Publications by business-science.io - Articles
The Tidy Time Series Platform: tibbletime 0.1.0
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 touc...
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The Tidy Time Series Platform: tibbletime 0.1.0
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 touc...
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How To Learn R, Part 1: Learn From A Master Data Scientist’s Code
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 begi...
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Data Science For Business: Course Now Open!
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...
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anomalize: Tidy Anomaly Detection
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 mu...
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Time Series Deep Learning: Forecasting Sunspots With Keras Stateful LSTM In R
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 a...
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Data Science For Business: Course Launch In 5 Days!!!
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 h...
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Algorithmic Trading: Using Quantopian’s Zipline Python Library In R And Backtest Optimizations By Grid Search And Parallel Processing
We are ready to demo our new new experimental package for Algorithmic Trading, flyingfox, which uses reticulate to to bring Quantopian’s open source algorithmic trading Python library, Zipline, to R. The flyingfox library is part of our NEW Business Science Labs innovation lab, which is dedicated to bringing experimental packages to our followe...
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Information Security: Anomaly Detection and Threat Hunting with Anomalize
Information Security (InfoSec) is critical to a business. For those new to InfoSec, it is the state of being protected against the unauthorized use of information, especially electronic data. A single malicious threat can cause massive damage to a firm, large or small. It’s this reason when I (Matt Dancho) saw Russ McRee’s article, “Anomaly...
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Data Science For Business Tutorial: Using Machine Learning With LIME To Understand Employee Churn
Data science tools are getting better and better, which is improving the predictive performance of machine learning models in business. With new, high-performance tools like, H2O for automated machine learning and Keras for deep learning, the performance of models are increasing tremendously. There’s one catch: Complex models are unexplainable�...
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