Source: see here

In Part 1 of this short series, I have included the most interesting articles that I wrote in the last few years. This part focuses on the business analytics / BI /…

Bernoulli trials are random experiments with two possible outcomes: "yes" and "no" (in the case of polls),  "success" and "failure" (in the case of gambling or clinical trials). The trials are independent from each other: for instance tossing a coin multiple times, or testing the success of a new drug against a specific…

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This a follow up to my previous article here, where you can find additional, very different images, the theory behind it, and relevance to machine learning techniques. What is surprising is that all these images were produced with a formula with a single parameter λ, and…

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Figure 1: The pillow basins (see section 3)

The topic discussed here is closely related to optimization techniques in machine…

Here I specifically discuss the case of a PhD degree from a third-tier university, though to some extent, it also applies to master degrees. Many professionals joining companies such as Facebook, Microsoft, or Google in a role other than a programmer, typically have a PhD degree, although there are many exceptions. It is still possible to learn data science on the job, especially if you have a quantitative background (say in physics or engineering) and have experience working with serious…

Convolution is a concept well known to machine learning and signal processing professionals. In this article, we explain in simple English how a moving average is actually a discrete convolution, and we use this fact to build weighted moving averages with natural weights that at the limit, have a Gaussian behavior guaranteed by the Central Limit Theorem. Moving averages are nothing more than blurring filters for signal processing experts, with a Gaussian-like kernel in the case discussed…

Here I provide translations for various important terms, to help professionals from related backgrounds better understand each other. In particular, machine learning professionals versus statisticians.

Source for picture:…

This is the second part of my article Spectacular Visualization: The Eye of the Riemann Zeta Function, focusing on the most infamous unsolved mathematical conjecture, one that has a $1 million dollar price attached to it. I used the word deep not in the sense of deep neural networks, but because the implications of these…

We discuss here one of the most famous unsolved mathematical conjectures of all times, one among seven that has a $1 million award attached to it, see here. It is known as the Riemann Hypothesis and abbreviated as RH. Of course I did not solve it (yet), but the material presented here offers a new…

I am a data scientist, and decided to open a restaurant last November, 10 days before the governor in my state banned dining-in (who knows for how long) and customers were already rare. Some data scientists in managerial positions dream about exiting the corporate world and envied me, at least before the Covid, when I told them my plan.

Here I explore the options and opportunities available, and this article reflects my optimism. I will also discuss analytics in some detail. The…

I have included a lot of Excel spreadsheets in the numerous articles and books that I have written in the last 10 years, based either on real life problems or simulations to test algorithms, and featuring various machine learning techniques. It is time to create a new blog series focusing on these useful techniques that can easily be handled with Excel. Data scientists typically use programming languages and other visual tools for these techniques, mostly because they are unaware that it can…

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There is no statistical test that assesses whether a sequence of observations, time series, or residuals in a regression model, exhibits independence or not. Typically, what data scientists do is to look at auto-correlations and see whether they are close enough to zero. If the data follows a Gaussian distribution, then absence of auto-correlations implies independence. Here however, we are dealing with non-Gaussian observations. The setting is similar to testing whether a pseudo-random…

Here I share my predictions as well as personal opinion about the pandemic. My thoughts are not derived from running sophisticated models on vast amounts of data. Much of the data available has major issues anyway, something I am also about to discuss. There are some bad news and some good news. This article discusses what I believe are the good news and bad news, as well a some attempt at explaining people behavior and reactions, and resulting consequences. My opinion is very different from…

The model-free, data-driven technique discussed here is so basic that it can easily be implemented in Excel, and we actually provide an Excel implementation. It is surprising that this technique does not pre-date standard linear regression, and is rarely if ever used by statisticians and data scientists. It is related to kriging and nearest neighbor interpolation, and apparently first mentioned in 1965 by Harvard scientists working on GIS (geographic information systems). It was referred…

While the Bernoulli and binomial distributions are among the first ones taught in any elementary statistical course, the Poisson-Binomial is rarely mentioned. It is however one of the simplest discrete distributions, with applications in survey analysis, see here. In this article, we are dealing with…

Here is our selection of featured articles and technical resources posted since Monday:

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Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  

Featured Resources and Technical…

Here is our selection of featured articles and technical resources posted since Monday:

Announcement

• Learn why 63% of firms will be advancing their adoption of AI by 2023.

Technical Resources

• Free book - Cloud Native, Containers and…