Technology

• A Data Scientist's Resources

Business

• How I Went From A Mechanical Engineer To The Director Of Sales Engineering Using Time Series Forecasting

• R is for Research, Python is for Production

• Hierarchical Time Series Forecasting [Full Code Tutorial]

• 5 Reasons You Should Learn Shiny

• R is for Research, Python is for Production

• Should I Become a Data Scientist or Data Analyst?

• What kind of job can you get if you master machine learning?

• 6 Reasons To Learn R For Business [2021]

• Web Scraping and Analyzing Home Rentals (Python + R)

• Top 5 Best Articles on R for Business [November 2020]

• Docker for Data Science: An Important Skill for 2021 [Video]

• Analyzing Solar Power Energy (IoT Analysis)

• Top 5 Best Articles on R for Business [October 2020]

• Top 5 Best Articles on R for Business [September 2020]

• Using Drake for ETL - Building A Shiny Real Estate App

• How To Get My Company To Pay For My Data Science Courses

• Win Data Science Competitions with Shiny

• From No-Shiny Experience to Deploying My First Shiny App in 3-Months

• How One Student Landed a VP-Level Analytics Role at a Major Bank

• Increase Your Salary With Data Science Skills

• Part 6 - R Shiny vs Tableau (3 Business Application Examples)

• Part 5 - Five Reasons to Learn H2O for High-Performance Machine Learning

• NEW BOOK - The Shiny Production with AWS Book

• Part 4 - Git for Data Science Applications (A Top Skill for 2020)

• Part 1 - Five Full Stack Data Science Technologies for 2020 (and Beyond)

• How I Landed My Data Science Job

• Part 3 - Docker for Data Scientists (A Top Skill for 2020)

• Is Your Job's Data Science Tech Stack Intimidating?

• Part 2 - Data Science with AWS (A Top Skill for 2020)

• Apply Data Science to Improve Addiction Treatment

• Unlocking Blue Oceans with Data Science

• How I Started My Data Science Business

• Excel to R, Part 2 - Speed Up Exploratory Data Analysis 100X (R Code!)

• 2 Critical Factors to Learn Data Science

• Data Science Workflow - The Process for Solving Data Problems

• Build A R Shiny App (Tutorial) - Wedding Risk Model

• How To Become A Financial Data Scientist (Or A Data Scientist In Any Domain)

• Customer Segmentation and RFM Analysis with K-Means Clustering - Part 2

• 3 Simple Rules For Getting Results With Data Science (A Story About Learning From Failure)

• A/B Testing with Machine Learning - A Step-by-Step Tutorial

• Targeted Marketing with Customer Segmentation and RFM Analysis - Part 1

• New Cheat Sheet - Customer Segmentation and Clustering Workflow

• Excel to R, Part 1 - The 10X Productivity Boost

• Marketing Analytics - Fast-Food Promotion Effectiveness

• Time Series Analysis for Business Forecasting with Artificial Neural Networks

• Marketing Analytics and Data Science

• R and Python: How to Integrate the Best of Both into Your Data Science Workflow

• Case Study: How To Build A High Performance Data Science Team

• Agile Framework For Creating An ROI-Driven Data Science Practice

• IML: Machine Learning Model Interpretability And Feature Explanation with IML and H2O

• How To Complete A Kaggle Competition In 30 Minutes - Home Credit Default Challenge

• DALEX: Interpretable Machine Learning Algorithms with Dalex and H2O

• Data Science for Business: 3 Reasons Why You Need the Expected Value Framework

• LIME: Machine Learning Model Interpretability with LIME

• How To Solve 90% of Business Problems with Data Science (The Business Science Problem Framework)

• Information Security: Anomaly Detection and Threat Hunting with Anomalize

• 6 Reasons To Learn R For Business

• Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn

• Predictive Sales Analytics: Use Machine Learning to Predict and Optimize Product Backorders

