Learning Labs Pro

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Your Resource for Cutting-Edge Technology in a Focused Course Format

Learning Labs cover a wide variety of topics that matter to data scientists. They are generally 1.5 hours & include live coding and demonstrations.

Why go PRO?

It’s simple – You get a new 1-hour course in your inbox every 2-weeks on intermediate & advanced topics. Perfect for continuous data science education on all of the critical topics we don’t touch in our core R-Track Course curriculum.

Watch Learning Lab 28 – Shiny Real Estate API (Free Sample)

You get a lab containing an Advanced Data Science Project in your inbox 2X per Month!

Code + Video Instruction + Shiny App!

LL PRO Topics & Course List

The most important topics in data science 2X per month

Special: Time Series Forecasting with Modeltime

Lab 47: Forecasting with Autoregressive Machine Learning | Scalable AR(ML) Bonus

Lab 46: Forecasting at Scale with Modeltime | “Nostradamus” Auto-Forecasting Shiny App Bonus

Lab 38: Time Series Forecasting | Intro to Modeltime

Building an R Package (R Developer Series)

Lab 45 [Part 3]: Lab 45: Shiny Apps with Golem | golem | Shiny PowerPoint Golem App Bonus

Lab 44 [Part 2]: R Package Development | usethis | Shiny PowerPoint Bonus

Lab 43 [Part 1]: Tidy Eval + PowerPoint Automation | officer & rlang | Automate PowerPoint Bonus

R in Production (MLOps)

Lab 42 [Part 4]: Automating Google Sheets with R API (Plumber, Docker, & AWS)

Lab 41 [Part 3]: Scalable Forecasting with Metaflow + Modeltime + AWS

Lab 40 [Part 2]: Docker for Data Science

Lab 39 [Part 1]: Building a Bankruptcy Prediction API with H2O & MLFlow

Python & R Series, 5-Part Series

Lab 37 [Part 5]: NLP & PDF Text Extraction (spaCy)

Lab 36 [Part 4]: TensorFlow Multivariate Forecasting & Enhanced TF Tutorial (Time Series, Energy)

Lab 35 [Part 3]: TensorFlow Univariate Forecasting & Gold Forecasting App (Time Series, Finance)

Lab 34 [Part 2]: Advanced Customer Segmentation & Market Basket Analyzer App (E-Commerce, Scikit-Learn)

Lab 33 [Part 1]: Employee Segmentation with Python & R (HR Analytics, Scikit-Learn)

Shiny API, 5-Part Series

Lab 32 [Part 5]: Text Mining Tweets with Twitter & Tidytext

Lab 31 [Part 4]: Forecasting Google Analytics with Facebook Prophet & Shiny

Lab 30 [Part 3]: Shiny Financial Analysis with Tidyquant API (Finance)

Lab 29 [Part 2]: Shiny Crude Oil Forecast (Multivariate ARIMA) with Quandl API & Fable

Lab 28 [Part 1]: Shiny Real Estate App with Zillow API

Marketing Analytics, 4-Part Series

Lab 27 [Part 4]: Google Trends Automation with Shiny

Lab 26 [Part 3]: Machine Learning for Customer Journey

Lab 25 [Part 2]: Marketing Multi-Channel Attribution with ChannelAttribution

Lab 24 [Part 1]: A/B Testing for Website Optimization with Infer & Google Optimize

SQL for Data Scientists, 3-Part Series

Lab 23 [Part 3]: Google Analytics & BigQuery (SQL) – Conversion Funnel Analysis

Lab 22 [Part 2]: SQL for Time Series – Mortgage Loan Delinquency

Lab 21 [Part 1]: SQL for Data Science – Home Loan Applications & Default

Plus 20 More Labs:

Lab 20: Explaining Machine Learning for Customer Churn

Lab 19: Network Analysis – Using Customer Credit Card History to Cluster Influencers

