Learning Labs Pro
1-Hour Data Science Projects Released 1X Per Month
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Your Resource for Cutting-Edge Data Science Projects
Learning Labs cover a wide variety of topics that matter to businesses. They are generally 1.5 hours & include live coding and demonstrations.
Why go PRO?
It's simple - You gain immediate access to our entire list of 80+ Learning Labs!
Plus, you get a new 1-hour course in your inbox every month on intermediate & advanced topics. Perfect for continuous data science education on all of the critical topics we don't touch in our core R and Python Track Courses. Win-win.
Watch Learning Lab 28 - Shiny Real Estate API (Free Sample)
You get a lab containing an Advanced Data Science Project in your inbox 1X per Month AND access to our entire library of courses!
Code + Video Instruction + Shiny App!
Multiplied by 80+ Labs!
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LL PRO Topics & Course List
Upon enrollment, you gain access to our entire course list!
Time Series in Python
- Lab 96 [NEW]: Macroeconomic Factors in Data Analysis and Forecasting (Special Guest: Jonathan Regenstein)
- Lab 95: Machine Learning Forecasting in Python (Feat. Polars + Nixtla MLForecast)
- Lab 94: ARIMA Forecast Modeling in Python (Special Guest Nixtla + StatsForecast)
Marketing Analytics for Data Scientists
- Lab 93: Bayesian MMM (Marketing Mix Modeling) in Python (Feat. PyMC Marketing + Special Guest: PyMC)
- Lab 92: Customer Lifetime Value CLV Forecasting in R (Feat. Modeltime)
- Lab 91: Customer Lifetime Value (CLV) in Python: 3 Methods (Feat. Lifetimes and PyCaret)
- Lab 90: Causal Machine Learning in Python (Feat. Uber's CausalML)
- Lab 89: Causal Inference & A/B Testing for Data Scientist 1 in R (Feat. Tidymodels)
- Lab 88: Price Optimization with R (Machine Learning | XGBoost)
- Lab 87: Price Optimization with Python (PyGAM)
- Lab 86: Python for Customer Segmentation Models (Scikit Learn and H2O)
Time Series Forecasting
- Lab 85: Cashflow Forecasting | Shiny Web App that Automates Excel Cash Flow Forecasting
AI for Data Scientists
- Lab 84: Building AI-Powered Apps (Email Lead Scoring App with OpenAI) (NEW!!!)
ChatGPT For Data Scientists
- Lab 83: ChatGPT Part 2 (Make A High-End Shiny App in HALF THE TIME)
- Lab 82: ChatGPT Part 1 (What I Learned From Making A FULL Machine Learning Model + App With ChatGPT)
Data Engineering ETL Automation
Shiny App: New Features
- Lab 80: Shiny for Python!
- Lab 79: Build A Shiny App In 15-Minutes with this Drag 'N Drop Editor
- Lab 78: Shiny Custom Business Themes with Bootstrap 5
Geospatial Analysis
- Lab 77: Geospatial Part 2: Networks with sf, nngeo, and osrm
- Lab 76: Geospatial Part 1: Intro to tidygeocoder, sf, and mapview
Bayesian (Probabilistic Prediction)
- Lab 75: Bayesian Part 2: Price Elasticity, Nonlinear Models, & Hierarchical Modeling
- Lab 74: Bayesian Part 1: Introduction to Bayesian for Business Analysis
Special Time Series
Natural Language Processing
- Lab 72: NLP in R Masterclass | Tidymodels & Text Recipes
- Lab 71: Introduction to NLP with Python | Customer Survey Data
- Lab 70: Introduction to NLP with R | Resume Analyzer App
Excel to R & Python
- Lab 69: Risk Analysis & Monte Carlo Simulation in Python
- Lab 68: Excel to R: Introduction to Risk Analysis | Monte Carlo Simulation | Shiny App
Spark in R & Python
- Lab 67: Time Series with Spark (Modeltime) | Shiny Google Analytics Forecaster
- Lab 66: Spark in Python | PySpark | 4500+ Stocks & Plotly Dash Investment App
- Lab 65: Spark in R | sparklyr | 4500+ Stocks & Shiny Investment App
Time Series: R & Python
- Lab 64: How to Forecast 100 Time Series | In Python (Sktime)
- Lab 63: How to Forecast 100 Time Series | In R (Modeltime)
Special: Time Series Forecasting with Modeltime
- Lab 63: How to Forecast 100 Time Series | Modeltime Nested
- Lab 60: Forecasting Airline Travel & COVID-19 | New Modeltime Features
- Lab 54: Modeltime Recursive Autoregressive Machine Learning | Energy Demand Forecasting
- Lab 53B: Modeltime H2O | Forecasting with H2O AutoML
- Lab 53A: Modeltime GluonTS | SaturnCloud NVIDIA GPU Bonus
- Lab 50: Hierarchical Forecasting LightGBM | Shiny Hierarchical Forecaster App Bonus
- Lab 47: Forecasting with Autoregressive Machine Learning (Recursive) | 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
Customer Analytics Series with R & Python
- Lab 62 (Python): Marketing Mix Modeling (MMM) Optimization | Dash App Bonus
- Lab 61 (R): Automated Marketing Mix Modeling (MMM) | Facebook Robyn | Shiny App Bonus
- Lab 59 (Python): Customer Lifetime Value with Machine Learning | Dash App Bonus
- Lab 58 (R): Customer Lifetime Value with Machine Learning | Shiny App Bonus
Production with Targets
- Lab 57: Targets & Modeltime for Production Forecasting | Automated Forecast Audit Report