• HR Analytics: Using Machine Learning to Predict Employee Turnover

• Customer Segmentation Part 3: Network Visualization

• Customer Segmentation Part 2: PCA for Segment Visualization

• Customer Segmentation Part 1: K Means Clustering

• orderSimulatoR: Simulate Orders for Business Analytics

• Marketing Strategy: Why MBAs Can Benefit from Learning Analytics

Investments

• Russell 2000 Quantitative Stock Analysis in R: Six Stocks with Amazing, Consistent Growth

• Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes

Code-Tools

• Make Microsoft Word Reports with R + officedown

• How to improve your storytelling with R

• Why learning Python is hard (but important) for Data Scientists that use R

• How to R code faster with ChatGPT

• ChatGPT: Made this Shiny App in 10 Minutes

• How To Geocode In R For FREE

• Exploratory DataXray Analysis (EDXA)

• How to make a plot with two different y-axis in R with ggplot2? (a secret ggplot2 hack)

• ggradar: radar plots with ggplot in R

• explore: simplified exploratory data analysis (EDA) in R

• ggdensity: A new R package for plotting high-density regions

• modelDown: Automate Explainable AI (Machine Learning) in R

• Survival Analysis in R (in under 10-minutes)

• The Most Overlooked R Package (That Can Get You Through A Data Science Job Interview)

• How I analyze 100+ ggplots at once

• A new R package for Business Analytics... radiant

• Introducing portfoliodown: The Data Science Portfolio Website Builder

• Tidy Time Series Forecasting in R with Spark

• Introducing Iterative (Nested) Forecasting with Modeltime

• Forecasting Many Time Series (Using NO For-Loops)

• Hyperparameter Tuning Forecasts in Parallel with Modeltime

• gghalves: Make Half Boxplot | Half Dotplot Visualizations with ggplot2

• DataEditR: The GUI for Interactive Dataframe Editing in R

• ggside: A new R package for plotting distributions in side-plots

• patchwork: ggplot2 plot combiner

• mmtable2: ggplot2 for tables

• ggplot2 Extension: corrmorrant for Flexible Correlation Plots in R

• Webscraping Tables in R: Datapasta Copy-and-Paster

• Not data.table vs dplyr... data.table + dplyr!

• Introducing Modeltime Recursive: Tidy Autoregressive Forecasting with Lags

• Enhance Your Storytelling - Interactive Slide Decks with Rmarkdown

• Make PDF Data Analysis Reports with R | Rmarkdown Visual Editor

• Build GGPLOT Code with Tableau Drag-and-Drop (R esquisse)

• A Gentle Introduction to R Shiny

• Introducing Modeltime H2O: Automatic Forecasting with H2O AutoML

• Assess Your DATA QUALITY in R with skimr

• DataExplorer: Exploratory Data Analysis in R

• How to Add Shiny to Rmarkdown

• Predictive Power Score vs CorrelationFunnel

• Full Feature Engineering Tutorial with Max Kuhn

• Make Awesome Statistical Plots in R

• 4 Ways to make Data Frames in R!

• Should you learn Python or R in 2021?

• How to Write SQL From R

• How to Handle Missing Data in R with simputation

• R/Python Teams Course Announcement

• How to Make 3D Plots in R

• 30 Year Weather Data Analysis

• 6 Life-Altering RStudio Keyboard Shortcuts

• Plotting Time Series in R (New Cyberpunk Theme)

• Build and Evaluate A Logistic Regression Classifier

• Interactive Principal Component Analysis in R

• How To Make Geographic Map Visualizations In R

• Time Series Demand Forecasting

• Forecasting Time Series ARIMA Models (10 Must-Know Tidyverse Functions #5)

• Detect Relationships With Linear Regression (10 Must-Know Tidyverse Functions #4)

• Python and R - Part 2: Visualizing Data with Plotnine

• 10 Must-Know Tidyverse Functions: #3 - Pivot Wider and Longer

• 10 Must-Know Tidyverse Functions: #2 - across()

• Python and R - Part 1: Exploring Data with Datatable

• 10 Must-Know Tidyverse Functions: #1 - relocate()

