This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as…

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

• The Browser of a Data Scientist 
• GDPR and the Paradox of Interpretability …

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting,…

You may even call it post-doctorship, as the level is beyond the traditional PhD degree. It is not a degree, not competing with university programs, but instead, akin to a fellowship or apprenticeship to learn doing state-of-the-art applied research, discover ground-breaking results or applications, and translate your discoveries into seminal material suitable for a broad audience. It is intended for professionals with substantial experience, perhaps to people who already have a PhD in a…

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.

Announcements

• Have you secured your spot at …

These results are relatively easy to prove (first year college-level calculus needed) and could be a good test to refresh your math skills. We posted another simple one (with a probabilistic / number theory flavor) a while back, see here.…

Here is our selection of featured articles and technical resources posted since Monday. Enjoy the reading! 

Upcoming DSC Webinars

• NLP and Conversational AI Platform - March 28
• Selection, Deployment and Optimization of Data Analytics Technology - March…

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.

Announcement

• IBM predicts that the demand for data scientists will soar 28% by 2020. Take advantage of this opportunity and enhance your skill set…

Here is our list of featured resources and articles posted since Monday.

Resources and Technical Contributions

• A Simple Introduction to Complex Stochastic Processes - Part 2 
• Neural network…

In my first article on this topic (see here) I introduced some of the complex stochastic processes used by Wall Street data scientists, using a simple approach that can be understood by people with no statistics background other than a first course such as stats 101. I defined and illustrated the continuous Brownian motion (the mother of all…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting,…

This is a new series, featuring great content from our top contributors. Some of these articles are rather technical in nature, but many are business-oriented and written in simple English. The entire series consists of about 120 articles. We intend to publish a new set every two weeks or so. Click here to check out the…

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.

Here are some recommended books for data science practitioners, covering machine learning, deep learning, business intelligence, forecasting, text analytics and much more.

Book Repository for Data Scientists

• New Book: Analytics - How to Win with Intelligence …

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

• Make Your Chatbot Smarter by Talking to It 
• Interesting Application of the Zipf Distribution: Data Purging …

Myth #1: You can only do research in an academic setting. Not true. There are plenty of research labs owned by big and small companies and organizations, including government, as well as abroad. In my case, I own and manage my self-funded research lab, publishing in my own niche media outlets (see …

The Zipf distribution is used to model situations in which a few observations have a very high value (or impact) and account for a large part of the total, while a very long tail of observations have medium, small, or very small values. A bit like …

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz,…

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.

• Enhance your data analysis skills with an online degree from Penn State World Campus. Our programs focus on data and data management. Learn to collect,…

They come in various shapes: infographics, cheat sheets, periodic tables, one-picture articles, and even maps. They cover everything: IoT, AI, machine learning, data science, deep learning, Hadoop, Python, R, dataviz, statistical theory, big data - you name it. Below is a selection that is most relevant to our readers. 

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees,…