Business Science



"ROI-Driven Data Science"

Applying Data Science to Business & Financial Analysis

How We Help


Data Scientists

You know data science and machine learning, but you could use some help building on your knowledge by adding specialized, topical experts.

We add subject-matter experts (SME) in data science for Digital Marketing, HR, Sales, Logistics and more to compliment your skill set.

Executives

You're interested in utilizing predictive analytics as a competitive advantage but don't have a data science team established.

We are your bolt-on data science team! We'll develop predictive models to solve the most complex business problems while understanding and fitting seamlessly into your organization.

Benefit


We Are

High touch, flexible, nimble

We Have

Top notch experts that use data to return value

You Get

No headache, only results

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Latest Insights
    Sales Analytics: How to Use Machine Learning to Predict and Optimize Product Backorders
    Written by Matt Dancho on October 16, 2017

    Sales, customer service, supply chain and logistics, manufacturing… no matter which department you’re in, you more than likely care about backorders. Backorders are products that are temporarily out of stock, but a customer is permitted to place an order against future inventory. Back orders are both good and bad: Strong demand can drive back orders, but so can suboptimal planning. The problem is when a product is not immediately available, customers may not have the luxury or patience to wait. This translates into lost sales and low customer satisfaction. The good news is that machine learning (ML) can be used to identify products at risk of backorders. In this article we use the new H2O automated ML algorithm to implement Kaggle-quality predictions on the Kaggle dataset, “Can You Predict Product Backorders?”. This is an advanced tutorial, which can be difficult for learners. We have good news, see our announcement below if you are interested in a machine learning course from Business Science. If you love this tutorial, please connect with us on social media to stay up on the latest Business Science news, events and information! Good luck and happy analyzing!

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    It's tibbletime v0.0.2: Time-Aware Tibbles, New Functions, Weather Analysis and More
    Written by Davis Vaughan on October 8, 2017

    Today we are introducing tibbletime v0.0.2, and we’ve got a ton of new features in store for you. We have functions for converting to flexible time periods with the ~period formula~ and making/calculating custom rolling functions with rollify() (plus a bunch more new functionality!). We’ll take the new functionality for a spin with some weather data (from the weatherData package). However, the new tools make tibbletime useful in a number of broad applications such as forecasting, financial analysis, business analysis and more! We truly view tibbletime as the next phase of time series analysis in the tidyverse. If you like what we do, please connect with us on social media to stay up on the latest Business Science news, events and information!

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    HR Analytics: Using Machine Learning to Predict Employee Turnover
    Written by Matt Dancho on September 18, 2017

    Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques. First, we’ll use the h2o package’s new FREE automatic machine learning algorithm, h2o.automl(), to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we’ll use the new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots. We can’t stress how excited we are to share this post because it’s a much needed step towards machine learning in business applications!!! Enjoy.

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    It's tibbletime: Time-Aware Tibbles
    Written by Davis Vaughan on September 7, 2017

    We are very excited to announce the initial release of our newest R package, tibbletime. As evident from the name, tibbletime is built on top of the tibble package (and more generally on top of the tidyverse) with the main purpose of being able to create time-aware tibbles through a one-time specification of an “index” column (a column containing timestamp information). There are a ton of useful time functions that we can now use such as time_filter(), time_summarize(), tmap(), as_period() and time_collapse(). We’ll walk through the basics in this post.

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    alphavantager: An R interface to the Free Alpha Vantage Financial Data API
    Written by Matt Dancho on September 3, 2017

    We’re excited to announce the alphavantager package, a lightweight R interface to the Alpha Vantage API! Alpha Vantage is a FREE API for retreiving real-time and historical financial data. It’s very easy to use, and, with the recent glitch with the Yahoo Finance API, Alpha Vantage is a solid alternative for retrieving financial data for FREE! It’s definitely worth checking out if you are interested in financial analysis. We’ll go through the alphavantager R interface in this post to show you how easy it is to get real-time and historical financial data. In the near future, we have plans to incorporate the alphavantager into tidyquant to enable scaling from one equity to many.

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