Data Science For Business: Course Now Open!

    Written by Matt Dancho on April 30, 2018

    We are pleased to announce that our Data Science For Business (#DS4B) Course (HR 201) is OFFICIALLY OPEN! This course is for intermediate to advanced data scientists looking to apply H2O and LIME to a real-world binary classification problem in an organization: Employee Attrition. If you are interested applying data science for business in a real-world setting with advanced tools using a client-proven system that delivers ROI to the organization, then this is the course for you. For a limited time we are offering 15% off enrollment.

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    Data Science For Business: Course Launch In 5 Days!!!

    Written by Matt Dancho on April 25, 2018

    Last November, our data science team embarked on a journey to build the ultimate Data Science For Business (DS4B) learning platform. We saw a problem: A gap exists in organizations between the data science team and the business. To bridge this gap, we’ve created Business Science University, an online learning platform that teaches DS4B, using high-end machine learning algorithms, and organized in the fashion of an on-premise workshop but at a fraction of the price. I’m pleased to announce that, in 5 days, we will launch our first course, HR 201, as part of a 4-course Virtual Workshop. We crafted the Virtual Workshop after the data science program that we wished we had when we began data science (after we got through the basics of course!). Now, our data science process is being opened up to you. We guide you through our process for solving high impact business problems with data science!

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    Time Series Deep Learning, Part 1: 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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    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!!

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