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Using Historical Data in the Age of Real Time Decision Making

Guest blog post by Mike Davie.

Dangers and Missed Opportunities: Using Historical Data in the Age of Real Time Decision Making

For many companies going down the big data path, collecting data for its own sake has been a costly exercise with varying degrees of ROI. 

Historic data is great for understanding past events, but as companies mature, their big data efforts move to become more central to business operations: from…

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Added by Vincent Granville on April 24, 2015 at 9:36am — No Comments

Analysis of Insurance Analytics Careers & Job Postings

As part of a deeper dive into domain specific analytic careers, we are exploring the Insurance industry. The below data & charts gives context & explains the opportunities for an analytics careers in Insurance.

Our first examination involved analyzing the top insurance companies hiring for analytics related positions.  The below chart shows the open job postings where the title was “Data Science” or “Analytics” or “Actuary” & plural or related word. We found a total of 185…

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Added by Vozag on April 24, 2015 at 1:44am — No Comments

Biostatistics Careers for Data Scientists

Analytics is becoming critical in all part of our lives. Biostatistics has been a big driver of this analytics demand in the field of pharmaceuticals, biotech, health & medicine.

But, What is Biostatistics? From the Wikipedia definition of Biostatistics:

Biostatistics (or biometry) is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological…

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Added by Pansop on April 24, 2015 at 1:00am — No Comments

How big data can terrorize global terrorism

It is the tragedy of the 21st century! Terrorism has become big business to some blood thirsty tyrants who would stop at nothing to use human beings as collateral in their quest for more resources.

The Islamic State, Al-Qaeda, Al-Shabaab, Boko-Haram...the list is endless. They are found all over the globe. Their objective is to kill, to steal and to destroy. Therefore, their master can only be what you and I can rightfully guess at the slightest instance.…

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Added by Christopher Alvin Mokaya on April 24, 2015 at 12:30am — No Comments

Taming Big Data

Photo: Big Bang Data, Madrid, April 2015

Let's keep it simple. The best application of Big Data is in systems and methods that will significantly reduce the Big Data footprint.

Why would we want to reduce the Big Data…

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Added by Martyn Jones on April 23, 2015 at 11:10pm — No Comments

What Is The Profession Of Data Science Really About Now And In The Future?

What is the profession of data science really about? How does one best become a data scientist or grow a career as one? What does the Data Science Central community think about these questions? (Please chime in!)

We’ve all read about the shortage of data scientists from McKinsey, heard about…

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Added by Sarah Aerni on April 23, 2015 at 6:30pm — No Comments

1,300 Technical Data Science / Algorithm Terms Defined

Very interesting list of algorithm, data science, machine learning, and computer science keywords. To check the definition for any keyword, go to xlinux.nist.gov. For whatever reasons, and like in many similar lists, the top three letters have more entries than subsequent letters, as if the editor suddenly became lazy when hitting letter D (maybe product developers create products that start with letter A, B, or C, to show up at the…

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Added by Mirko Krivanek on April 23, 2015 at 5:00pm — No Comments

Analyzing customer data in sales & marketing - part 1

The key asset of any company is its customers. It is therefore very important to identify their needs and preferences as well as to know the factors affecting their behavior. The collected customer data allows predicting customer behavior and creating appropriate marketing offers, sales plans, and retention programs that match customers’ needs.

Data mining tools are used to create models that predict customer behavior by using historical data. These methods can be…

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Added by Algolytics on April 23, 2015 at 5:00pm — No Comments

36 Computer and Data Science Concepts Explained In Layman’s Terms

Broken down in eight categories.

1. Algorithms and Data Structures

  • Big O Notation
  • Sorting Algorithms
  • Recursion
  • Big Data - Let’s assume you have a leak in a water pipe in your garden. You take a bucket and some sealing materials to fix the problem. After a while, you see that the leak is much bigger that you need a plumber to bring bigger tools. In the meanwhile, you are still using the bucket to drain the water. After a while,…
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Added by Mirko Krivanek on April 23, 2015 at 12:30pm — No Comments

Making MySQL 5,888 times faster



Photo Credit: Morgan Sessions

Making MySQL 5,888.6x faster! Yep, you read that right!