Lab 18: Anomaly Detection for Time Series

Lab 17: Anomaly Detection with H2O Machine Learning

Lab 16: R Optimization Toolchain – Part 2 – Stock Portfolio Analysis & Nonlinear Programming

Lab 15: R’s Optimization Toolchain For Business Decision Making Part 1

Lab 14: Customer Churn Survival Analysis

Lab 13: Big Data – Wrangling 4.6M Rows (375 MB) of Financial Data with data.table

Lab 12: How I Built This – R Package Anomalize using Tidy Eval & Rlang

Lab 11: Market Basket Analysis & Recommendation Systems w/ recommenderlab

Lab 10: Building API’s with Plumber & Postman

Lab 9: Finance with R – Performance Analysis & Portfolio Optimization with tidyquant

Lab 8: Web Scraping – Build A Strategic Database With Product Data

Lab 7: 5 Strategies to Improve Business Forecasting by 50% (or more)

Lab 6: Communicating Machine Learning with the rmarkdown package

Lab 5: Hands-On Coding with the NEW parsnip package

Lab 4: H2O AutoML – Erin LeDell Guest Appearance!

Lab 3: Marketing Analytics Case Study – Excel to R

Lab 2: R In Production: Building Production-Quality Apps with Shiny

Lab 1: How to Learn R Fast!

New Learning Labs are released 2X per month!

All in one convenient location so you can watch on your schedule (and rewatch any time!)

Lab 34 – Advanced Customer Segmentation w/ Scikit-Learn & Shiny

Sign up to unlock this lab immediately!

Learn Continuously. Accelerate Your Career.

Going PRO Compliments our University Courses by hitting diverse & critical topics.

Learning Labs PRO are intermediate and advanced labs that keep you learning long after you’ve completed the R-Track. Learn continuously. Accelerate you Career.

No Experience?

Start with our NEW 5-Course R-Track System to go from beginner to advanced FAST!

I highly recommend starting with the R-Track Course Program. This will set your data science foundations and teach you how to build and deploy Shiny web applications. The Learning Labs will then extend your knowledge by giving you new projects that expand your skills.

Gain Foundations & Advanced Techniques so you can take FULL ADVANTAGE of Learning Labs PRO

Learn About Our 5-Course R-Track

Private Slack Community

Ask questions, provide feedback, and learn with the community!

Summary of Everything

You get

1-Hour Courses on Advanced Topics

Full Working Code

Slack Channel Community

Resources (Slides, References, Links, and more)

Course Curriculum

Welcome to Learning Labs PRO!

 PreviewLearning Labs PRO! (0:52)

 PreviewThank You For Joining LL PRO – Here’s The Dime Tour!

 StartJoin Our Slack Channel

SPECIAL: Forecasting with Modeltime!

 StartLab 47: Forecasting with Autoregressive Machine Learning | Scalable AR(ML) Bonus (86:47)

 StartLab 46: Forecasting at Scale with Modeltime | Nostradamus Lite Shiny Bonus (75:15)

 StartLab 38: Time Series Forecasting | Intro to Modeltime (85:29)

Building an R Package | R Package Developer Series

 StartLab 45: Shiny Apps with Golem | golem | Shiny PowerPoint Golem App Bonus (95:07)

 StartLab 44: R Package Development | usethis | Shiny PowerPoint Bonus (110:58)

 StartLab 43: Tidy PowerPoint Automation | officer & rlang | “Functionizing” Workflow (90:30)

R in Production | MLOps Series

 StartLab 42: Automating Google Sheets with R API (Plumber, Docker, & AWS) (86:12)

 StartLab 41: Forecasting at Scale with MetaFlow + Modeltime + AWS (97:21)

 StartLab 40: Docker for Data Science (91:37)

 StartLab 39: H2O & MLFlow for Bankruptcy Prediction API (88:47)

Python + R Series

 StartLab 37: NLP & PDF Text Extraction (spaCy) (100:37)

