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For Companies, Data Analytics is a Pain; But Why?

Businesses across the globe are facing the brunt, one of huge data influx and second of increasing data complexity and of course the market volatility. To address these challenges, companies and all their verticals are turning to data-driven analytics and insights as a means to better understand their organization’s customer bases and to grow their businesses; and manage the increasing uncertainty up to a certain extent. 

The shift from conventional…

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Added by Chirag Shivalker on June 22, 2017 at 1:30pm — No Comments

Descriptive, Predictive & Prescriptive Analytics will fail to Help You Understand Your Business

It has been a practice followed religiously by companies and organizations to analyze how they have performed over a…

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Added by Chirag Shivalker on May 12, 2017 at 7:30am — No Comments

Business Analytics and Intelligence Compared

Business analytics and business intelligence are two different notions, but only few people understand the difference. Interestingly, even people who have worked in the business industry struggle with this particular topic or have various different answers when someone asks the question 'What is the difference between business analytics and business intelligence?'

Some people define business analytics as an umbrella term and place intelligence as one of its parts, together with data…

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Added by Laura Buckler on April 25, 2017 at 8:00am — 1 Comment

12 Interesting Reads for Math Geeks

Many data scientists have a passion for mathematics, and many modern math problems can be explored using data science. Below is a selection of interesting articles, many about challenging, deep mathematical problems, by a data scientist who developed math-free algorithms. Some of these articles cover statistical theory and thus belong to data science,…

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Added by Vincent Granville on May 3, 2017 at 11:30am — No Comments

16 Data Science Repositories

Each one is a repository in its own, and they cover topics such as time series, regression, outliers, clustering, correlation, Hadoop, deep learning, Python, IoT, data sets, cheat sheets, infographics, and more (AI coming soon.) 

Each one features a number of popular articles and resources.

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Added by Vincent Granville on May 3, 2017 at 10:00am — No Comments

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

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