Zillow is a FREE TOOL with an API that allows Data Scientists to connect to a massive repository of Home Prices and Features. In this lab, we use Shiny, Crosstalk, and Zillow API to create an ML-powered Zillow Home Price Explanation tool.

I can't tell you how excited I am to be a sponsor at rstudio::conf this year. This is my 2nd year attending, and my first as a sponsor. It's an amazing honor. And, I'm here to help you accelerate your career.

Google Trends is a FREE tool to gain insights about Google Search Terms your organization cares about. What if you could streamline the process of analyzing keyword search trends and emailing a report in 3-4 seconds? Learn how to do just that.

Learn how to model product prices using the tune library for hyperparameter tuning and cross-validation.

H2O is the scalable, open-source ML library that features AutoML. Here's why it's an essential library for me (and you).

The enterprise-grade process for deploying, hosting, and maintaining Shiny web applications using AWS, Docker, and Git.

Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. A key skill that companies need is Git for application development (I call this Full Stack Data Science). Here's what is driving Git's growth, and why you should learn Git for data science application development.

Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. Here are the essential skills for Data Scientists that need to build and deploy applications in 2020 and beyond.

Getting a job in Data Science is difficult. Here's how one Business Science student aced his Data Science Interview and landed a job at a top Management Consulting Firm.

Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. Here's how Docker plays a part in the essential skills of 2020.

Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.

A data science team has many tools that all need to be integrated. And, this can be INTIMIDATING. Here are some tips to deal with the complexity of a data science tech stack.