOutput() (2:34)
StartReactive Filtering – reactive() (5:48)
StartData Table – Interactive Tables with DT (5:26)
StartReactive Expressions: Adding More Inputs to reactive() (5:27)
StartReactive Summarization: DT (5:17)
StartReset Button, Part 1: actionButton() (2:29)
StartResource #7: Font Awesome (1:16)
StartReset Button, Part 2: observeEvent() (7:31)
StartCode Checkpoint (File Download)
3.2 Integrating Shiny into the Sales Dashboard
StartSales Dashboard: Setting Up For Shiny (4:25)
StartshinyWidgets (2:08)
StartData Preparation (7:10)
StartBike Type Selector – shinyWidgets::checkboxGroupButtons() (6:26)
StartBike Type Selector – reactive() & renderPlotly() (6:34)
StartBike Family Selector – shinyWidgets::pickerInput() (7:03)
StartBike Family Selector – reactive() filter (1:20)
StartReset Button: actionButton() (6:22)
StartCode Checkpoint (File Download)
3.3 Challenge 1 – Add Date Range Input
StartChallenge 1 – Add Date Range Input (File Download) (1:37)
StartChallenge 1 – Solution, Part 1 (5:32)
StartChallenge 1 – Solution, Part 2 (7:50)
StartCode Checkpoint (File Download)
Course Survey
StartQuick Course Survey
4.0 Extending The Sales Dashboard with Time Series & shinyjs
PreviewWhat You Build In this Section (1:02)
StartSetup (File Download) (1:38)
4.1 Time Series Plot
StartTime Series Plot: Game Plan (1:03)
StartFlexdashboard Layout: “Over Time” Section (1:35)
StartData Preparation (6:56)
StartMaking the ggplot Geometries (5:45)
StartFormatting the ggplot (2:53)
StartAdding Interactivity: ggplotly() (1:26)
StartParameterizing The Time Unit (2:02)
StartNext Steps: Reactivity (0:55)
StartCode Checkpoint (File Download)
4.2 Adding Reactivity to the Time Series Plot
StartAdding Reactivity: Game Plan (1:32)
StartAdding Reactivity, Part 1: Date Range Input (5:55)
StartAdding Reactivity, Part 2: renderPlotly() (2:04)
StartAdding Reactivity, Part 3: Connecting the Category 1 & 2 Inputs (4:46)
StartAdding Reactivity, Part 4: Date Aggregation with Radio G
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