Nowadays, most data scientists use either Python or R as their main programming language. That was also my case until I met Julia earlier this year. Julia promises performance comparable to statically typed compiled languages (like C) while keeping the rapid development features of interpreted languages (like Python, R or Matlab). This performance is achieved by just-in-time (JIT) compilation. Instead of interpreting code, Julia compiles code in runtime. While JIT compilation has…

Visualization has become a key application of data science in the telecommunications industry.

Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand and availability.

Data

To illustrate this point, a k-means clustering algorithm is used…

There has been much hype surrounding deep learning and data science learning in recent times, and one of the cornerstones of deep learning is the neural network. In this article, we will look at what a neural network is and get familiar with the relevant terminologies.

In simplest terms, a neural network is an interconnection of neurons. Now the question arises, what is a neuron? To understand neurons in deep learning, we first…

Functions are used to simplify a series of calculations.

For instance, let us suppose that there exists an array of numbers which we wish to add to another variable. Instead of carrying out separate calculations for each number in the array, it would be much easier to simply create a function that does this for us automatically.

A function in R generally works by:

(a) Defining the variables to include in the function and the calculation. e.g. to add two…

Testing is an often overlooked yet critical component of any software system. In some ways this is more true of models than traditional software. The reason is that computational systems must function correctly at both the system level and the model level. This article provides some guidelines and tips to increase the certainty around the correctness of your models.

Guiding Principles

One of my mantras is that a good tool extends our ability and never gets in…

In this post I will present a simple way how to export your regression results (or output) from R into Microsoft Word. Previously, I have written a tutorial how to create Table 1 with study characteristics and to export into Microsoft Word. These posts are especially useful for researchers who prepare their manuscript for publication in peer-reviewed journals.

Get the results…

In research, especially in medical research, we describe characteristics of our study populations through Table 1. The Table 1 contain information about the mean for continue/scale variable, and proportion for categorical variable. For example: we say that the mean of systolic blood pressure in our study population is 145 mmHg, or 30% of participants are smokers. Since is called Table 1, means that is the first table in the manuscript.

To create the Table 1…

In statistics, a outlier is defined as a observation which stands far away from the most of other observations. Often a outlier is present due to the measurements error. Therefore, one of the most important task in data analysis is to identify and (if is necessary) to remove the outliers.

There are different methods to detect the outliers, including standard deviation approach and Tukey’s method which use interquartile (IQR) range approach. In this post I will use…

Here are three useful resources for learning about Data Science:

• Machine Learning and Deep Learning Tutorial List: This link contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, codes, articles and other resources

• Data Science…

I created an R package for exploratory data analysis. You can read about it and install it here.  

The package contains several tools to perform initial exploratory analysis on any input dataset. It includes custom functions for plotting the data as well as performing different kinds of analyses such as univariate, bivariate and multivariate investigation which is the first step of any…

Data is growing, and hence is the requirement for perfect decision making by companies. In this era where analytics is the new buzzword, companies are looking for able and determined Data Scientists.

But as always, the quest to get the sexiest and the highest paying job is not an easy task. For acquiring such a demanding position one must be well skilled and experienced.

Don’t freak out! Because now a days a lot of resources and tips are available to be perfect in what you are…

Photo: Nathaniel Welch

It turns out I’m not the only one who thinks AI alarmism is a bit out of hand. The ITIF Luddite Award nominations include “alarmists, even including respected luminaries such as Elon Musk and Stephen Hawking, touting an artificial intelligence…

Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith’s invisible hand…

This tutorial was authored by Jose A Dianes, PhD.

Introduction

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster…

Excerpt reprinted with permission from ckmadvisors.com

This weekend sees Super Bowl XLVIII come to New York (yes, we're well aware that the stadium is technically in New Jersey). Earlier this week one of our data scientists noticed the Empire State Building lights putting on quite a show. A quick search revealed that the iconic building's…

By: Nicholas Hartman, Director at CKM Advisors

Today we'd like to share with you some fun charts that have come out of our internal linguistics research efforts. Specifically, studying weather events by analyzing social media traffic from Twitter. 

We do not specialize in social media and most of our data analytics work focuses on the…