Now that everyone is thinking about IoT and the phenomenal amount of data that will stream past us and presumably need to be stored we need to break out a vocabulary well beyond our comfort zone of mere terabytes (about the size of a good hard drive on your desk).

In this article Beyond Just “Big” Data author Paul McFedries argues…

Guest blog by Justin Tenuto

One of the big reasons we created our Data for Everyone initiative is that there simply aren't a ton of great open datasets out there for small businesses, startups, and academics to do work on. Sure, there are plenty of small, toy-sized datasets but those simply aren't big enough to create algorithms that anyone can trust. In fact, our founder Lukas wrote as much in this post:

Remember…

Imagine if Donald Trump (or some blowhard like him, ex the misogyny) were ranting to apprentices about the current state of data analytics in “real world” corporate America.

Below are 3 Myths, Realities and Truths he might say (if he knew what he was talking about), as counterpoints to some of the (known/acknowledged) hype:

MYTH #1: Data analytics is transforming…

This is a guest blog from David Lefkowich, VP Sales and Marketing for FreeSight Software

I have yet to find an organization that doesn’t have occasional or consistent data quality concerns.  Some concerns are innocuous inconveniences; but others can…

Summary:  Data Scientist may be a prestigious title but it doesn’t reflect our area of specialization or the depth of our experience.  As legions of newly minted Data Scientists are granted degrees over the next few years the problem for both employee and employer will only grow worse.

With the explosion in undergraduate and graduate level offerings in data…

When you hear the term data scientist, what do you think of? If you’re like most people, you might think of something incredibly complex, with statistical terms and programming languages that are beyond comprehension. You might think that only PhD’s in computer science can do data science.

But if you peel back the layers, you’ll find that this isn’t the case. Data science, coined by DJ Patil who is now the…

In my final year of Law School I did a Practical Legal studies course, it is compulsory at the University  that I attended and students are…

In this last part of the tutorial we will discuss the LIFT curve.

Definition and types of lift curves

A lift chart pictures gains from applying a classification model in comparison to not applying it (i.e. using a random classifier) for a given section of data.

Two simple examples are shown below.…

Zoher Karu is Vice President of Global Customer Optimization and Data at eBay, where he works to use data, analytics, and insights to drive growth across all customer interactions, with…

Open source and proprietary cloud services both aim to provide end-users with reliable software. Some users prefer the backing of a large company like Amazon or Microsoft, with a tailored list of compatible programs and services. Others prefer the interoperability and flexibility of open source alternatives like OpenStack or Eucalyptus. It's not necessarily an issue of right or wrong, for some people open source software is the obvious choice. Those who want more managed solutions…

1) NJF Global Holdings (Hedge Fund) - Hiring for about 7 Positions in New York City

NJF Global Holdings Is one of the leading Quantitative Hedge Funds in the world that applies a fully systematic approach to investing across global financial markets. The firm’s investment approach uses statistical and machine / deep…

Data modeling is usually one of those subjects that make people's eyes glaze over. It's not really programming, though understanding programming concepts such as objects, inheritance, polymorphism and similar multisyllabic words is usually helpful to do modeling. It's not a business analyst function, though most BAs end up participating in the modeling process. Perhaps the best way of thinking about modeling is to see it as a way to describe a business in clearly defined pieces.

From…

When people in the financial services sector talk about “the bank of the future,” they often focus on the external things. In a few years from now, for example, bank branches may look completely different than they do today, equipped with many more touchscreens and sensors within furniture connected to the Internet of…

Here's a different angle on a much analyzed question at the heart of our professional activities.  In this article, Steve Miller of Inquidia tackles how NoSQL has changed our traditional understanding of Predictive Analytics and Data Science.  You might also look back at our previous post How NoSQL Fundamentally Changed Machine Learning.

Here's the beginning…

One of the beauties of running Javascript related applications is you don’t need to install any client side software, optimize servers and spend tons of time on the core infrastructure. Javascript just work outs of the core browser. In that spirit, there is a lot of increasing momentum on building Machine Learning in Javascript. We have collected a list of resources on Javascript…

Having thoroughly enjoyed the debate around Bernard Marr's post, Why so many fake data scientists?, it occurred to me that "data scientist" is not the only problematic term in our industry. Many of the most common data-related terms and concepts are also ambiguous or poorly-defined.

Here are some of the terms that cause me frustration.…

It’s no surprise that Silicon Valley and the Washington, D.C. metro area are consistently the two biggest cities for finding big data jobs; tech startups drive the demand in California, while the U.S. government is the single biggest tech employer around.

Source for picture: …

Guest blog sent by Veronica Johnson at Investintech.com

see the original here

In this day and age of big data and information overload, data visualizations are, hands down, the most effective way of filtering out and presenting complex data.

A…

While many of us recognize that companies are empowered by actionable information penetrations and help drive sales, devotion and superior customer experiences, the thought of making sense of enormous quantities of information and undertaking the task of unifying is daunting. But that is slowly changing. Experts forecast that this year, budgets will be allocated by most companies, and that 2015 will undoubtedly be the year of big data and discover the best tools and resources to really…