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50 Important Things You Need to Know About Data Science

Below are a few extract from a long list of quotes by leading data scientists. Click here to read the whole list. Also, do not miss our new popular articles: How to lie with data, and…

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Added by Vincent Granville on April 21, 2017 at 10:30am — No Comments

The Ultimate Guide for Choosing Algorithms for Predictive Modeling

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…

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Added by Steven M. Mehler on April 3, 2017 at 12:00am — 1 Comment

Monte Carlo Analysis and Simulation

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.…

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Added by Arnaldo Gunzi on April 11, 2017 at 4:00pm — No Comments

4 types of social media analytics explained

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…

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Added by Gohar Feroz Khan on April 10, 2017 at 1:30pm — No Comments

Tips for Reducing Fraud and Bolstering Customer Data Security

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…

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Added by Peter Davidson on April 17, 2017 at 6:00am — No Comments

Factoring Massive Numbers: Machine Learning Approach - Why and How

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…

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Added by Vincent Granville on April 6, 2017 at 7:30pm — No Comments

Math Challenge: Computing the Average Rotational Speed of Earth

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…

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Added by Vincent Granville on March 3, 2017 at 1:00am — No Comments

In Japan, "Artificial Intelligence" comes to be a super star while "Data Scientist" is fading away

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…

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Added by Takashi J. OZAKI on January 13, 2017 at 6:30am — 1 Comment

12 Statistical and Machine Learning Methods that Every Data Scientist Should Know

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.

  1. Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)
  2. Multiple Regression (Linear Models)
  3. General Linear Models (GLM: Logistic Regression, Poisson Regression)
  4. Random Forest
  5. Xgboost (eXtreme Gradient Boosted Trees)
  6. Deep Learning
  7. Bayesian Modeling with…
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Added by Takashi J. OZAKI on January 8, 2017 at 6:30am — 1 Comment

Deep Learning in Python: Getting Started

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.…

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Added by Malia Keirsey on December 5, 2016 at 12:00pm — No Comments

7 Traps to Avoid Being Fooled by Statistical Randomness

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. 

Unfortunately, we…

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Added by Kirk Borne on January 9, 2017 at 6:00pm — 5 Comments

Blog - R vs Python. Which one has higher demand on the job market? A short study

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…

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Added by Olga on September 27, 2016 at 7:30pm — No Comments

How to build a search engine: Part 4

This post is the fourth part of the multi-part series on how to build a search engine –

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Added by Vivek Kalyanarangan on January 10, 2017 at 1:00am — No Comments

How to build a search engine: Part 3

 

This post is the third part of the multi-part series on how to build a search engine –

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Added by Vivek Kalyanarangan on December 30, 2016 at 6:00am — No Comments

The Perceptron Algorithm explained with Python code

1. Introduction

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…

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Added by ahmet taspinar on December 22, 2016 at 10:30am — No Comments

How to build a search engine - Part 2: Configuring elasticsearch

This post is the second part of the multi-part series on how to build a search engine –

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Added by Vivek Kalyanarangan on December 23, 2016 at 10:30am — No Comments

How to build a search engine: Part 1

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 –

  • How to build a search engine – Part 1: Installing the tools and…
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Added by Vivek Kalyanarangan on December 16, 2016 at 2:00am — No Comments

The Generating New Probability Theorems

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)?

Let me…

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Added by Gholamreza Soleimani on November 16, 2016 at 3:00am — No Comments

10 Tools For Working With Big Data For Successful Analytics

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.

 1.    Hadoop

If you know, or are willing to…

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Added by Diana Beyer on November 17, 2016 at 8:30am — 1 Comment

How can organizations successfully convert big data into real-world decisions?

The word wide web is turning into a colossal heap of data that is being stored at hundreds and thousands of datacenters across the world. According to a recent research made by…

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Added by Rick Riddle on November 10, 2016 at 10:00am — No Comments

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