1. Data Complexity
2. Speed
3. Analytic Complexity
4. Accuracy & Precision
5. Data Size

Scale Model, one of the newest companies to launch out of betaworks, helps identify, follow and reach communities on Twitter. While there’s a great visual dashboard that gives us a way to look at what’s bubbling up from within communities, it is still hard to evaluate which items appear on a regular basis, and which are more unique. For example, in the US politics model, the #WakeUpAmerica hashtag is used on a regular basis by conservatives, hence appears on the dashboard quite often. Wouldn’t it be great to know when activity around a certain hashtag is unique? Or more specifically, deviates from the expected behavior? Since we can’t expect users to be continuously glued to our dashboard, it’d be great if we could send out notifications whenever something important happens. In the following post, we detail work done by Rohit Jain, a Master’s student at Cornell Tech, who spent the summer with the betaworks data team. Rohit’s work lays the groundwork for a number of new features we hope to integrate into Scale Model.

If you are not a trained clinician, the chances are, you will find it quite hard to correctly identify the signs of this disease. So, how well can a computer program do it?

In commemoration of the 50th anniversary of Sir Hoare’s null, this article explains what null is, why it is so terrible, and how to avoid it.

Have you ever sat in a briefing with an analyst as they describe the results of their analytics? You probably heard some of the terms described below. You may have had a statistics class in your MBA course work ten years ago, and you vaguely remember hearing the same terms there. If you are like me, you probably can spell them correctly 60% of the time, but their actual meaning escapes you. So, let’s look at some commonly misunderstood terms.

Let’s face it, a good statistics refresher is always worthwhile. There are times we all forget basic concepts and calculations. Therefore, I put together a document that could act as a statistics refresher and thought that I’d share it with the world. This is part one of a two part document that is still being completed. This refresher is based on Principles of Statistics by Balmer and Statistics in Plain English by Brightman.

This post will begin to apply a hypothesis-driven development framework (that is, the framework written by Brian Peterson on how to do strategy construction correctly, found here) to a strategy I’ve come across on SeekingAlpha. Namely, Cliff Smith posted about a conservative bond rotation strategy, which makes use of short-term treasuries, long-term treasuries, convertibles, emerging market debt, and high-yield corporate debt-that is, SHY, TLT, CWB, PCY, and JNK. What this post will do is try to put a more formal framework on whether or not this strategy is a valid one to begin with.

For companies that supply physical products, inventory management throughout the supply chain is one of the most important tasks that will largely determine the business’ success or failure. The trick is to always have stock on hand to meet demand, but not have too much capital or storage space taken up with excess inventory. One solution for inventory management is known as the “push.” It is so named because the retailer essentially plans for customer demand in advance, so therefore it produces and then actively “pushes” this inventory in order to meet the forecasted demand. The other solution is the diametric opposite – the “pull” approach which essentially bases inventory on actual real-time orders.