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.

GDPR (EU General Data Protection Regulation) is around the corner and bigger companies are getting ready to adopt it as they already know what kind of penalties come from non-compliance. It replaces replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe and it is the biggest change on data privacy regulation in 20 years for Europe. While GDPR main elements can be a little tricky to understand, one thing is clear as sensitive Data Discovery is mandatory, so you can find the Personal and sensitive information on your data repositories, that can be almost everything from databases to files. Basically, we focus our data discovery on three main areas: column discovery, data discovery and file discovery. Column discovery is easy to understand, based on specific keywords or sentences we find column names on databases and match it with possible sensitive data.

A large body of water separated the present-day North American continent into two halves during most of the late Cretaceous Period, between 95 and 66 million years ago, and because of the seaway linking the Arctic Ocean to the Gulf of Mexico, land animals on one side could not make it to the other, and would therefore evolve independently. One such genus of animals, trapped on the western half, was the horned dinosaur, whose remains have been found in western North America, as well as Asia. However, the discovery of a tooth in Mississippi provides evidence that horned dinosaurs were present in eastern North America as well. The fossil, dated to between 66 and 68 million years ago, is from a dinosaur closely related to Triceratops, the most well-known genus of horned dinosaurs. The find also suggests that there could have existed some land connection between the two land masses thought to be completely separate at the time. This is a tooth of a ceratopsid horned dinosaur from Mississippi.

The Internet of Things (IoT) is an uninterrupted connected network of embedded objects/ devices with identifiers without any human intervention using standard and communication protocol. It provides encryption, authorization and identification with different device protocols like MQTT, STOMP or AMQP to securely move data from one network to another. IoT in connected Government helps to deliver better citizen services and provides transparency. It improves the employee productivity and cost savings. It helps in delivering contextual and personalized service to citizens and enhances the security and improves the quality of life.

The Internet of Things (IoT) is an uninterrupted connected network of embedded objects/ devices with identifiers without any human intervention using standard and communication protocol. It provides encryption, authorization and identification with different device protocols like MQTT, STOMP or AMQP to securely move data from one network to another. IoT in connected Government helps to deliver better citizen services and provides transparency. It improves the employee productivity and cost savings. It helps in delivering contextual and personalized service to citizens and enhances the security and improves the quality of life.

The Internet of Things (IoT) is an uninterrupted connected network of embedded objects/ devices with identifiers without any human intervention using standard and communication protocol. It provides encryption, authorization and identification with different device protocols like MQTT, STOMP or AMQP to securely move data from one network to another. IoT in connected Government helps to deliver better citizen services and provides transparency. It improves the employee productivity and cost savings. It helps in delivering contextual and personalized service to citizens and enhances the security and improves the quality of life.