Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)

A data scientist generates organizational value by building web apps that take machine learning models into production.Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)Build Web Apps with Machine LearningThe web application you learn how to build uses data science to predict new product prices!Predictive Web Applications Productionalize Data ScienceA 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 BrandingSo give it to them. Learn how to customize the appearance of your application to match your organizations branding.Final ProjectYou 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 ProgrammingWill be distributed via Shinyapps.ioFinal ProjectDark ThemeThis course is designed for…Beginner data scientists that have completed the DS4B 101-R course and want to build predictive web applicationsIntermediate data scientists familiar with R but want to learn Shiny and FlexdashboardYou build production-ready applicationsGet ed now!Paid Course$3993 Low Monthly Payments3X Monthly3 payments of $159/month3-Step SystemFollow a 3-step learning path:Build your knowledge of core concepts with a Sales DashboardExtend your knowledge of Machine Learning and advanced techniques into Price Prediction ApplicationCustomize the end product with theme and logosCourse RoadmapExperience the innovative 3-Step System!Step 1: You’ll by creating a Sales DashboardCreating 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:ShinyGeographic DataTime Series DataInteractive PlotsReactive ProgrammingObserving Events & Controlling FlowStep 2: Next, You Create A Predictive Web ApplicationYou 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 AppModularize code into functionsCreate advanced interactive chartsStep 3: You finish by Customizing Your Web ApplicationYour 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 & CSSUse Google’s Chrome InspectorYou will:Learn to use Google Inspector for inspecting web pagesAdd logosAdjust the theme with CSSBusiness Objective: Use Data Science to More Consistently Price ProductsThe 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 ProvideWe 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 appsA Cohesive Tool Chain that includes shiny, flexdashboard, shinyWidgets, and shinyjsComprehensive 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 & Flexdashboard2 Web Apps That You Can Productionalize ($5000 value)Sales Dashboard – Exposes you to Geographic and Time Series data along with learning reactive programming with ShinyProduct Prediction Application – Integrates Machine Learning (XGBoost) and advanced visualizationsHundreds of Resources($1000 value):ULTIMATE R CHEAT SHEET – The New & Improved Version 2.0100+ Video Coding Lessons7 Key Resources2 ChallengesAdding it up: $6,000 valuePurchase today for: $399*Price excludes local taxes & VATGet ed now!Paid Course$3993 Low Monthly Payments3X Monthly3 payments of $159/monthYour InstructorMatt DanchoMatt DanchoFounder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane ups! Matt loves educating data scientists on how to apply powerful tools within their organization to yield ROI. Matt doesn’t rest until he gets results (literally, he doesn’t sleep so don’t be suprised if he responds to your email at 4AM)!Course CurriculumWelcome to Shiny Web Application Development (Level 1)Building Web Applications that Deliver Business Value! (2:15)Course Roadmap – Building Production-Ready Web Apps Fast! (1:54)Private Slack Channel: How to JoinCourse Certificate – InstructionsPrerequisitesPrerequisitesGetting