Data Science

Understanding ELK: Elastic, Logstash, and Kibana - Part 1

  Useful links: pfSense Forum Topic (doesn’t seem to be maintained) I currently work for a big data company.  We take machine data, or digital exhaust, and turn it into searchable, intelligible, information for analytics and visualization.  As an vendor knows, there are always competitors whose names come up often in conversation.  One such competitor, Elastic, […]

Kevin Rose Launches New Website

I first learned of Kevin Rose (@kevinrose) from watching The Screen Savers on TechTV in the US.  Kevin is still a young man, but I remember seeing this really young kid along with so many other talented geeks telling us about technology news and showing us cool ways of hacking things and how to build great […]

Data vs Insight: Which Do You Want?

I have many conversations with people on a regular basis regarding the collection, processing, and dissemination of data.  There are so many sources of data in our world today and each of these sources are so noisy and fighting for our constant attention.  From our smart watches to our enterprise systems, we have a need […]

Predictive Analytics vs Intuition

The attached article from TechRepublic asks the reader which they would trust more, predictive Analytics, or their gut. I would say this is a lot like police work. Once you’ve walked a beat long enough, you get to know the neighborhood. You get a feel for the people, the spots with the most questionable activity, […]

Angry Spouse-o-meter

This article speaks to an experiment being conducted by USC in which wearable technology and machine learning are being used together to detect when conflicts arise within a couple’s relationship.  At this point, they are gathering data and not forcing environmental elements in order to make a conflict arise, but as you can imagine, there […]

Recommeded Reading: Neural Networks Made Easy

As I have stated before, I am no data scientist and I am doing my best to get up to speed on the technologies and terminology, but there is so much to learn.  I really appreciate  Ophir Tanz (@OphirTanz), and Cambron Carter, for writing the included article.  Please visit the link at the end of this […]

Introduction to Machine Learning: Part 4

I have talked some about the generalities of machine learning and data science.  We have gone into some level of detail on the types of learning: supervised, unsupervised, reinforcement (not yet discussed, but coming). While these are interesting topics, I really want to go back a little in the process and focus on data munging, […]

Recommended Reading: Big Data Has to Make Sense to People

Nice article from LinkedIn on the merits of making big data understandable. Those of us who ho live in this world have so much more clarity. It is difficult sometimes to see that many people do not understand the value of their own data. Great points.

Introduction to Machine Learning: Part 3

In this post I wanted to dive into a little more detail on supervised versus unsupervised machine learning.  Basically, as described in the last post in this series, the focus has to do with the variables that are known and can be observed.  The detail below comes from Dataconomy.  I’ll provide the link at the end […]

Introduction to Machine Learning : Part 2

This post might require a cup of coffee. I want to take time to explain a few terms as we move forward in our machine learning series, discuss some common algorithms, and speak to data wrangling, or data munging. I’ll start with the topic of data wrangling since it is one of the first obstacles […]