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.


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.


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
    How To Learn R, Part 1: Learn From A Master Data Scientist's Code
    Written by Matt Dancho on March 3, 2018

    The R programming language is a powerful tool used in data science for business (DS4B), but R can be unnecessarily challenging to learn. We believe you can learn R quickly by taking an 80/20 approach to learning the most in-demand functions and packages. In this article, we seek to ultimately understand what techniques are most critical to a beginners success through analyzing a master data scientist’s code base. Half of this article covers the web scraping procedure (using rvest and purrr) we used to collect our data (if new to R, you can skip this). The second half covers the insights gained from analyzing a master’s code base. In the next article in our series, we’ll develop a strategic learning plan built on our knowledge of the master. Last, there’s a bonus at the end of the article that shows how you can analyze your own code base using the new fs package. Enjoy.

    The Tidy Time Series Platform: tibbletime 0.1.0
    Written by Davis Vaughan on January 4, 2018

    We’re happy to announce the third release of the tibbletime package. This is a huge update, mainly due to a complete rewrite of the package. It contains a ton of new functionality and a number of breaking changes that existing users need to be aware of. All of the changes have been well documented in the NEWS file, but it’s worthwhile to touch on a few of them here and discuss the future of the package. We’re super excited so let’s check out the vision for tibbletime and its new functionality!

    Six Reasons To Learn R For Business
    Written by Matt Dancho on December 27, 2017

    Data science for business (DS4B) is the future of business analytics yet it is really difficult to figure out where to start. The last thing you want to do is waste time with the wrong tool. Making effective use of your time involves two pieces: (1) selecting the right tool for the job, and (2) efficiently learning how to use the tool to return business value. This article focuses on the first part, explaining why R is the right choice in six points. Our next article will focus on the second part, learning R in 12 weeks.

    Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn
    Written by Matt Dancho on November 28, 2017

    Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We’re super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it’s equally important to explain what features drive the model, which is why we’ll use the lime package for explainability. We cross-checked the LIME results with a Correlation Analysis using the corrr package. We’re not done yet. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you’re interested in applying Deep Learning in R! We are so let’s get going!!

    EARL Presentation on HR Analytics: Using ML to Predict Employee Turnover
    Written by Matt Dancho on November 6, 2017

    The EARL Boston 2017 conference was held November 1 - 3 in Boston, Mass. There were some excellent presentations illustrating how R is being embraced in enterprises, especially in the financial and pharmaceutical industries. Matt Dancho, founder of Business Science, presented on using machine learning to predict and explain employee turnover, a hot topic in HR! We’ve uploaded the HR Analytics presentation to YouTube. Check out the presentation, and don’t forget to follow us on social media to stay up on the latest Business Science news, events and information!