HelpGetting Help (IMPORTANT!!!)1.0 Getting edOverview1.1 Business Case & Course RoadmapWhy Pricing Products Consistently Is Important (0:57)Course Objective – Product Price Prediction App with Shiny & Flexdashboard (1:17)1.2 Tools In Our ToolboxResource #1: The Ultimate R Cheat Sheet – Version 2.0 (File Download) (2:51)1.3 Data Science Project SetupInstalling R (Optional) (3:06)Installing RStudio IDE (Optional) (3:03)Setting Up The Project (File Download) (2:34)Installing R Packages (File Download) (3:03)1.4 Transactional Data Introduction – Bike Sales (Recap from 101)Transactional Data – What Is It? (1:41)Orders: The Building Blocks of Transactional Data (3:53)Data Model: Entity Relationship Diagram (2:14)Understanding Database Relationships (6:18)Part 1 – Sales DashboardPart 1 – Learning Shiny By Building A Sales Dashboard! (2:02)2.0 Making A Sales Dashboard with FlexdashboardWhat You Build In This Section (0:54)2.1 Flexdashboard PrimerResource #2: Flexdashboard Documentation & Key Resources (6:30)Flexdashboard: Introduction & Layout Basics (3:05)Orientation: Column vs Row (1:24)Vertical Layout: Fill vs Scroll (3:49)Tabsets (2:50)Multiple Pages (4:01)Code Checkpoint2.2 Sales Dashboard – Integrating a Plotly Chloropleth MapFlexdashboard Setup (1:35)Libraries (1:23)Database Connection (4:02)Joining Data Using The SQLite Backend – Part 1 (5:00)Joining The Data Using The Database Backend – Part 2 (4:12)Processing Data: Final Preparations for the Map (2:20)Adding A Section To The App (1:59)Making the Plotly Map, Part 1: Plotly Chloropleth Maps (1:39)Making The Plotly Map, Part 2: Aggregation By State (3:01)Making The Plotly Map, Part 3: plot_geo() (2:37)Making the Plotly Map, Part 4: add_trace() (3:27)Making the Plotly Map, Part 5: layout() (3:14)Code Checkpoint3.0 Adding Shiny Reactive Components to the Sales DashboardWhat You Build In This Section (0:41)Setup (File Download) (1:47)3.1 Shiny TutorialResources #3: Shiny Cheat Sheet (8:18)Resource #4: Shiny Widgets Gallery (1:40)Resource #5: HTML Widgets Showcase (4:47)Resource #6: shinyjs (2:05)Shiny Tutorial App – Overview (5:18)Checkbox – checkboxGroupInput() (5:37)Checkbox – renderPrint() & textOutput() (7:19)Date Range – dateRangeInput() (5:12)Date Range – renderPrint() & textOutput() (2:41)Slider – sliderInput() (3:49)Slider – renderPrint() & textOutput() (2:34)Reactive Filtering – reactive() (5:48)Data Table – Interactive Tables with DT (5:26)Reactive Expressions: Adding More Inputs to reactive() (5:27)Reactive Summarization: DT (5:17)Reset Button, Part 1: actionButton() (2:29)Resource #7: Font Awesome (1:16)Reset Button, Part 2: observeEvent() (7:31)Code Checkpoint (File Download)3.2 Integrating Shiny into the Sales DashboardSales Dashboard: Setting Up For Shiny (4:25)shinyWidgets (2:08)Data Preparation (7:10)Bike Type Selector – shinyWidgets::checkboxGroupButtons() (6:26)Bike Type Selector – reactive() & renderPlotly() (6:34)Bike Family Selector – shinyWidgets::pickerInput() (7:03)Bike Family Selector – reactive() filter (1:20)Reset Button: actionButton() (6:22)Code Checkpoint (File Download)3.3 Challenge 1 – Add Date Range InputChallenge 1 – Add Date Range Input (File Download) (1:37)Challenge 1 – Solution, Part 1 (5:32)Challenge 1 – Solution, Part 2 (7:50)Code Checkpoint (File Download)Course SurveyQuick Course Survey4.0 Extending The Sales Dashboard with Time Series & shinyjsWhat You Build In this Section (1:02)Setup (File Download) (1:38)4.1 Time Series PlotTime Series Plot: Game Plan (1:03)Flexdashboard Layout: “Over Time” Section (1:35)Data Preparation (6:56)Making the