In 2018 Fast Company declared the Data Scientist the best job for the third year in a row, which I wholeheartedly agree with (besides the Director of Fun at the York National Railway Museum), however the role of data scientist, as we know it, will soon have the same fate as the bowling pinsetters, chariot racers, and human alarm clocks.

In 2000-2010 data science was dominated by masters of herculean subjects, with PhDs in linear…

Here is our selection of featured articles posted in the last few days:

Featured Resources and Technical Contributions

• Data Science and Machine Learning: Great List of Resources 
• Free eBooks on Data Visualization…

Here is our selection of featured articles and resources posted in the last few days:

Featured Resources and Technical Contributions

• Comparison of top data science libraries for Python, R and Scala - [Infographic] 
• IBM Community Day: Data…

We discuss a new approach for selecting features from a large set of features, in an unsupervised machine learning framework. In supervised learning such as linear regression or supervised clustering, it is possible to test the predicting power of a set of features (also called independent variables by statisticians, or predictors) using metrics such as goodness of fit with the response (the dependent variable), for instance using the R-squared coefficient. This makes the process of feature…

Here are our selection of featured contributions posted in the last few days:

Featured Resources and Technical Contributions

• AutoEncoders and Parallel Computing with KernelML
• New Book: Mastering Machine Learning…

Python continues to take leading positions in solving data science tasks and challenges. Last year we made a blog post overviewing the Python’s libraries that proved to be the most helpful at that moment. This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year.…

The impact of a change of scale, for instance using years instead of days as the unit of measurement for one variable in a clustering problem, can be dramatic. It can result in a totally different cluster structure. Frequently, this is not a desirable property, yet it is rarely mentioned in textbooks. I think all clustering software should state in their user guide, that the algorithm is sensitive to scale.

We illustrate the problem here, and propose a scale-invariant methodology for…

New article by Bill Vorhies.

Summary:  There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail.  It’s not as simple as it seems and here we try to take a more nuanced look.  The result is not as threatening as many headlines make it seem.…

Here is our selection of featured articles and resources posted in the last few days.

Featured Resources and Technical Contributions

• Free Book: Applied Stochastic Processes 
• A guide to manipulating, analyzing, and visualizing…

Many data scientists have a passion for mathematics, and many modern math problems can be explored using data science. Below is a selection of interesting articles, many about challenging, deep mathematical problems, by a data scientist who developed math-free algorithms. Some of these articles cover statistical theory and thus belong to…

Mathematical Olympiads are popular among high school students. However, there is nothing similar for college students, except maybe IMC. Even IMC is not popular. It focuses mostly on the same kind of problems as high school Olympiads, and you can not participate if you are over 23 years old. In addition, it is organized by country, as opposed to globally, thus favoring countries with a large population. Topics such as…

The list below is a (non-comprehensive) selection of what I believe should be taught first, in data science classes, based on 30 years of business experience. This is a follow up to my article Why logistic regression should be taught last.

I am not sure whether these topics below are even discussed in data camps or college…

Here is our selection of featured articles and resources posted in the last few days.

• New Book: Principles of Database Management 
• Competition: Explaining black box machine learning models …

With a data analysis plan, you know what you’re going to do when you actually sit down to do the analysis of the data you’ve gathered. It’s a vitally important thing for you to have, as it will guide how you’re going to collect your data. After all, it’s very difficult to add in new variables afterward.

For that reason, you want to make sure you’ve created your plan beforehand so that you can be sure that you’re asking all the questions you need to and you know what you’re going to…

Here is our selection of featured articles and resources posted in the last few days:

• Machine Learning and Its Algorithms to Know – MLAlgos
• A quick introduction to PyMC3 and Bayesian models…

These articles are between 3 and 5 year old, but are still valuable today. The methodology used in these articles is modern, and still state-of-the-art today. Some discuss immense data sets still available to the public, and that resulted in designing new machine learning techniques to handle them. 

I am in the process of organizing these articles (written by myself) to eventually self-publish data science tutorials, in a few separate booklets, that are easy to understand for the…

In this article, we show that the issue with polynomial regression is not over-fitting, but numerical precision. Even if done right, numerical precision still remains an insurmountable challenge. We focus here on step-wise polynomial regression, which is supposed to be more stable than the traditional model. In step-wise regression, we estimate one coefficient at a time, using the classic least square technique. …

Guest blog post by David Enríquez Arriano. For more information or to get higher pictures resolution, contact the author (see contact information at the bottom of this article.)

Introduction

This is a different approach to solve the AI problem. It is a cognitive math based on pyramids built with self-programming logic gates through learning.

A Boolean polynomial associated with a given truth table can be implemented with electronic…

Here is our selection of featured resources and articles published in the last few days. Enjoy the reading!

Resources

• Showcase your Data Science Expertise, and Learn New Tricks from Pros 
• Generalized Machine Learning - Simple ML to…

Summary: Our starting assumption that sequence problems (language, speech, and others) are the natural domain of RNNs is being challenged.  Temporal Convolutional Nets (TCNs) which are our workhorse CNNs with a few new features are outperforming RNNs on major applications today.  Looks like RNNs may well be history.

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