Red Ventures Data Science & Engineering

Linear Algebra for Machine Learning

Foundational ideas, musings, and examples

How to Make Good Decisions

Using bandit policies to efficiently learn as you go

Who We Are — RV Data Science

Medium only allows one person in the “author” slot, but this post was a group effort among many people…

Create and Deploy a Machine Learning Model Pipeline in Spark

As data scientists at Red Ventures, we define ourselves as Type B data scientists, which differs from the role of business intelligence or data analysts. Besides providing business insights to drive actionable items, we are…

The 2019 Queen City Hackathon: A Personal Account

How to do Cost-Sensitive Learning

Be right in classification modeling when it matters most

Securing MicroServices

At RedVentures we have been benefiting from migrating our architectures to MicroServices for a few years. These fine…

My First Foray into Spark MLlib

As a data science team that’s always working to increase efficiency, especially when it comes to pushing…

Conversations at rstudio::conf 2018

“Never meet your heroes, unless they write R”

Queen City Hackathon — Developing an Interactive Shiny Application to Benefit the City of Charlotte

Working with Spatial Databases and R

And how to trick your database into using the index.

Strata NYC 2017 and the State of Data Engineering

One of the great things about working for Red Ventures is the investment they make in the development of their employees. Engineers are provided with plenty of resources for learning and development, as well as the opportunity to attend…

Overhauling our Data Warehouse with Data Vault

How we used data vault to create a backroom processing layer in our data warehouse architecture.

At Red Ventures, we model our analytic data in a Kimball-style dimensional model that is queried by analysts and used by SSAS…

Business value of Polyglot programming

Many years ago, after working with PHP for about 8 years, I was pushed to start development…