A Data Science Central Community
Added by Vincent Granville on April 21, 2017 at 10:30am — No Comments
There are three ways to look at data. The first is analytics. This is when you look at data from the (potentially very recent) past. Think analytics. It allows you to explore the questions what happened and why did it happen? The second is monitoring. This is looking at things as they happen. In many…Continue
The Monte Carlo method is an simple way to solve very difficult probabilistic problems. This text is a very simple, didactic introduction to this subject, a mixture of history, mathematics and mythology.
The method has origins in the World War II, proposed by the Polish American mathematician Stanislaw Ulam and Hungary American mathematician John Von Neumann.…
Added by Arnaldo Gunzi on April 11, 2017 at 4:00pm — No Comments
Depending on the business objectives, social media analytics can take four different forms, namely, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Social media data is the new gold and analytics is its digging tool. Social Media Analytics (SMA) is the art and science of extracting valuable hidden business insights from social media media data (Khan, 2015) . SMA turns the…Continue
Added by Gohar Feroz Khan on April 10, 2017 at 1:30pm — No Comments
Many businesses, especially small businesses, underestimate the danger their company’s data is in. They have the idea that because they’re fairly small, no one would want to try to steal the customer information they collect. After all, why go after a few thousand customer records when you could attack a large corporation and potentially walk away with tens of…Continue
Added by Peter Davidson on April 17, 2017 at 6:00am — No Comments
We are interested here in factoring numbers that are a product of two very large primes. Such numbers are used by encryption algorithms such as RSA, and the prime factors represent the keys (public and private) of the encryption code. Here you will also learn how data science techniques are applied to big data, including visualization, to derive insights. This article is good reading for the data scientist in training, who might not necessarily have easy access to interesting data: here the…Continue
Added by Vincent Granville on April 6, 2017 at 7:30pm — No Comments
Or of any celestial body. Here I discuss a solution that can be explained to high school students, to get them interested in mathematics, statistics and probabilities. A few interesting related problems further enhance the pedagogical value of this discussion.
I stumbled upon this kind of problems when learning advanced mathematics in my postgraduate studies, in a course entitled stochastic geometry. Just formulating the problem required advanced knowledge of sophisticated…Continue
Added by Vincent Granville on March 3, 2017 at 1:00am — No Comments
I published a post about the current status of "Data Scientist" in Japan, as a periodic follow-up analysis since two years ago. Its trend still remains, but it's beyond my anticipation at that time.
Indeed growing trend of "Artificial Intelligence" in Japan is steeper than…Continue
Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.
Deep learning is all the rage. You hear about it in the news, you read it about it in the news and it’s all over popular culture as well.…Continue
Added by Malia Keirsey on December 5, 2016 at 12:00pm — No Comments
Randomness is all around us. Its existence sends fear into the hearts of predictive analytics specialists everywhere -- if a process is truly random, then it is not predictable, in the analytic sense of that term. Randomness refers to the absence of patterns, order, coherence, and predictability in a system.
R vs Python. Which language should you choose?
R is great for mathematical people. Think of R as spreadsheets on steroids. A lot of people progress from spreadsheets to R. These people are usually statisticians at heart.
Python, of the other hand, is more…Continue
Added by Olga on September 27, 2016 at 7:30pm — No Comments
This post is the fourth part of the multi-part series on how to build a search engine –
Added by Vivek Kalyanarangan on January 10, 2017 at 1:00am — No Comments
This post is the third part of the multi-part series on how to build a search engine –
Added by Vivek Kalyanarangan on December 30, 2016 at 6:00am — No Comments
Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (positive class) and who doesn’t (negative class). We have a dataset from the financial world and want to know which customers will default on their credit (positive class) and which customers will not (negative class).
To do this, we can train a Classifier with a ‘training dataset’ and after such a Classifier is…
Added by ahmet taspinar on December 22, 2016 at 10:30am — No Comments
This post is the second part of the multi-part series on how to build a search engine –
Added by Vivek Kalyanarangan on December 23, 2016 at 10:30am — No Comments
In this multi-part series, we will explore how to build a search engine. It will be quite powerful and industrial strength. The first part will focus on getting the right tools and getting technology stack ready. We will build this search engine with an AngularJS front-end and use elasticsearch as the computation back end.
This post is the first part of the multi-part series on how to build a search engine –
Added by Vivek Kalyanarangan on December 16, 2016 at 2:00am — No Comments
The purpose of this article is to generate new theorems of probability and to find out some applications of these theorems. In this case, suppose that we have a covered basket that contains many dices. In many blind tests, we will reach in and pull out a dice and set it on the table on one row from left to right. It is clear, each dice has six events (choices) including 1, 2, 3, 4, 5, and 6.
What is the application of these theorems (1 and 2)?
Added by Gholamreza Soleimani on November 16, 2016 at 3:00am — No Comments
Traditional computer systems and software applications don’t have what it takes to support big data. If you want to collect, store, refine, or analyze big data, you have to have the right tools. Check out the following ten tools that are specifically designed with big data in mind.
If you know, or are willing to…Continue
Added by Rick Riddle on November 10, 2016 at 10:00am — No Comments