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Featured Blog Posts – October 2016 Archive (76)

IoT as a Metaphor

What exactly is “IoT”? Internet of Things, yes; but what does that mean?

Internet of Things is a structural definition; it says there are “Things” such as sensors and devices (on machines or people) connected together in a Network. So what? What does a Network of Sensors & Devices allow us to DO? What is the functional description of IoT?

Being able to connect things together is “table stakes” at the intelligence augmentation game. What…

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Added by PG Madhavan on October 16, 2016 at 9:30am — No Comments

Tricks in Face Recognition

Last year I started developing a Face Recognition model. I started with static pictures and using Wolfram Mathematica. This year I found out we can do the same job using OpenCV in Python, or creating specific filters in R and applying Weierstrass and Gaussian transformation.

There are lots of difficulties in recognizing faces of the same person, like: position, rotation of face, age, feeling, brightness, gamma, contrast, gamma, saturation, obstacles like hands,hair and so…

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Added by Rubens Zimbres on October 15, 2016 at 4:00am — No Comments

Weekly Digest, October 17

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

  • Harness the business power of big data. Earn your…
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Added by Vincent Granville on October 14, 2016 at 5:00pm — No Comments

Looking at the Big Picture: The Future of Big Data



At first there were a lot of different opinions, but now, almost everybody agrees that big data was able to take over the whole business world. A lot of businesses are relying on it and improving the tools they use for taming big data on a daily basis. They all agree that the importance of big data is huge and that everyone who wants to stay competitive in the business world…

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Added by Ivan Dimitrijevic on October 14, 2016 at 3:00am — No Comments

Response Modeling using Machine Learning Techniques in R

Response Modeling Using Machine Learning Techniques with R-Programming (WIP). I have tried to exhibit credit scoring case studies with German Credit Data.



This article includes detail programming of predictive modeling

1. Univariate And Bi-Variate Analysis

2. Information Value and Weight Evidence to access prediction power of variables

3. Multivariate Analysis and Dimension Reduction using Variable Clustering

4. Different Machine Learning Techniques and their…

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Added by Ariful Islam Mondal on October 13, 2016 at 10:30am — No Comments

Understanding machine learning: Do we need machine learning at all?

In the previous post of our Understanding machine learning series, we presented how machines learn through multiple experiences. We also explained how, in some cases, human beings are much better at interpreting data than machines. In many tasks machines still can’t replace humans, who understand surrounding reality better and can make more accurate decisions.

Machines can be given a…

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Added by Algolytics on October 13, 2016 at 4:30am — No Comments

Measuring Similarity between Objects

The ability to recognize objects and their relationships is at the core of intelligent behavior. This, in turn, depend on one’s ability of perceiving similarity or dissimilarity between objects, be physical or abstract ones. Hence, if we are interested to make computers behave with any degree of intelligence, we have to write programs that can work with relevant representation of objects and means to compute their similarities or lack thereof, i.e., dissimilarity (obviously, they are…

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Added by Arijit Laha on October 12, 2016 at 11:00pm — 1 Comment

Honeypot Turing Test

The honeypot is a method of cybersecurity in which a bait (‘honey’) system/network is designed to emulate or act as a real system/network to divert malicious attacks upon the actual real system/network.  The honeypot may act to mitigate, block, and in some cases capture the malicious behavior.  The concept of the honeypot probably originated from two books, “The Cuckoos Egg” by Clifford Stoll and “An Evening with Berferd” by Bill Chewick, both describing the authors’ own personal…

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Added by Arshak Navruzyan on October 12, 2016 at 7:00pm — No Comments

Differences between Data Mining and Predictive Analytics

What is Data Mining?

Data mining is an integrated application in the Data Warehouse and describes a systematic process for pattern recognition in large data sets to identify conclusions and relationships. Using statistical methods, or genetic algorithms, data files can be automatically searched for statistical anomalies, patterns or rules.

Wikipedia defines Data Mining as “Data mining is an interdisciplinary subfield of computer science. It is the computational process…

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Added by Jason Li on October 12, 2016 at 5:30pm — 7 Comments

Aligning Data Science and organizational structure: how companies are solving this issue?

