My daughter just started a business analytics Master's program. For the probability sequence of the core statistics course, one of her assignments is to calculate the probability of single 5 card draw poker hands from a 52-card…

Digital marketing nowadays is powered by cutting-edge machine learning technologies (and not so cutting-edge analytical methods). 

Digital methods, however, were not nearly as revolutionary in their impact as the advent of direct mail, pioneered by the Wards and Sears catalogs nearly a century and a half ago. Riding on the backs of an…

During my first project in McKinsey in 2011, I served the CEO of a bank regarding his small business strategy. I wanted to run a linear regression on the bank's data but my boss told me: "Don't do it. They don't understand statistics". (We did not use Machine Learning but, 7 years down the road, I still believe we developed the right…

You know who you are. A high-calibre machine learning magician, a well-versed wrangler of data... but you want a bit more from your role. That may be progression, more money or the chance to work on new, more exciting projects, but where do you go from here?

Many companies are looking to increase investment in data science departments and looking for leaders to build out new teams to do this. But before you take the plunge into the C-level, weigh up what this role entails and…

R is a well-known and increasingly popular tool in the Data Science field. It is a programming language and a software environment primarily designed for statistical computing, so its interface and structure are very well suited for the scientific tasks. Moreover, R has one of the most developed libraries systems that counts thousands of packages to solve a wide variety of problems.

Although there are many general-purpose…

Created an R package for exploratory data analysis. Package name is SmartEDA now available on CRAN. This package includes multiple custom functions to perform initial exploratory analysis on any input data describing the structure and the relationships present in the data. The generated output can be obtained in both summary and graphical form. The graphical form or charts…

Summary:  Researchers in Synthetic Neuro Biology are proposing to solve the AGI problem by building a brain in the laboratory.  This is not science fiction.  They are virtually at the door of this capability.  Increasingly these researchers are presenting at major AGI conferences.  Their argument is compelling.

If you step…

It is hard to imagine that some data element could contain less information than a bit (a digit equal to either 0 or 1.) Yet examples are abundant. Indeed, I am wondering if we should create a unit of information called microbit, or nanobit.

The first examples that come to my mind are some irrational numbers such as Pi: it's digits are widely believed to be indistinguishable from pure noise, thus carrying essentially no information. While there is not enough data storage in the…

In this column, we would like to elaborate on the concept of data security.  

Although security is often related to privacy, they are not synonyms. Data security can be defined as the set of policies and techniques to ensure the confidentiality, availability and integrity of data at all times. On the other hand, data privacy refers to the fact that the parties accessing and using the data do so only in ways that comply with the agreed upon purposes of data…

To SQL or not To SQL: that’s the question!

Lemahieu W., vanden Broucke S., Baesens B.

This article is based upon our upcoming book Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data, www.pdbmbook.com  See also our corresponding YouTube channel with free video lectures :…

I recently read a very popular article entitled 5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist. Here I provide my opinion on why this should no be the case.

It is nice to have logistic regression on your resume, as many jobs request it, especially in some fields such as biostatistics. And if you learned the details during your college classes, good for you. However, for a beginner, this is not the first thing you should…

These pictures were posted on Quora by Oleg Sergeykin, former Structural Analysis Engineer at Boeing. His philosophy is that Data science is actually an iterative processes. It is never possible to complete a DS project in a single pass. A data scientist constantly tries new ideas and changes steps of his pipeline.…

Many are free. They are available online. They are offered by Princeton, Georgia Tech, Harvard, Columbia, Stanford, and Penn State. 

• Neural Networks and Deel Learning - Andrew Ng via Coursera
• Machine Learning - Georgia Institute of Technology via Udacity…

Machine Learning is hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need.

The Brief

Machine Learning is the hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. The real need for today’s time and business is to clarify,…

This is the new book by Andrew Ng, still in progress. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. He is one of the most influential minds in Artificial Intelligence and Deep Learning. Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company's Artificial Intelligence Group into several thousand people. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University. Ng is also an early…

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.

• Join 4,000 members of the Apache Spark™ community in San Francisco June 4th-6th for Spark + AI Summit, the conference for data scientists…

The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO. Designers and end users of machine learning algorithms will both benefit from more interpretable and…

The Practical Guide to Storing, Managing and Analyzing Big and Small Data -- Cambridge University Press.

This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a…

Technology is known to shift landscapes, even change the game. We saw that when the internet exploded in scale and popularity, as computers became smarter, and the world goes through the digital transformation. An easy example is in traditional marketing, which now borders on the irrelevant, unable to hold a candle to its more modern counterparts.

Email,…