 StartLab 36: Tensorflow Multivariate Forecasting (Energy, LSTM) (108:17)

 StartLab 35: TensorFlow for Finance & Gold Price Forecaster App (Time Series, LSTM) (119:27)

 StartLab 34: Advanced Customer Segmentation & Market Basket App (E-Commerce) (107:21)

 StartLab 33: Employee Segmentation w/ Scikit-Learn (HR Analytics) (88:08)

Shiny API Series

 StartLab 32: Text Mining Tweets with Twitter & Tidytext (91:07)

 StartLab 31: Forecasting Google Analytics with Facebook Prophet & Shiny (79:26)

 StartLab 30: Shiny Finance with Tidyquant (Excel in R) (88:54)

 StartLab 29: Shiny Crude Oil Forecast (Multivariate ARIMA) App with Fable & Quandl API (83:13)

 StartLab 28: Shiny Real Estate App with Zillow API (72:50)

Marketing Analytics Series

 StartLab 27: Google Trends Automation with Shiny (66:52)

 StartLab 26: Machine Learning for Customer Journey (96:38)

 StartLab 25: Marketing Multi-Channel Attribution with ChannelAttribution (96:08)

 StartLab 24: A/B Testing for Website Optimization with Infer & Google Optimize (90:59)

 StartLab 14: Customer Churn Survival Analysis w/ correlationfunnel, parsnip, & H2O (88:30)

 StartLab 11: Market Basket Analysis & Recommendation Systems w/ recommenderlab (78:35)

 StartLab 3: Marketing Analytics Case Study – Excel to R (77:54)

Databases – SQL

 StartLab 23 – Google Analytics & BigQuery (SQL) – Conversion Funnel Analysis (85:04)

 StartLab 22 – SQL for Time Series – Stocks & Fannie Mae Mortgage Delinquency Analysis (90:16)

 StartLab 21 – SQL for Data Science – Home Loans with SQL, R, & dplyr (92:06)

Explainable Machine Learning

 StartLab 20 – Explaining Machine Learning for Customer Churn (79:03)

Network Analysis

 StartLab 19 – Using Customer Credit Card History to Cluster with Network Analysis (83:09)

Anomaly Detection

 StartLab 18 – Time Series Anomaly Detection – anomalize (87:15)

 StartLab 17 – Anomaly Detection with H2O Machine Learning (90:34)

Optimization & Simulation

 StartLab 16: R Optimization Toolchain – Part 2 – Stock Portfolio & Nonlinear Programming with ROI (88:09)

 StartLab 15: R Optimization Toolchain – Part 1 – Product Mix & Linear Programming with ompr (80:35)

Big Data

 StartLab 13: Wrangling 4.6M Rows (375 MB) of Financial Data with data.table (78:36)

Time Series

 StartLab 7: 5 Strategies to Improve Business Forecasting by 50% (or more) (89:02)

Production: Shiny & Plumber

 StartLab 10: Building API’s with Plumber & Postman (80:18)

Data Collection

 StartLab 8: Web Scraping – Build A Strategic Database With Product Data (70:07)

Domain: Finance

 StartLab 9: Finance with R – Performance Analysis & Portfolio Optimization with tidyquant (77:35)

Advanced Functional Programming

 StartLab 12: How I Built This – R Package Anomalize using Tidy Eval & Rlang (74:50)

Machine Learning – Beginning of Coded Labs

 StartLab 5: Hands-On Coding with the NEW parsnip package (75:54)

 StartLab 4: H2O AutoML – Erin LeDell Guest Appearance! (87:15)

Free / No-Code Labs (Before we transitioned to FULL CODE Labs)

 Start[IMPORTANT] Labs 1-6 were made before LL PRO existed.

 StartLab 6: Communicating Machine Learning with the rmarkdown package (71:38)

 StartLab 2: R In Production: Building Production-Quality Apps with Shiny (55:32)

 StartLab 1: How to Learn R Fast! (56:35)

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