At the Mobile World Congress, MasterCard recently announced that it was teaming up with Facebook to provide small businesses in Africa and Asia with an affordable and simple on-ramp for accepting mobile payments.

The partnership will help unbanked retailers and merchants in the regions open bank accounts…

There are many good and sophisticated feature selection algorithms available in R.  Feature selection refers to the machine learning case where we have a set of predictor variables for a given dependent variable, but we don’t know a-priori which predictors are most important and if a model can be improved by eliminating some predictors from a model.  In linear regression, many students are taught to fit a data set to find the best model using so-called “least squares”.  In most…

Across the country, animal shelters work around the clock to help pets get rescued. For the most part, people assume animal adoption is mostly driven by intuition and emotion — a family comes in, falls in love, and then welcomes a new pet into their home.

As readers here are likely to suspect, if you have the data, you can see there’s more to the story. Fortunately, the Austin Animal Shelter has made their animal…

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.

• The APEXX W3 Brings DL to Your Deskside. Featuring an 18-core Intel® Xeon® W CPU and up to four professional NVIDIA Quadro GV100 GPUs, APEXX W3…

Although a support vector machine model (binary classifier) is more commonly built by solving a quadratic programming problem in the dual space,  it can be built fast by solving the primal optimization problem also. In this article a Support Vector Machine implementation is going to be described by solving the primal optimization…

R language is a free statistical computing environment; hence there are multiple ways/packages to achieve a particular statistical/quantitative output. I am going to discuss here a concise list of R packages that one can use for the modeling of financial risks and/or portfolio optimization with utmost efficiency and effectiveness. The intended audience for this article is financial market analysts interested in using R, and also for quantitatively inclined folks…

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

• New Decimal Systems - Great Sandbox for Data Scientists and Mathematicians 
• Data Engineer and Business Analyst Might be the…

It's been a while since my last blog post, but I wanted to update everyone on a project that's been keeping me busy recently.

Last year Pearson Publishing commissioned me to write a book on big data / data science.  They asked me to write it for executive audiences (non-technical), and to make it very practical for business use. Writing the book was quite a task, but it was a great exercise in summarizing the industry experience I'd gathered in my years leading data science teams…

We illustrate pattern recognition techniques applied to an interesting mathematical problem: The representation of a number in non-conventional systems, generalizing the familiar base-2 or base-10 systems. The emphasis is on data science rather than mathematical theory, and the style is that of a tutorial, requiring minimum knowledge in mathematics or statistics. However, some off-the-beaten-path, state-of-the-art number theory research is discussed here, in a way that is accessible to…

After reviewing 8 great ETL tools for fast-growing startups, we got a request to tell you more about open source solutions.There are many open source ETL…

With high profile security breaches like Equifax, the publicity over data security, as well as the cost, has only continued to grow. According to The 2017 Cost of Data Breach Study from the Ponemon Institute, the global average cost of a data breach is $3.6 million, or $141…

My new book may be of interest to some members.

Data Science is an inter-disciplinary field, which deals with algorithms, processes, systems and is used to extract insights from huge amounts of data and improve understanding. Data mined can be in any form – structured or unstructured. Data Science utilizes numerous theories & techniques that are part of other fields such as Mathematics, Statistics, Computer Science, Information Science, and Chemometrics.

Rise of Data Science

The emergence…

Reinforcement Learning (RL) –  3rd / last post in this sub series “Machine Learning Type” under master series “Machine Learning Explained“. Next sub series “Machine Learning Algorithms Demystified” coming up. This post talks about reinforcement machine learning only. 

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Data Dictionary to Meta Data III is the third and final blog devoted to demonstrating the automation of meta data creation for the American Community Survey 2012-2016 household data set, using a published data dictionary. DDMDI was a teaser to show how Python could be used to generate R statements that…

The European General Data Protection Regulations (GDPR) will come into force on May 25, 2018. These regulations will have a significant impact on existing data collection and analysis methods. 

Many businesses have become reliant on customer…

Customers leave behind an incomprehensible amount of data while they go about shopping. Making sense of that data and reacting in real time are the two things that will keep companies one-step ahead of their customers (and competition) in the present-day customer-centric world.

Today, the average customer is spoiled for choice. Every time he goes shopping, he expects highly personalized, relevant offers. One poor interaction with a brand, and poof, the customer’s gone, almost-certain…