Politecnico di Milano is investigating on it.

The proliferation of data and the huge potentialities for companies to turn data into valuable insights are increasing more and more the demand of Data Scientists.

But what skills and educational background must a Data Scientist have? What is its role within the organization? What tools and programming languages does he/she mostly use? These are some of the questions that the Observatory for Big Data Analytics of Politecnico di…

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Added by Alessandro Piva on October 12, 2016 at 8:30am — No Comments

Top 30 people in Big Data and Analytics

Innovation Enterprise has compiled a top 30 list for individuals in big data that have had a large impact on the development or popularity of the industry. …

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Added by Emmanuelle Rieuf on October 12, 2016 at 8:30am — 1 Comment

Looking To Evolve Your Business Through Software Intelligence?

Let’s take a trip down memory lane

 

Since the dawn of time, man has been using analytical reasoning and procured data to enhance his business. Take the example of the farmer, who plans his crop cycles on assumptions that are based on observations made, or what we may call now as data collected on the basis of the monsoon season and seed quality of the last few years. Thinking about the heat during the summer, the humidity, the…

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Added by Lucjan Zaborowski on October 12, 2016 at 2:30am — No Comments

Towards a New Data Modelling Architecture - Part 2

Atomic Information Resource (AIR)

How do we design a data model, how do we connect data, how do we represent information, how do we store or retrieve them ? These are all fundamental questions in data modeling but there is a common key to unlock them. You have to start by defining a primitive information resource, and then understand how one can build complex information structures on top of…

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Added by Athanassios Hatzis on October 12, 2016 at 12:30am — No Comments

16 Great IoT Articles Published in 2016

This reference is a part of a new series of DSC articles, offering selected tutorials, references/resources, and interesting articles on subjects such as deep learning, machine learning, data science, deep data science, artificial intelligence, Internet of Things, algorithms, and related topics. It is designed for the busy reader who does not have a lot of time digging into long lists of advanced publications.…

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Added by Vincent Granville on October 11, 2016 at 7:42pm — No Comments

Where Film Locations Abound

Contributed by Ismael Jamie Cruz. He is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on his third class project - Web Scraping (due…

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Added by NYC Data Science Academy on October 11, 2016 at 1:00pm — No Comments

Data Scientist – Still the Best Job in America - Again

Summary:  It’s that time of the year when the major salary surveys come out.  Once again it’s time for Data Scientist to take a victory lap.  This year there’s a lot more detail.

 

It’s that time of the year again when the major surveys come out from O’Reilly, GlassDoor, and Burtch Works that tell us how we’re doing.  Well, no…

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Added by William Vorhies on October 11, 2016 at 7:30am — No Comments

Life Cycle of Data Science Projects

A traditional business problem customized here to data science.

1. Identify the problem

  • Identify metrics used to measure success over baseline (doing nothing)
  • Identify type of problem: prototyping, proof of concept, root cause analysis, predictive analytics, prescriptive analytics,…
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Added by Vincent Granville on October 10, 2016 at 9:00pm — 8 Comments

A Database of 800 Analytics Companies

In a prior post I outlined some thoughts on the outlook for the data analytics sector and referenced a database I prepared of analytics companies.  At the time the list comprised about 400 names categorized into a number of sectors and segments.

I’ve continued to update the list since that time and it now comprises about 800 companies.

 

It can be…

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Added by Gregory Thompson on October 10, 2016 at 1:00pm — 4 Comments

Linear Regression in Astronomy: Cartoon

This image comes from Xkcd, a webcomic of romance, sarcasm, math, and language. Created by Randall Munroe, he is a CNU graduate with a degree in physics. Before starting xkcd, he worked on robots at NASA's Langley Research Center in Virginia.

Source: …

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Added by Emmanuelle Rieuf on October 10, 2016 at 11:30am — 1 Comment

How To Make a Scatter Plot in R for AP Statistics Using R Markdown

I teach AP Statistics in China at an International school and I believe it's important to not only show my students how to do plots and inferential statistics on their TI Nspire calculators, but also in R using ggplot, dplyr, and R Markdown.…

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Added by Kevin Smith on October 9, 2016 at 12:30am — 2 Comments

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