Featured Blog Posts – December 2016 Archive (71)

ALDI – A New Paradigm for Integrating Marketing Analytics with Data Science

Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn’t happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a…


Added by Srividya Kannan Ramachandran on December 21, 2016 at 3:00pm — No Comments

Where are developers looking next?

As part of the research underpinning Developer Economics we actively monitor industry trends and opportunities, looking for new areas of significant developer interest. In our Developer Economics survey, we invested in trends in Data Science and Machine Learning among other areas of emerging tech- the latter probably being  the least hyped emerging tech space with the most developer activity. 

Making sense of…


Added by Vanessa Measom on December 21, 2016 at 1:30am — No Comments

What are the Big Guys Using?

Summary:  The largest companies utilizing the most data science resources are moving rapidly toward more integrated advanced analytic platforms.  The features they are demanding are evolving to promote speed, simplicity, quality, and manageability.  This has some interesting implications for open source R and Python widely taught in schools but significantly less necessary with these more sophisticated platforms.



Added by William Vorhies on December 20, 2016 at 8:38am — 6 Comments

Top 30 Data Science Articles of the Year

As we approach 2017, we have compiled a list of the most popular data science, machine learning, deep learning and related articles published on DSC in 2016. Many great articles have not been included yet as they are very recent, but I have no doubt that they will make it to the top when we will publish our 2017 selection. Also, click here to check out the best…


Added by Vincent Granville on December 18, 2016 at 1:30pm — No Comments

Preambles and Future Directions

I will be using this blog to assemble a number of different concepts that I introduced over many years in previous blogs (indicated in bold); then I will explain where all of this will be going in the future.  I am turning 50 years old in a couple of weeks, and I find that I habitually take inventory of my belongings these days before beginning any lengthy mission or journey.  I recently acquired a fairly expensive device called a CPAP machine.  It resembles a small stereo with…


Added by Don Philip Faithful on December 18, 2016 at 8:30am — 2 Comments

Dark Data: The billion dollar opportunity

Every organization collects, stores and retains portions of dark data. It’s the digital equivalent of emotional baggage which hangs around after every user interaction, transaction, and customer engagement.

In fact, not using data effectively is …


Added by Mark Ross-Smith on December 18, 2016 at 4:30am — 1 Comment

Great list of resources: data science, visualization, machine learning, big data

Fantastic resource created by Andrea Motosi. I've only included the 5 categories that are the most relevant to our audience, though it has 31 categories total, including a few on distributed systems and Hadoop. Click here to view the 31 categories. You might also want to check our our our internal resources (the first section below).…


Added by Vincent Granville on December 17, 2016 at 11:00pm — 2 Comments

A Beautiful Probability Theorem

We all know that, given two events A and B, the probability of the union A U B is given by the formula P(A U B) = P(A) + P(B) - P( AB) where AB represents the intersection of A and B. Most of us even know that

P(A U B U C) = P(A) + P(B) + P(C) - { P(AB) + P(AC) + P(BC) } + P(ABC)

In particular, if the events are independent, it becomes:

1 - P(A U B U C) = 1 - { P(A) + P(B) +…


Added by Vincent Granville on December 17, 2016 at 1:00pm — 1 Comment

Deciphering the Neural Language Model

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail.

One of the assignments in…


Added by Burak Himmetoglu on December 17, 2016 at 10:00am — No Comments

Weekly Digest, December 19

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.



Added by Vincent Granville on December 17, 2016 at 9:30am — No Comments

Want to know how to choose Machine Learning algorithm?

Machine Learning is the foundation for today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms.
Some of the most common examples of machine learning are Netflix’s algorithms to give movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend products…

Added by Sandeep Raut on December 16, 2016 at 3:30pm — 4 Comments

History of MySQL

This article was written on Database Friends. It is on Database, visualization and big data news. Blogs are about MySQL, PostgreSQL, Oracle, SQL Server and other DBMS.

MySQL is one of the most widely used open source relational database management systems in the world. With a total distribution amounting to more than 100 million worldwide, the software has become the first choice of large data management corporations spanning over a wide range of internet…


Added by Emmanuelle Rieuf on December 16, 2016 at 8:30am — No Comments

Why data preparation should not be overlooked

Data is the new language today. Data leads to insights, and insights help organizations to make actionable business decisions. However, sourcing the data and preparing it for the analysis is one of the tedious tasks organizations face these days. Analysts devote a lot of time in searching and gathering the right data. According to a research firm,…

Added by Ashish Sukhadeve on December 16, 2016 at 2:00am — 3 Comments

Your Guide to Master Hypothesis Testing in Statistics

This article was written by Sunil Ray. Sunil is a Business Analytics and Intelligence professional with deep experience.

Introduction – the difference in mindset

I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently…


Added by Emmanuelle Rieuf on December 15, 2016 at 10:00pm — 1 Comment

Naive Bayes Classification explained with Python code


Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us).

Within Machine Learning many tasks are - or can be reformulated as - classification tasks.

In classification tasks we are trying to produce a model which can give the correlation…


Added by Ahmet Taspinar on December 15, 2016 at 2:00pm — No Comments

How to Discover Hidden Value in Your Customer Journey

The world of business and customer service has changed immensely over the past few years. Whereas once business was largely driven by outbound marketing and advertising, in today’s world you have to consider the customer experience and your customers’ journey as they interact with your brand.


Today, thanks to the advent of…


Added by Ronald van Loon on December 15, 2016 at 8:00am — No Comments

10 Data Science, Machine Learning and IoT Predictions for 2017

It's time again to share your predictions for 2017. I did my homework and came with these 10 predictions. I invite you to post your predictions in the comment section, or write a blog about it. Ramon Chen's predictions are posted here, while you can read Tableau's prediction…


Added by Vincent Granville on December 14, 2016 at 10:00am — 3 Comments

Is it time to consider data in motion in your big data projects?

As data scientists plan and evolve their big data programs, it is time to evaluate the value of adding data in motion to the data lake. What’s the difference between data in motion and data at rest? We are all familiar with data at rest. This is the data we are most accustomed to working with. Data at rest is persistent data that is stored for some period of time on either disk or in memory like sales transaction records or account information. Data in motion, on the other hand, is…


Added by Michael E. Serrano on December 14, 2016 at 7:30am — 1 Comment

Top programming languages that will be most popular in 2017

The post 'Top programming languages that will be most popular in 2017' was originally posted on the HackerEarth blog.

Which is the most preferred programming language or the top programming languages to learn across the globe? How do we judge it and what should be the criteria?

'By most preferred language, we do…


Added by ARPIT MISHRA on December 13, 2016 at 9:00pm — 3 Comments

Has Deep Learning Made Traditional Machine Learning Irrelevant?

Summary:  The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant.  Here we explore both sides of that argument.


On Quora the other day I saw a question from an aspiring data scientist that asked – since all the…


Added by William Vorhies on December 13, 2016 at 9:24am — 4 Comments

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