Late last week, as some staff astronomers embarked on trips to see Monday's solar eclipse, two of NASA's space-based observatories--Hubble and Chandra X-ray--and at least two land-based telescopes scrambled to capture a far more explosive event. The astronomers who stayed behind trained their telescopes on a patch of sky where they hoped to find an astrophysical Rosetta stone: a cataclysmic event capable of producing electromagnetic signals on top of gravitational waves separately detected by the Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO). Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. The LIGO collaboration made headlines in February 2016 when it announced it had detected gravitational waves from two colliding black holes. Four months later, while still in its first observing run, the team confirmed the detection of a second black hole merger.

American physicist and philosopher Thomas Kuhn came up with idea of a "paradigm shift" in the 1960s to describe a scientific revolution – a momentous discovery that fundamentally rewrites the laws of science, such as Galileo proving the Earth revolves around the Sun or Newton discovering gravity. It is not too much to say that finance is undergoing a paradigm shift today, driven by smartphones, financial technology startups, and trends such as blockchain and artificial intelligence. "Most of the change in the industry was quite incremental and what I regard as linear – the introduction of ATMs, the introduction of credit cards, those kinds of things," says Antony Jenkins, former chief executive of Barclays. "When you look at what's happening now with mobile banking, it's a true transformation. The power in people's pockets enables them to change things in really quite a radical way."

Application of hypothesis testing is predominant in Data Science. It is imperative to simplify and deconstruct it. Like a crime-fiction story, hypothesis testing, based on data, leads us from a novel suggestion to an effective proposition. Hypothesis originates from the Greek work hupo (under) and thesis(placing). It means an idea made from limited evidence. It is a starting point for further investigation.

Drawing on some recent results that provide the formalism necessary to definite stationarity for infinite random graphs, this paper initiates the study of statistical and learning questions pertaining to these objects. Specifically, a criterion for the existence of a consistent test for complex hypotheses is presented, generalizing the corresponding results on time series. As an application, it is shown how one can test that a tree has the Markov property, or, more generally, to estimate its memory.

The study of networks leads to a wide range of high dimensional inference problems. In most practical scenarios, one needs to draw inference from a small population of large networks. The present paper studies hypothesis testing of graphs in this high-dimensional regime. We consider the problem of testing between two populations of inhomogeneous random graphs defined on the same set of vertices. We propose tests based on estimates of the Frobenius and operator norms of the difference between the population adjacency matrices. We show that the tests are uniformly consistent in both the "large graph, small sample" and "small graph, large sample" regimes. We further derive lower bounds on the minimax separation rate for the associated testing problems, and show that the constructed tests are near optimal.

Essentially good hypotheses lead decision-makers like you to new and better ways to achieve your business goals. When you need to make decisions such as how much you should spend on advertising or what effect a price increase will have your customer base, it's easy to make wild assumptions or get lost in analysis paralysis. A business hypothesis solves this problem, because, at the start, it's based on some foundational information. In all of science, hypotheses are grounded in theory. Theory tells you what you can generally expect from a certain line of inquiry.

Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses $\mathcal{H}(n) = (H_1,\dotsc, H_n)$, Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected null hypotheses, and proposed a testing procedure that controls FDR below a pre-assigned significance level. Nowadays FDR is the criterion of choice for large scale multiple hypothesis testing. In this paper we consider the problem of controlling FDR in an "online manner". Concretely, we consider an ordered --possibly infinite-- sequence of null hypotheses $\mathcal{H} = (H_1,H_2,H_3,\dots )$ where, at each step $i$, the statistician must decide whether to reject hypothesis $H_i$ having access only to the previous decisions. This model was introduced by Foster and Stine. We study a class of "generalized alpha-investing" procedures and prove that any rule in this class controls online FDR, provided $p$-values corresponding to true nulls are independent from the other $p$-values. (Earlier work only established mFDR control.) Next, we obtain conditions under which generalized alpha-investing controls FDR in the presence of general $p$-values dependencies. Finally, we develop a modified set of procedures that also allow to control the false discovery exceedance (the tail of the proportion of false discoveries). Numerical simulations and analytical results indicate that online procedures do not incur a large loss in statistical power with respect to offline approaches, such as Benjamini-Hochberg.

DuPont has a rich history of scientific discovery that has enabled countless innovations and today, we're looking for more people, in more places, to collaborate with us to make life the best that it can be. Seeking a Data Engineer/Software Developer to design, develop, and implement high quality data solutions and applications for our data science and analytics platform in AWS. Education & Experience: BS degree in Computer Science, Physics, Electrical Engineering, or a related field.

Visual Analytics and Data Discovery allow analysis of big data sets to find insights and valuable information. This is much more than just classical Business Intelligence (BI). See this article for more details and motivation: "Using Visual Analytics to Make Better Decisions: the Death Pill Exa...". Let's take a look at important characteristics to choose the right tool for your use cases. Several tools are available on the market for Visual Analytics and Data Discovery.

I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently machine learning. Each of these transition has required me to do a change in mind set on how to look at the data. But, one instance sticks out in all these transitions. This was when I was working as a BI professional creating management dashboards and reports. Due to some internal structural changes in the Organization I was working with, our team had to start reporting to a team of Business Analysts (BA). At that time, I had very little appreciation of what is Business analytics and how is it different from BI. So, as part of my daily responsibilities, I prepared my management dashboard in the morning and wrote a commentary on it. I compared the sales of first week of the current month to sales of previous month and same month last year to show an improvement in business.