• How to Visualize Time Series Data: Tidy Forecasting in R

• How to Automate PDF Reporting with R

• How to Make Publication-Quality Excel Pivot Tables with R

• Introducing Modeltime Ensemble: Time Series Forecast Stacking

• How to Automate Exploratory Analysis Plots

• How to Automate Excel with R

• How to Automate PowerPoint Slidedecks with R

• How to Scrape Word Documents with R

• Time Series in 5-Minutes, Part 6: Modeling Time Series Data

• Time Series in 5-Minutes, Part 5: Anomaly Detection

• Time Series in 5-Minutes, Part 4: Seasonality

• Time Series in 5-Minutes, Part 1: Data Wrangling and Rolling Calculations

• Introducing Modeltime: Tidy Time Series Forecasting using Tidymodels

• Time Series in 5-Minutes, Part 3: Autocorrelation and Cross Correlation

• Time Series in 5-Minutes, Part 2: Visualization with the Time Plot

• Timetk: Visualize Time Series Data (in 1-Line of Code)

• Shiny Real Estate with Zillow API (Free Course)

• Google Trends Email Automation with Shiny

• Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial

• Customer Churn Modeling using Machine Learning with parsnip

• Web Scraping Product Data in R with rvest and purrr

• Cleaning Anomalies to Reduce Forecast Error by 9% with anomalize

• PDF Scraping in R with tabulizer

• Big Data: Wrangling 4.6M Rows with dtplyr (the NEW data.table backend for dplyr)

• Introducing correlationfunnel v0.1.0 - Speed Up Exploratory Data Analysis by 100X

• Python Integration in RStudio - Data Science IDE Review

• Anomaly Detection Using Tidy and Anomalize

• The Tidy Time Series Platform: tibbletime 0.1.0

• Demo Week: Time Series Machine Learning with h2o and timetk

• Demo Week: Tidy Time Series Analysis with tibbletime

• Demo Week: Tidy Forecasting with sweep

• Demo Week: Time Series Machine Learning with timetk

• Demo Week: class(Monday) <- tidyquant

• It's tibbletime v0.0.2: Time-Aware Tibbles, New Functions, Weather Analysis and More

• It's tibbletime: Time-Aware Tibbles

• alphavantager: An R interface to the Free Alpha Vantage Financial Data API

• BizSci Package Updates: Formerly timekit... Now timetk :)

• sweep: Extending broom for time series forecasting

• timekit: New Documentation, Function Improvements, Forecasting Vignette

• timekit: Time Series Forecast Applications Using Data Mining

• tidyquant 0.5.0: select, rollapply, and Quandl

• tidyquant Integrates Quandl: Getting Data Just Got Easier

• tidyquant 0.4.0: PerformanceAnalytics, Improved Documentation, ggplot2 Themes and More

• tidyquant 0.3.0: ggplot2 Enhancements, Real-Time Data, and More

• Speed Up Your Code Part 2: Parallel Processing Financial Data with multidplyr + tidyquant

• tidyquant 0.2.0: Added Functionality for Financial Engineers and Business Analysts

• tidyquant: Bringing Quantitative Financial Analysis to the tidyverse

• Speed Up Your Code: Parallel Processing with multidplyr

Financial-Analysis

• Recreating RView's ''Reproducible Finance With R: Sector Correlations''

Technical-Papers

• tidyquant: New Tools for Performing Financial Analysis within the Tidy Ecosystem

Presentations

• EARL Presentation on HR Analytics: Using ML to Predict Employee Turnover

• LIVE DataTalk on HR Analytics Tonight: Using Machine Learning to Predict Employee Turnover

• Business Science EARL SF 2017 Presentation: tidyquant, timekit, and more!

• tidyquant: R/Finance 2017 Presentation

Timeseries-Analysis

• Time Series Analysis: KERAS LSTM Deep Learning - Part 2

• Time Series Analysis: KERAS LSTM Deep Learning - Part 1

• Tidy Time Series Analysis, Part 4: Lags and Autocorrelation

• Tidy Time Series Analysis, Part 3: The Rolling Correlation

• Tidy Time Series Analysis, Part 2: Rolling Functions

• Tidy Time Series Analysis, Part 1

Learning-R

• 3 Free Resources to Learn R - Now Open

• Introducing the Ultimate R Cheat Sheet Version 2.0: The Shinyverse

• Data Science In R - The Ultimate R Cheat Sheet - The Ultimateness Just Doubled!

