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

Our Expertise


See Us In Person


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What's New


Latest Insights
    Time Series Deep Learning: Forecasting Sunspots With Keras Stateful LSTM In R
    Written by Matt Dancho on April 18, 2018

    Time series prediction (forecasting) has experienced dramatic improvements in predictive accuracy as a result of the data science machine learning and deep learning evolution. As these ML/DL tools have evolved, businesses and financial institutions are now able to forecast better by applying these new technologies to solve old problems. In this article, we showcase the use of a special type of Deep Learning model called an LSTM (Long Short-Term Memory), which is useful for problems involving sequences with autocorrelation. We analyze a famous historical data set called “sunspots” (a sunspot is a solar phenomenon wherein a dark spot forms on the surface of the sun). We’ll show you how you can use an LSTM model to predict sunspots ten years into the future with an LSTM model.

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    anomalize: Tidy Anomaly Detection
    Written by Matt Dancho on April 8, 2018

    We recently had an awesome opportunity to work with a great client that asked Business Science to build an open source anomaly detection algorithm that suited their needs. The business goal was to accurately detect anomalies for various marketing data consisting of website actions and marketing feedback spanning thousands of time series across multiple customers and web sources. Enter anomalize: a tidy anomaly detection algorithm that’s time-based (built on top of tibbletime) and scalable from one to many time series!! We are really excited to present this open source R package for others to benefit. In this post, we’ll go through an overview of what anomalize does and how it works.

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    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.

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    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!

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    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.

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