- Lab 56: Targets Machine Learning Pipelines | Shiny Customer Churn App (Marketing)
Tidy-Modeling Ecosystem
- Lab 52: Stacks Ensembles | Customer Churn Retention App
- Lab 51: Deep Learning with Torch & Tabnet | Shiny Loan Default Scorer App Bonus | Special Guest: Josh Starmer BAM
- Lab 50: Hierarchical Forecasting LightGBM | Shiny Hierarchical Forecaster App Bonus
- Lab 49: Feature Engineering & Customer Analytics | Special Guest: Max Kuhn | Shiny Customer Explorer Bonus
- Lab 48: NLP for Business | Text Recipes | Shiny AutoNLP Bonus
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 every 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!
Get started now!
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
Private Slack Community
Ask questions, provide feedback, and learn with the community!
Summary of Everything
You get
1-Hour Courses on Advanced Topics
Full Code
Slack Channel Community
Resources (Slides, References, Links, and more)
Get started now!
Frequently Asked Questions
What will be the frequency of new material for this service?
The frequency is 1 screen-cast (1hr + code) every month.
What is the content roadmap & how do you pick topics?
Our topics are driven by our members - they pick the topics. For example, webscraping is a topic we consistently get requests for. This gets added to our list and we do webinars then on it. The roadmap is therefore flexible and driven by the community!
What is the advantage of Learning Labs PRO versus the BSU Courses?
Courses are foundational, project-driven, take weeks to complete, and you gain a ton of knowledge on how many different tools integrate to solve a problem.
Learning Labs are tactical, tool or application focused, and provide short bursts on topics that are smaller in scope but are really important!
This way both the Courses and Learning Labs COMPLIMENT each other. One teaches projects & foundations, the other teaches skills, tools & applications. WIN-WIN!
What if I can't attend LIVE?
That's actually why we started Learning Labs PRO - So you can get the recordings and content even though you may be halfway around the world from us. Now you can get all of it, plus ask questions, plus get more topical training like webscraping, deep learning, domain-specific topics like sales, marketing, and more.
Your Instructor
Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane startups! 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 Curriculum
Welcome to Learning Labs PRO!
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Generative AI for Data Scientists
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Time Series Analysis & Forecasting Series
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StartLab 96: Macroeconomic Factors for Data Science and Forecasting (Special Guest: Jonathan Regenstein) (116:25)
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StartLab 95 (Python): Time Series Forecasting (Feat. Polars and Nixtla MLForecast) (118:58)
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StartLab 94: ARIMA Sales Demand Forecasting in Python (Feat. Nixtla + Positron) (136:59)
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StartSPECIAL EVENT: Introducing Time Series in Python: Pytimetk (Feat. Snowflake) (121:37)
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StartLab 73 (JUST UPGRADED): Time Series Features in R & Python | NEW Timetk for Python (119:35)
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StartLab 85: Cashflow Forecasting (Finance) | Shiny App Excel Forecast Automation (109:32)
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StartLab 63: How to Forecast 100 Time Series | Nested Modeltime (113:02)
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StartLab 60: Airline Covid Forecasting | NEW Modeltime & Modeltime GluonTS Features (93:34)
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StartLab 54: Energy Demand Forecasting | Autoregression Modeltime Recursive (95:16)
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StartLab 53B: Modeltime H2O | Forecasting with H2O AutoML! (63:43)
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StartLab 53A: Modeltime GluonTS Deep Learning | Saturn Cloud GPUs (78:07)
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StartLab 50: Hierarchical Forecasting | Shiny Hierarchical Forecasting App Bonus
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StartLab 47: Forecasting with Autoregressive Machine Learning (Recursive) | Scalable AR(ML) Bonus (86:47)
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StartLab 46: Forecasting at Scale with Modeltime | Nostradamus Lite Shiny Bonus (75:15)
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StartLab 38: Time Series Forecasting | Intro to Modeltime (85:29)