• R Cheat Sheet: Data Science Workflow with R

• How To Learn R, Part 1: Learn From A Master Data Scientist's Code

University

• Course Launch: Python for Data Science Automation in 7 Days!

• Time Series Forecasting Course - Now Available

• Course Launch: High-Performance Time Series Forecasting in 7 Days!

• Going to rstudio::conf? Meet Business Science, Accelerate Your Career

• Expert Shiny Developer with AWS - Course Launch Today!

• Course Launch - Expert Shiny Developer with AWS in 7 Days!

• Building APIs with plumber - New Learning Lab 1-Hour Course

• Predictive Web Applications for Business with R Shiny - Course is Open!!!

• Finance in R with tidyquant - New Learning Lab 1-Hour Course

• New Learning Lab 1-Hour Course - Build A Strategic Database With Web Scraping

• Data Science Course Launch Today - Business Analysis With R Now Open!

• Course Launch - Business Analysis With R in 8 Days!

• Data Science With R Course Series - Week 10

• Data Science With R Course Series - Week 9

• Data Science With R Course Series - Week 8

• Data Science With R Course Series - Week 7

• Data Science With R Course Series - Week 6

• Data Science With R Course Series - Week 5

• Data Science With R Course Series - Week 4

• Data Science With R Course Series - Week 3

• Data Science With R Course Series - Week 2

• Data Science With R Course Series - Week 1

• New Course Content: DS4B 201 Chapter 7, The Expected Value Framework For Modeling Churn With H2O

• Data Science For Business: Course Now Open!

• Data Science For Business: Course Launch In 5 Days!!!

Business-Science-Labs

• Algorithmic Trading: Using Quantopian's Zipline Python Library In R And Backtest Optimizations By Grid Search And Parallel Processing

Press-Releases

• Press Release: Business Science Partners With Method Data Science To Accelerate Your Data Science Career

Learning-Python

• Ultimate Python Cheat Sheet: Data Science Workflow with Python

Time-Series

• Time Series Machine Learning (and Feature Engineering) in R

• Strategies To Improve Your Business Forecasting 50% (or more) | Time Series Learning Lab

Finance

• Finance in R - Evaluating American Funds Portfolio

• tidyquant v1.0.0: Pivot Tables, VLOOKUPs in R

• R for Excel Users: Pivot Tables, VLOOKUPs in R

• Tidy Discounted Cash Flow Analysis in R (for Company Valuation)

Learn-R

• How to Set Up TensorFlow 2 in R in 5 Minutes (BONUS Image Recognition Tutorial)

• How to Set Up Python's Scikit-Learn in R in 5 minutes

python

• 3 Ways to Read Multiple CSV Files: For-Loop, Map, List Comprehension

• SweetViz: Automated Exploratory Data Analysis (EDA) in Python

• sklearn: Make your first linear regression model in Python [Video]

• plotnine: Make great-looking correlation plots in Python

• Siuba: Data wrangling with dplyr in Python

• Pandas Profiling: Make Exploratory Data Analysis Reports

R

• My 4 most important explainable AI visualizations (modelStudio)

• How to Make a Heatmap in R

• Tidy Parallel Processing in R with furrr

• ggalt: Make a Lollipop Plot to Compare Categories in ggplot2

• ggalt: Make a Dumbbell Plot to Visualize Change in ggplot2

• ggforce: Make a Hull Plot to Visualize Clusters in ggplot2

• ggdist: Make a Raincloud Plot to Visualize Distribution in ggplot2

• easystats: Quickly investigate model performance

• Gentle Introduction to Forecasting with Modeltime [Video Tutorial]

• grafify: Make 5 powerful ggplot2 graphs quickly with R

Careers

• What is the Career Path for a Data Scientist? (From $75,000 to $150,000 salary in 1-year)

• The 14 most important data science skills (To get a $50,000 increase in salary)

tbs-newsletter

• TBS 002: The Insider Secrets Of Data Science Consulting

• TBS 001: The D.A.A. Method