In many of my presentations and lectures, I have made the following declaration: In Big Data, it isn’t the volume of data that’s interesting, it’s the granularity; ...
This infographic was produced by 365DataScience. Last year they completed a research on 1,001 data scientists to get a profile of the ‘typical’ data scientist in 201...
This article was written by James Le. Here is a brief summary. Link to the full article is provided at the bottom. Some techniques are not mentioned in Le’s article...
Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new t...
At first glance, data science and customer experience (CX) don’t have much in common. Data scientists like me are typical left-brained thinkers: detail-oriented, logica...
What if you had a corporate asset that was never used? Your first response might be that corporate asset obviously has no value. And you’d probably be right. Howeve...
The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify this concept. The post is part of my forthcomi...
Leveraging the benefits of effective data preparation to help build a modern ERP system is a vital component in innovating an organization’s data workflow systems. ...
Originally posted by Michael Grogan. One of the big issues when it comes to working with data in any context is the issue of data cleaning and merging of datasets, since...
Because of the advancements in machine-learning based image recognition and AI-driven optimization, the influence of big data in the context of computer science is given ...