Internal Capacity, External Demand, and the Metrics of Consumption
In my blogs, I often distinguish between event data and metrics. I usually say something to the effect that events help to explain the metrics – or events “prov...
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The Twilight Zone Between True and False
Recently we read a lot about fake news, alternate facts and journalism lies. Companies like Facebook develop data science algorithms to detect these postings, based amon...
The Mathematics of Machine Learning
Guest blog post by Wale Akinfaderin, PhD Candidate in Physics. In the last few months, I have had several people contact me about their enthusiasm for venturing into t...
DSC Webinar Series: How to Keep Your R Code Simple While Tackling Big Datasets
R, TERR, Spark and Python are tools that benefit from larger systems. Software-Defined Servers enable data scientists to size their processing system to the size of a par...
How Uber Depends on Data Analytics to Deliver Extreme Customer Service – Face To Face With Uber’s Chief Data Architect
From a simple limo hailing app for friends to the world’s go-to taxi app. Uber’s growth in the approximately 7 years of existence can be described by one word, “Phe...
Indicator Based Recommenders – The One We Missed
Summary: In our recent article on “5 Types of Recommenders” we failed to mention Indicator-Based Recommenders. These have some unique features and ease of impleme...
Data Engineering vs. Data Science Infographic
If you’re interested in the field of analytics, you’ve probably heard the terms Data Engineering and Data Science, but do you know the difference? Although th...
Selecting Forecasting Methods in Data Science
We are dealing with plethora of data and information in the world today and expectation is to predict and forecast how we can gain competitive advantage based on the info...
A Quick Guide on How to Prevail in the Graph Database Arena
Introduction There are endless discussions on the databases arena about which DBMS is best suited for operational or data warehousing analytics, which one is the most eff...
A Discussion: IT Data, Ambiguities & Classification model performance
“Ambiguity is pervasive” – true to its definition, as increasingly data getting generated, system connectivity reaching its peak, data and outcome are diverging. IT...