ggplot Geometries (5:45)Formatting the ggplot (2:53)Adding Interactivity: ggplotly() (1:26)Parameterizing The Time Unit (2:02)Next Steps: Reactivity (0:55)Code Checkpoint (File Download)4.2 Adding Reactivity to the Time Series PlotAdding Reactivity: Game Plan (1:32)Adding Reactivity, Part 1: Date Range Input (5:55)Adding Reactivity, Part 2: renderPlotly() (2:04)Adding Reactivity, Part 3: Connecting the Category 1 & 2 Inputs (4:46)Adding Reactivity, Part 4: Date Aggregation with Radio Group Buttons (8:40)Adding Reactivity, Part 5: Connecting the Date Aggregation Buttons (0:40)Finishing Touches (2:24)Next Steps (0:45)Code Checkpoint (File Download)4.3 Integrating an Apply Button & shinyjsApply Button: Reactive Programming Overview (2:09)Adding the Apply Button: actionButton() (1:56)Reactive Button Click: eventReactive() (5:19)Loading Plots When Firing Up The Application (3:07)Welcome to shinyjs (0:59)Setting Up shinyjs in RMarkdown (2:25)Reset Button: Update Time Aggregation Buttons (2:39)Reset Button: Click “Apply” with shinyjs (4:05)Reset Button: Delay “Apply” Click with shinyjs (2:30)Recap (1:12)Code Checkpoint (File Download)4.4 BONUS – Adding Value Boxes to Your DashboardWhat You Build (0:53)Setup & Layout (3:30)Making Value Boxes: valueBox() (3:33)Data Summarization, Part 1 (7:24)Data Summarization, Part 2 (4:20)Reactive Data Summarization (2:23)Healthy Value Box (4:35)Wealthy Value Box (2:59)Wise Value Box (3:00)Code Checkpoint (File Download)Part 2 – Product Pricing Prediction AppPart 2 – Making A Predictive Web Application that Helps Your Organization (2:10)5.0 Predictive Analysis – XGBoost + ParsnipPredictive Analysis Goals (4:10)Setup (File Download) (3:32)Preprocessing the Bikes Table (7:06)Training Data Set: Getting Ready for parsnip + XGBoost (3:12)Machine Learning Algorithm: parsnip + XGBoost (6:36)Code Checkpoint #1 (File Download)Modularizing the Preprocessing Code, Part 1: Separate Bike Description (9:06)Modularizing the Preprocessing Code, Part 2: Separate Model Description (5:03)Code Checkpoint #2 (File Download)Making Predictions from User Input (6:02)Modularizing the Prediction: Generating New Bikes (8:12)Code Checkpoint #3 (File Download)Formatted Table (5:18)Modularizing the Table Output: format_table() (1:08)Bike Prediction Plot: Data Preparation (7:03)Bike Prediction Plot: ggplot, Part 1 (7:16)Bike Prediction Plot: ggplot, Part 2 (4:47)Modularize the Bike Prediction Plot: plot_bike_prediction() (5:05)Code Checkpoint #4 (File Download)6.0 Prediction App – Getting The Analysis Into The Flexdashboard LayoutWhat You Build In This Section (0:52)Setup: dir_create() & flexdashboard creation (2:47)Setting Up The Flexdashboard Layout (3:25)Load Libraries (3:35)Data: Connect to SQLite and Load Bikes Table Into Memory (3:19)Scripts: Source Our Modular Prediction Functions (3:00)Machine Learning Model: Loading Our XGBoost Algorithm (1:51)Generating Bike Predictions (3:33)The Prediction Table: format_table() (0:40)The Price Prediction Plot: plot_bike_prediction() (2:26)Aside – The Difference Between Flexdashboard With & Without runtime: shiny (1:37)Code Checkpoint (File Download)7.0 Prediction App – Adding User Input with Shiny!What You Build In This Section (0:59)Setup (File Download) (0:47)7.1 Adding Shiny To The Predictive Web ApplicationAdding Shiny To Our App – runtime: shiny (1:33)Adding A Text Input: textInput() (2:09)Adding An Apply Button: actionButton() (2:09)Adding Reactivity To The Apply Button: eventReactive() (4:21)Rendering The Prediction Plot: renderPlotly() (2:31)Rendering The Prediction Table: