Tag: Big Data

Feed: R-bloggers. Author: R on The CuRious Financial Risk ManageR. In this post we will close the trilogy on (old style) swap pricing. In particular, we will look at how downloading the data for the variable rate needed to calculate the variable leg accrual. Part 1gave the general idea behind tidy pricing interest rate swaps using a 7 linespipe Part 2went much more into detail and priced some real world contract comparing the resultsobtained vs Bloomberg and showing significantly good results. The only part missing was calculating the accrual for the floating leg. To do thiswe need the information about ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. The grainchanger package provides functionality for data aggregation to a coarser resolution via moving-window or direct methods. Why do we need new methods for data aggregation? As landscape ecologists and macroecologists, we often need to aggregate data in order to harmonise datasets. In doing so, we often lose a lot of information about the spatial structure and environmental heterogeneity of data measured at finer resolution. The issues around scale disconnects are both conceptual and practical: Conceptual issues Technical issues The scale we measure this stuff at varies too Ecological response ... Read More

Feed: R-bloggers. Author: Keith Goldfeld. In the last two posts, I introduced the notion of time-varying intra-cluster correlations in the context of stepped-wedge study designs. (See here and here). Though I generated lots of data for those posts, I didn’t fit any models to see if I could recover the estimates and any underlying assumptions. That’s what I am doing now. My focus here is on the simplest case, where the ICC’s are constant over time and between time. Typically, I would just use a mixed-effects model to estimate the treatment effect and account for variability across clusters, which is ... Read More

Feed: R-bloggers. Author: hrbrmstr. Despite being a full-on denizen of all things digital I receive a fair number of dead-tree print magazines as there’s nothing quite like seeing an amazing, large, full-color print data-driven visualization up close and personal. I also like supporting data journalism through the subscriptions since without cash we will only have insane, extreme left/right-wing perspectives out there. One of these publications is The Economist (I’d subscribe to the Financial Times as well for the non-liberal perspective but I don’t need another mortgage payment right now). The graphics folks at The Economist are Top Notch and a ... Read More

Feed: Big Data Made Simple. Author: Rodney Laws. Maintaining stable finances is a core concern of almost every business, regardless of the size or type, because — unless you happen to be independently wealthy and willing to throw good money after bad — you can’t keep operating once your funds run dry. But it’s often a complicated matter. Payments big and small stack up, economic circumstances shift, and revenue varies. Even if you’re smart and careful, you can still have great difficulty.In recent years, though, the field of big data has moved from an interesting consideration to an influential practical ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: sue-green; Last year, more than 100,000 developers from 156 nations built 2,500+ applications in Call for Code 2018, an IBM initiative to create meaningful change through technology. This year, it's your turn. Join Call for Code 2019 and you’ll have the opportunity to create sustainable software solutions that help people prepare and recover from natural disasters.Call for Code is inspired by a sobering reality: though natural disasters are inevitable, innovative solutions can be hard to come by. But big ideas can create big change. When motivated developers apply innovative, ... Read More

Feed: Planet big data. Author: Gina Bhawalkar. A recent study on design maturity from InVision revealed something really interesting: that design team size is not predictive of a design team’s maturity. That is, it’s possible to dedicate a lot of headcount to design but still see limited benefits on business outcomes. Not only did they find there’s not a correlation, but they also found the average size of the highest-maturity teams — what they call Level 5 — is half that of Level 1 maturity teams. So if unlocking the business benefits of design doesn’t come with investing in more ... Read More

Feed: Planet big data. Author: Arielle Trzcinski. One out of two US consumers encountered some type of setback or frustration with their healthcare.[i] While that captures just one moment in time of a consumer’s dissatisfaction with the patient experience, it’s safe to say that this has been the norm for years. The patient experience is broken. There’s a silver lining, though. The critical need to improve the patient experience has given rise to providers such as One Medical that have been adept at delivering best-in-class digital and physical experiences through innovations in digital touchpoints, process enhancement, and wellness management. To ... Read More

Feed: Big Data Made Simple. Author: Stephanie Donahole. The world seems to be fueling a dystopian view of what artificial intelligence (AI) can do. However, no machine can ever be imbued with the intelligence of a human; they slavishly follow indefinite program conditions which have already been laid down by a smart programmer. With the increase in technology advancments, Gartner foresees a shift. From stand-alone intelligent things to a collaborative swarm network of AI systems.It shouldn’t come to any surprise that workspaces all across the globe are being reshaped. By disruptive technology day in day out. Moreover, they have the ... Read More

Feed: R-bloggers. Author: Nathan Piccini. Think of this as the one-stop-shop/dictionary/directory for your machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you’ll find 101 machine learning algorithms, including useful infographics to help you know when to use each one (if available). 101 Machine Learning Algorithms Each of the accordian drop downs are embeddable if you want to take them with you. All you have to do is click the little ’embed’ button in the lower left hand corner and copy/paste ... Read More