A Data Science Central Community
In a previous article, we defined data charaterization as a “methodology for generating descriptive parameters that describe the behavior and characteristics of a data item, for use in any unsupervised learning algorithm to find features, clusters, patterns, and trends in the data without the bias of incorporating class…Continue
Added by Kirk Borne on May 31, 2014 at 11:30am — No Comments
OLAP is an important constituent part of BI(business intelligence).
Understood literally, OLAP is online analytical processing, that is, users conduct analytical operation on real-time business data.
But, currently the concept of OLAP is seriously narrowed, and only it refers to operations such as conducting drilling,…Continue
Added by Jessica May on May 27, 2014 at 8:00pm — No Comments
A story that has been making the news recently and got me thinking about the widening reach of data science involves a rather shadowy US organisation called the National Security Agency (NSA). These guys have a mandate to gather data that enemies of the state would like to keep secret. Unfortunately the NSA have been rather over-zealous in their pursuit of information and have wound up gathering data, that was supposed to be private, from the likes of Google, Facebook, Microsoft, Skype,…Continue
A recent study by Accenture talks about the future state of banking in US by 2020. Thankfully, the study reports, US banks have emerged from the travails of a battered economy. Two important findings from the study stand out.
While we can debate the findings, the current activity stream at banks does indicate that…Continue
Added by Naagesh Padmanaban on May 25, 2014 at 9:00pm — No Comments
Business Analyst is now what organizations are talking about these days. Do we really understand what business analyst does; in my opinion business may understand Business Analytics however person responsible for it may not understand it in full at any level across all organizational domain. Let me sight few examples across domains to explain it.
1) Human Resource Team during Business Analyst recruitment
The great myth of Big Data is that it’s defining characteristic is size. In spite of the warnings about Variety and Velocity, in addition to every other V-word out there, the world has been obsessively focused on the collection of bigger and bigger data. But the key to extracting valuable information from data isn’t actually the size of the database, but your ability to make the most out of the data that you have.
In the NFL, scouts don’t simply identify talent by…Continue
Added by Radhika Subramanian on May 12, 2014 at 1:00pm — No Comments
In today’s blog post, we shall look into time series analysis using R package – forecast. Objective of the post will be explaining the different methods available in forecast package which can be applied while dealing with time series analysis/forecasting.
A time series is a collection of observations of well-defined data items…Continue
As an Applied Statistician with Bayesian background ["Bayesian Inference in Life-Testing," Ph.D. 1968, Dept. of Math., Indian Institute of Technology, Kharagpur , India] I would like to add some views in this topic of Data and Data Science. Living and working in USA for last few decades as given me some experience and exposures to different phases of data development. Earlier there was not much available Data and we had to go after it…Continue
I just did a quick computation. Let's say that you are born in extreme poverty, and your net worth (yearly revenue and assets) is just $1 by the time you reach 20 years old. Let's say you think and work (on average) 1% faster than the average guy - in short producing just 1% more valuable output than the average guy, each day. Let's say that you leverage half of this advantage for your own growth (to produce compound return), and the other half for your quality of life (nice restaurants and…Continue