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January 2015 Blog Posts (62)

Weekly Digest, January 26

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. 

Sponsored Announcement…

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Added by Vincent Granville on January 21, 2015 at 11:30am — No Comments

Poster about data science

High resolution version can be found via www.icris.nl or here.

Added by Linda Terlouw on January 21, 2015 at 11:00am — No Comments

Actionable HR analytics

Human resources analytics can provide businesses the keys to improving processes, reducing workforce costs and making the right policy changes to improve efficiency. Leaders in the space and speakers from the upcoming…

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Added by Alesia on January 20, 2015 at 12:00pm — No Comments

The impact of asking the wrong question

For any data science project, if you start with the wrong question, you are bound to end up with the wrong answer, and fail. Who should identify the right question? I believe data scientists should be involved in the process, otherwise, they will be held responsible for the failure.

CDC headquarters in Druid Hills,…

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Added by Vincent Granville on January 19, 2015 at 6:30pm — No Comments

Latest Project for my current Coursera course is published

I thought I would share it on here in case anyone is interested.

The project can be found here: http://rpubs.com/Mandypar/54270

And the course I am doing is described here:  https://www.coursera.org/course/repdata

It would be fun to go into more detail for this, but I have more than filled the project brief and have run out…

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Added by Mandy Parmenter on January 19, 2015 at 9:34am — No Comments

The 5 Essential Skills Any Data Scientist Needs

Guest blog post by Bernard Marr.

In my last post, I explained the difference between what I consider the two core types of data scientist – strategic and operational.

Broadly speaking, they require many of the same skillsets – but the distribution of your expertise and experience within these skillsets will vary, depending on whether…

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Added by Vincent Granville on January 18, 2015 at 2:39pm — 3 Comments

Structural Data Objects

In my previous blog on the Hopscotch and Robots simulation environment, I discussed the use of structural data extracted from hypothetical and real-life organizational events. In the current blog, I will be briefly covering conceptual issues more focused on the nature of the structural data itself including its theoretical significance.

Structural data holds information about the relationship between events.…

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Added by Don Philip Faithful on January 18, 2015 at 8:10am — No Comments

Bigdata Analytics and Supply chain management

     I am a newbie to Bigdata and would like to highlight some significant advantages if incorporated in a company's supply-chain management strategies, expecting the reader's views and suggestions.

     Because, in recent past I have developed a online supply-chain management systems in which sellers and customers are matched using an algorithm. It acted as a decision support system and I needed to dig deeper on the available data to get more insights over the data pattern (even for…

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Added by VINU KIRAN .S on January 17, 2015 at 6:30am — 5 Comments

The Thrill of the [Data Science] Chase

Data Scientists are a unique group. We come from a variety of backgrounds with a unique set of life experiences. At Booz Allen our data science team is now 500+ strong. We have the people you would expect – those with degrees in math, stats, and physics. We also have a forester (me), a jazz musician, and a computer graphics major. There’s no set approach or ‘one size fits all’ model. We all took a different path to get to where we are today, but we all share one common trait – we never get…

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Added by Steven Mills on January 17, 2015 at 6:00am — No Comments

The Cognitive Cost of Switching Technology Stacks

Originally posted on my blog.

I do kinda feel like my head is full! 

 My context switching penalty is high and my process isolation is not what it used to be.
-          Elon MuskReddit AMA, Jan 5, 2015
 
Cognitive load is a term applied to the overall effort used in working memory for an…
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Added by Christopher Hogue on January 15, 2015 at 2:30pm — No Comments

Drake, AC/DC, and...Steely Dan?! Twitter Reacts to Coachella 2015

Music fans with long memories probably recall the lamentable fiasco that was Woodstock 1999. In contrast to its '60s antecedent, Woodstock '99 was a cravenly commercial venture that replaced Jimi Hendrix and Janis Joplin with Kid Rock and Godsmack. Temperatures hovered in triple digits. Food and water were in short supply. Candles distributed for an anti-gun vigil were used to start plastic bottle bonfires. Limp Bizkit was prominently featured. 

It was the worst of…

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Added by Justin Tenuto on January 14, 2015 at 4:30pm — No Comments

Weekly Digest, January 19

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. 

Featured

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Added by Vincent Granville on January 14, 2015 at 4:00pm — No Comments

VC investment analytics with data visualization

In this article we perform analytics on a huge dataset available from https://www.pwcmoneytree.com. PWC Money tree provides 20 years of  Venture capital investment data from 1995 onward. Having data that goes far into the history should give us enough to extract the necessary analytical juice out of it.

VC Investment in Billions from 1995 through 2014

The year 2000 was…

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Added by Nilesh Jethwa on January 14, 2015 at 9:46am — No Comments

The Problem with Recommenders

Eve recommended an apple to Adam, and they shared the first buyer’s remorse.

 

Water, Water Everywhere

Recommender systems select products for customers based on past experience. Sometimes the product choices are called “items” or “content”, and other nouns are substituted for the customer. The most familiar case is that of…

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Added by Michael Bryan on January 14, 2015 at 8:08am — 1 Comment

The Changing Nature of Predictive Analytics in the Enterprise

Today, an increasing number of institutional clients are looking for solutions, strategies and roadmaps to implement Big Data and Predictive Analytics initiatives within their own organizations. While the exact nature of the solutions and recommendations may differ from client to client, based on a number of factors, like the industry they operate in, the size of their operations and business model, there are common threads that can be applied to their needs.

While looking for these…

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Added by Mark Sharma on January 13, 2015 at 8:57pm — 2 Comments

Get Started with the Data Science Bowl

We’ve created a Domino project with starter code in R and Python for participating in the Data Science Bowl. 

Get a jump start in the competition with our starter project by training your models on massive hardware and running multiple experiments in parallel while keeping track of…

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Added by Anna Anisin on January 13, 2015 at 3:00pm — No Comments

In Big Data, Preparing the Data is Most of the Work



A common misconception about Big Data is that it is a black box: you load data and magically gain insight. This is not the case. As this New York Times article “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights” describes, loading a big data platform with quality data with enough…

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Added by Sullexis LLC on January 13, 2015 at 11:43am — 1 Comment

Analytics Business in 2014 - A review

It is the time of the year when businesses take stock of their performance in the year that went by and also see what the New Year may bring in. How did the Analytics & Insights business fare in 2014 and what are the learning that can help us forecast performance in 2015? In review I think 2014, in spite of some hiccups, has been a great year for the business. Definitely the industry experienced greater acceptance of analytics across verticals as a vital tool in helping organizations…

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Added by Naagesh Padmanaban on January 12, 2015 at 7:13pm — No Comments

Data Science - learn R or Python?

Hi Folks,

I have a query around whether to learn R from scratch or should I leverage my basic python knowledge to extend into Data Science with scikit,numpy ,pandas? So I am bit confused ... I am not shy to learn New programming language like R etc bur really need to know who edges out whom in market. Maybe i should learn R too along with Python so  your valuable opinion matters.

            Also i am playing around with IBM's MessageSight product for Internet of things so…

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Added by Perminder Singh on January 12, 2015 at 10:09am — 8 Comments

Temporal Databases: Why you should care and how to get started (Part 3 of 3)

This is the third article in a series.  The first article is available here. The second article is available here.

Not every database requires a temporal database implementation, but some do. We can help…

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Added by Sullexis LLC on January 12, 2015 at 8:00am — No Comments

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