When Jeff our architect first ran the benchmark I could not believe it! I was sitting in front of the terminal screen trying to take in what I had just seen. “Jeff is this correct?!” I asked. “Yep!” he said grinning. I had patiently waited 588 seconds (close to 10 minutes)…

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Added by Titus Blair on April 23, 2015 at 9:30am — No Comments

Data governance 101

Abstract:

We are living in a world dominated by data. Data overload and fatigue is drowning most corporations and individuals. There was a time when we had an opinion and we would speak our mind to our friends and families. Now we instantaneously write our minds on Facebook, Instagram, Twitter and LinkedIn. Everyone has a strong opinion and we don't hesitate to take our opinions public. In the last five years, we have generated as much as data as we have never done…

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Added by Rupen Shah on April 23, 2015 at 7:30am — No Comments

Using Big Data to Target In-Market Shoppers

Retailers know they need Big Data and are charging forward to get in the game.  But many retailers continue to face challenges. What type of data should be collected? How should the data be used to generate insights? How do I measure ROI?

Why Retailers Use Big Data

101data recently surveyed US retailers, across a range of sizes. When asked about which processes would be most impacted by Big Data technology, 50% stated that targeted offers and promotions would have the biggest…

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Added by Larisa Bedgood on April 23, 2015 at 7:30am — No Comments

Big Data Mining and Big Data Analytics

 
In three previous posts, I made an attempt to describe Data Mining, Data…
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Added by Khosrow Hassibi on April 22, 2015 at 11:00am — No Comments

Visualize Your Data Using Silk.co

Creating interactive visualizations of your data for web is a cakewalk using silk.co, all you need to do is to import your spreadsheet and start generating your interactive visualizations

You need to sign in to start creating visualizations on silk.co. If you dont have an account, signing up is easy. All you need is a name, an email id and a password, and the account is created immediately

Add your data…

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Added by Vozag on April 22, 2015 at 2:30am — 1 Comment

RethinkDB for Advanced Analytics

RethinkDB is an open source noSQL database that stores JSON documents. This can be great for open ended data analytics. The company officially provides drivers for Ruby, Python and NodeJS and community supported drivers and ORMs are available in around a dozen languages. 

The production ready version 2.0 was released very recently on April…

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Added by Pansop on April 21, 2015 at 2:30am — No Comments

Weekly Digest - April 27

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

Featured

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Added by Vincent Granville on April 20, 2015 at 9:30am — No Comments

You are Your Own Worst Enemy. Maybe.

It’s happened to all of us sooner or later: The hypothesis seemed plausible, the data was clean, the conclusions sound. Our recommendation was damn near foolproof. Yet when put into practice, the result was anything but favorable.

How could that happen? Data science is about, well, DATA. And science, which implies a reliable method. We have the information, we have the models, we aren’t just shooting in the dark here. Where did it go wrong?

The truth is there are lots of things…

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Added by Randal Scott King on April 20, 2015 at 8:35am — 1 Comment

Sexing up Big Data's Dodgy Dossier

Most of us would probably like to work in a profession recognised for its legality, decency and honesty. At least I hope so. In my line of work, what we have right now is palpable evidence that the IT industry lacks a reliable moral compass.

Imagine this. A major sensationalist tabloid pulls…

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Added by Martyn Jones on April 19, 2015 at 10:30pm — No Comments

How do I learn machine learning?

This was a great question posted on Quora.com, and attracted many comments. Here we summarize the must interesting contributions for you.

Source for picture: …

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Added by Mirko Krivanek on April 19, 2015 at 6:30pm — 1 Comment

Great Github list of public data sets

Many data set resources have been published on DSC, both big and little data. Some associated with our data science apprenticeship. A list can be found here. Below is a repository published on Github, originally posted here. …

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Added by Mirko Krivanek on April 19, 2015 at 1:30pm — 1 Comment

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