renderTable() (1:49)Render On Load: Prediction Plot & Table (1:26)Code Checkpoint (File Download)7.2 Challenge 2 – Reset ButtonChallenge #2: Reset Button (File Download) (1:29)Challenge #2 Solution (File Download) (6:22)8.0 Completing the Predictive Web App – More Shiny!What You Build In This Section (0:54)Setup (File Download) (0:43)Organizing Our UI & Adding The shinyWidgets Library (2:04)Getting The Unique Categories: distinct() (3:00)Adding Bike Family UI: pickerInput() (3:58)Connecting Bike Family UI To The Apply Button (2:07)Connecting Bike Family UI To The Reset Button (2:00)Generate Bike Type From Bike Family (6:22)Update Bike Family In Bike Prediction (3:17)Adding Frame Material UI: pickerInput() (2:17)Connecting Frame Material UI To Apply, Reset, & Generate New Bike Prediction (3:37)Code Checkpoint (File Download)Part 3 – Customizing The Application ThemePart 3 – Customizing The App To Your Organizations Brand! (1:07)9.1 HTML & CSS Crash CoursesTraining for Web Developers – Traversy Media1 Hour HTML Crash Course for Beginners – Traversy Media1.5 Hour CSS Crash Course – Traversy Media9.2 Customizing The Appearance Of Your AppSetup (File Download) (1:44)Resource #8: Chrome DevTools – Browser Web Development Tools (10:10)[OPTIONAL / ADVANCED] 1 Hour Chrome DevTools Crash Course – Traversy MediaResource #9: Google Fonts (8:03)CSS Setup & Coloring the Navbar (7:07)Coloring the Sidebar (3:49)Adding a Logo (4:53)Google Fonts (8:03)Navbar – Montserrat (5:01)Headers – Montserrat (4:31)Body – Roboto (2:25)Coloring the Buttons (9:15)Code Checkpoint (File Download)9.3 Challenge 3 – Matching Cannondale’s Brand AppearanceChallenge #3: Cannondale Challenge! (File Downloads) (4:18)Challenge #3 Solution, Part 1 (File Download) (14:10)Challenge #3 Solution, Part 2 (15:28)9.4 Bonus – Adding Images To Your Cannondale AppDisplaying Product Images in Your Shiny App (1:20)Setup (File Downloads) (4:52)Connect the Script & Model Paths (4:09)Image Placeholder (1:20)Extracting the Model Base (5:58)Generate New Bike: To Speed Up Debugging (4:01)Detecting & Retrieving the Image Path (5:20)Expose the Image Path to the App (2:36)Rendering Images: renderImage() (4:58)Handling Bike Models with Numbers in their Names (5:38)Code Checkpoint (File Download)Part 4 – Add Demand Forecasting & Customizing Your Sales DashboardPart 4 – Adding Demand Forecasting & Customizing Your Sales Dashboard (0:59)App Setup (File Download) (4:04)10.1 Demand Forecast Analysis – Parnsip + XGBoostForecast Analysis: Setup & Overview (4:04)Data Processing (5:16)Time Series Aggregation: aggregate_time_series() (7:04)Time Series Plot: plot_time_series() (5:16)Time Series ML vs ARIMA: Why We Are Using ML For Our App (1:45)timetk, Part 1: A toolkit for time series ML prep (6:21)timetk Part 2: A Toolkit for Time Series ML Prep (3:14)Making Training & Future Data (8:47)XGBoost Forecast Model (File Download) (7:51)Making Predictions & Outputting in the Format for Our New Forecast Plot (8:22)Modularizing the XGBoost Forecast: generate_forecast() (6:02)Interactive Forecast Plot (6:45)plot_forecast() (2:30)Code Checkpoint (File Download)10.2 Visual Forecast Inspection & Model AdjustmentVisual Forecast Inspection (6:10)Preparing for the Model Change (4:08)Add Logic: Separating the Yearly Data from the Other Time Aggregations (2:32)Making a Linear Regression Model for Yearly Data (5:01)Updating the Plot Forecast Function, Part 1 (5:28)Updating the Plot Forecast Function, Part 2 (5:40)Saving Our Functions (1:21)Code Checkpoint (File Download)10.3 Forecasting – What About Trend? (XGBoost vs GLMNet)Setup (File Download) (3:23)Trend Evaluation (3:07)Experiment #1: Exploring Trend w/ XGBoost Forecast (3:09)Experiment #2: Comparing XGBoost & GLMNet (13:53)10.4 Forecast Mode – Integrating the ML Forecast into our App!Overview (2:02)Setup (2:24)Toggle Switch: switchInput() [with CSS Copy-Paste] (5:46)Showing & Hiding the Forecast Horizon: conditionalPanel() (4:07)Link the Forecast Toggle Switch to Reset & Update Buttons (2:59)Integrating the Forecast (8:34)Producing the Forecast Plot (4:20)Connecting the Forecast to the Time Unit Radio Buttons: observeEvent() (3:25)Connecting the Forecast to the Forecast Mode Toggle Switch: observeEvent() (3:26)Value Boxes, Part 1: Update Colors with Changing Data Filters (6:04)Value Boxes, Part 2: Update Colors with Changing Data Filters (6:51)Customer Selection: pickerInput() (6:00)Connecting the Customer Selection to the Data Filter (4:50)Recap (0:58)Code Checkpoint (File Download)10.5 Customization – Business Science Light ThemeCreating a CSS Theme for Our Shiny App (1:09)Setup (File Download) (1:14)Connecting Our CSS & Logo (2:59)Adjusting the Sidebar Width (8:34)Coloring the Forecast Mode Toggle Switch (4:54)Coloring the Time Series Radio Buttons (10:59)Coloring the Value Boxes (5:51)Updating the CSS to Make Responsive for Mobile Devices (2:35)Enabling Scroll Capability on the UI Sidebar for Mobile Devices (3:16)Code Checkpoint (File Download)10.6 Customization – Business Science Dark ThemeMaking a Dark Theme! (0:45)Adding A View Mode Toggle Switch (9:19)Creating The Dark Theme (6:05)Switching Themes with includeCSS() & renderUI() (5:39)Adjusting the Theme for Mobile Devices (3:14)Final Shiny Sales Dashboard with Forecasting & Dark Theme! (1:10)Code Checkpoint (File Download)Part 5: Production & DeploymentPublishing Your Shiny Web Application (0:29)Publishing Options (6:03)11.1 Publishing Your Shiny Web App!Setup: Shinyapps.io & App Files (3:40)Connecting To Shinyapps.io & Publishing (3:04)Troubleshooting Error #1: Files are Outside of App Directory (5:20)Troubleshooting Error #2: xgboost package is not being installed (1:54)Shiny App Deployed!!! (0:26)11.2 ChallengeChallenge #4: Create & Deploy Your Own App (1:36)Congratulations!!!You Did It! I’m So Happy For You!! (1:38)Frequently Asked QuestionsWhen does the course and finish?The course s now and never ends! It is a completely self-paced online course – you decide when you and when you finish.How long do I have access to the course?How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like – across any and all devices you own.What if I am unhappy with the course?We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.Read more: https://archive.is/VWCivHere’s What You’ll Get in Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)– Download Sample files “Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)”Course Requirement: Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)Real Value: $79Frequently Asked Questions For “Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)”How to make payment for “Matt Dancho – DS4B 102-R – Shiny Web Applications (Intermediate)” ?Please add to cart on this page and go to checkout page. You can also add as many other products as you like and make a one-time payment.We accept several type of Stripe payments such as Visa, Mastercard, American Express, Discover, Diners Club, Google Pay, Apple Pay and JCB, payments from customers worldwide. Paypal & Bitcoin please contact us.We strongly recommend our customers to make a payment through Stripe & Paypal . 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