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June 2018 Blog Posts (97)

#Apress “Practical Enterprise Data Lake Insights” – Published!

Hello All,

Gives me immense pleasure to announce the release of our book “Practical Enterprise Data Lake Insights” with Apress. The book takes an end-to-end solution approach in a data lake environment that includes data capture, processing, security, and availability. Credits to the co-author of the book, Venkata Giri and technical…

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Added by Saurabh K. Gupta on June 30, 2018 at 10:13am — No Comments

Weekly Digest, July 2

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.

Featured Resources and Technical Contributions

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Added by Vincent Granville on June 30, 2018 at 7:30am — No Comments

Cohort Analysis in the Age of Digital Twins

To be actionable, Big Data and Data Science must get down to the level of the individual - whether the individual is a customer, physician, patient, teacher, student, coach, athlete, technician, mechanic or engineer. This is the “Power of One.” By applying data science to the growing wealth of human purchase, interaction and social engagement data, organizations can capture individual’s tendencies, propensities, inclinations, behaviors, patterns, associations, interests, passions,…

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Added by Bill Schmarzo on June 30, 2018 at 5:31am — No Comments

Unintended Consequences of the Wrong Measures

In an attempt to put the patient first in healthcare, Congress and President Obama in 2015 approved a bipartisan bill for United States healthcare reform. The bill is known as “Medicare Access and CHIP Reauthorization Act of 2015”, or MACRA.  Among the major provisions of MACRA is the Quality Payment Program.  Under the Quality Payment Program, physicians, and nurses receive positive, neutral, or negative Medicare payment adjustments based upon a…

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Added by Bill Schmarzo on June 30, 2018 at 5:00am — 3 Comments

Variance-Covariance Matrix: Stock Price Analysis in R

The purpose of a variance-covariance matrix is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.

Why do we use variance-covariance matrices?

A variance-covariance matrix is particularly useful when it comes to analysing the volatility between elements of a group of data. For instance, a variance-covariance matrix has particular applications when it comes to…

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Added by Michael Grogan on June 30, 2018 at 4:30am — No Comments

What is cryptocurrency?

Understanding what cryptocurrency is, is not difficult at all. In fact, the definition of cryptocurrency is "a digital or virtual currency that uses cryptography for security." Since it is a digital currency, cryptocurrency of any kind is going to be difficult to counterfeit. Click here, to learn more about cryptocurrency.

Cryptocurrency is not going to be issued by a central authority, so, it is in theory,…

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Added by Frances C Walker on June 29, 2018 at 8:00am — No Comments

Bill Vorhies Retrospective: Part 5

Bill is the Editorial Director for Data Science Central, and President and Chief Data Scientist at Data-Magnum, providing predictive analytics and big data infrastructure projects as a service. Bill has been an active commercial predictive modeler since 2001.…

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Added by Vincent Granville on June 29, 2018 at 5:30am — No Comments

The Death of “Big Bang” IT Projects

Why is it so hard to change, especially from a business perspective? Why is it so hard to understand that new technology capabilities can enable new technology approaches?  And nowhere is that more evident than in how companies are trying (and failing) to apply the old school “Big Bang” approach to Big Data Analytic projects. 

The “Big Bang” approach likely comes from how businesses have historically gained competitive advantage – through…

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Added by Bill Schmarzo on June 29, 2018 at 5:00am — No Comments

Comparison of top data science libraries for Python, R and Scala [Infographic]

Data science is a promising and exciting field, developing rapidly. The area of data science use cases and influence is continuously expanding, and the toolkit to implement these applications is growing fast. Therefore data scientists should be aware of what are the best solutions for the particular tasks.  

So while many languages can be useful for a data scientist, these three remain the most popular and…

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Added by Igor Bobriakov on June 29, 2018 at 3:30am — No Comments

Why You Should Encourage Your Daughters to Become Data Scientists

The fact that women are underrepresented in technology roles is not a novel observation. Data science roles follow this unfortunate pattern. In the United States, women hold a mere 26% of data jobs. The data science field is booming and the rise of big data is visible almost everywhere. While data science is still new, as a society, we cannot be complacent with women making up just a quarter of the field — we need more women in data…

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Added by Sydney Sarachek on June 28, 2018 at 11:00am — No Comments

5 Questions that Define Your Digital Transformation

I had the opportunity in 2017 to give a 10-minute keynote at DataWorks Summit 2017.  I know what most of you are thinking: Schmarzo can barely introduce himself in 10 minutes! What sort of keynote could he give in just 10 minutes?  And to be honest, I too struggled with what to say.

But after some brainstorming, I decided to pose 5 questions that every organization needs to consider as they prepare themselves for digital transformation.  And…

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Added by Bill Schmarzo on June 28, 2018 at 4:30am — No Comments

Who are our Data Quality Heroes?

Who are our Data Quality Heroes?

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…

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Added by Bart Baesens on June 27, 2018 at 10:30pm — No Comments

Why Every Data Scientist Needs A Data Engineer

This article was written by Lauren Brunk.

The data scientist was deemed the “sexiest job of the 21st century.” The Harvard Business Review reasons that this “hybrid of data hacker, analyst, communicator and trusted adviser” is a rare combination of skills, worth a high paycheck.…

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Added by Amelia Matteson on June 27, 2018 at 2:00pm — No Comments

Introduction to Markov Chains

This article was written by Devin Soni

What are Markov chains, when to use them, and how they work …

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Added by Amelia Matteson on June 27, 2018 at 1:30pm — No Comments

Private Equity and Data Science: Due Diligence Stage

An emerging trend in the private equity space is an enhanced focus on data science.

This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and…

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Added by Howard Friedman on June 27, 2018 at 9:30am — 1 Comment

How History Can Prepare Us for Upcoming AI Revolution or, What is the AI Equivalent to the ¼” Bolt?

Many predict, and warn, that the Artificial Intelligence (AI) Revolution will change the world – and possibly the very essence of mankind.  But society-changing revolutions are not new.  History is full of such revolutions.  What can we learn from those previous revolutions that might provide an indication as to how this AI revolution might play out?

We will examine two other revolutions – the Industrial Revolution and the Information Revolution…

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Added by Bill Schmarzo on June 26, 2018 at 3:30pm — 2 Comments

Data-Driven Culture & the Reptilian Brain

Artificial Intelligence (A.I.) is a wide umbrella of emerging technologies which have the potential to completely transform business and society.

In its immense complexity, artificial intelligence is like human intelligence. Effective analytics teams in business interact in the same way…

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Added by Pedro URIA RECIO on June 26, 2018 at 6:30am — No Comments

Issues with Random Experiments: Attrition

Continuing my thoughts on random experiments and what can go wrong:

Another common problem is Attrition, especially situations where the attrition rate is not randomly distributed (if the attrition rate is randomly distributed then you have lost power in your study).

After random…

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Added by Howard Friedman on June 26, 2018 at 6:00am — No Comments

Crossing the Big Data / Data Science Analytics Chasm

The key of any organization’s digital transformation is becoming more effective at leveraging data and analytics to power their business models. That is, how can organizations exploit the growing bounty of internal and external data sources to uncover new sources of customer, product, service, operational and market insights that they can use to optimize key business and operational processes, mitigate compliance and cybersecurity risks, uncover new monetization opportunities,…

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Added by Bill Schmarzo on June 26, 2018 at 6:00am — 2 Comments

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