AnalyticBridge is one of Data Science Central channels. Below is a selection of popular articles posted a while back:

• 16 Data Science Repositories 
• 12 Interesting Reads for Math Geeks …

Summary:  Quantum computing is now a commercial reality.  Here’s the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning.

Like a magician distracting us with one hand while pulling a fast one with the other Quantum computing has crossed over from research…

Among the marquee brands that a data science professional would like to join, Uber is likely to be top of many lists. But do you really have what it takes to become a Uberite?

BigInsights Principal Raj Dalal met up with Uber’s Chief Data Architect M C Srivas on a recent visit to San Francisco, where, in the course of the hour-long…

There are three development approaches: native, cross-platform, and hybrid. Each of them has its own special features, and brings about different results. Not trying to influence your decision as your outsourcing partner and get the ideal for your business product, let's compare all the technologies.

Every day the number of smartphone users keeps growing and world market of mobile apps keeps developing. Every skilled businessman should have already noticed that…

About two months ago there was  new SaaS product, the Keyword Hero. It’s the only solution to “decrypt” the organic keywords in Google Analytics that users searched for in order to get to one’s website. We do so by buying lots of data off sources such as plugins and matching the data with our customers’ sessions in Google Analytics (side note: the entire algorithm was coded in R before we refactored it in Python to allow scalability and operability with AWS).

Using Keyword…

It’s not easy for a retailer to face ongoing economic challenges. The power of the customers is rising. They have the right to choose the best, and they are not happy with anything less. The competition in every single industry is brutal. We’re not exaggerating when we say that every business battles for survival.

In this war of competitors, data analytics are the most effective weapon. In February 2017, JDA Software Group and PwC (PricewaterhouseCoopers) released…

In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

• Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
• Support Vector Machine: e.g., the optimal choice of Kernel Functions
• Ensemble: e.g., the number of Learning Cycles for Bagging.
• Discriminant Analysis: e.g., Linear/Quadratic; regularization…

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.

Featured Resources and Technical Contributions

• How to Solve the New $1 Million Kaggle Problem -…

Robotics has been and still is an enormous fascination for us humans. Even when we did not have computers, we were fascinated with Mary Shelley's Frankenstein because the concept of creating life out of nothing so resonates with us.

Fast forward to the modern word, making a…

A new competition is posted on Kaggle, and the prize is $1.2 Million. Here we provide some help about solving this new problem: improving home value estimates, sponsored by Zillow. 

We have published in the past about home value forecasting, see here, and also .…

Guest blog by Manmit Shrimali.

Data science, deep learning, citizen scientists, data lake, big data, AI, machine learning, hadoop, Spark, deep learning....Yes, I get these and breathe them every single day. But let me ask you...Have you been asked any of following questions....

1. As a data scientist, how do you bridge a gap between algorithms and business needs?

2. On…

I have personally worked in SAS, SPSS and R, and while I agree there are advantages to SAS for example, R has a definite place as free (open source) software with lots of tried and true modules to enable, and easy to access virtually free training via LinkedIn Learning (or Fka Lynda.com). Many small enterprises cannot afford SAS, or the cost of its training, and many large enterprises are trying to force the transition to open source solutions to save money. I have found R extremely easy to…

For python programmers, scikit-learn is one of the best libraries to build Machine Learning applications with. It is ideal for beginners because it has a really simple interface, it is well documented with many examples and tutorials.

Besides supervised machine learning (classification and regression), it can also be used for clustering, dimensionality reduction, feature extraction and engineering,  and pre-processing the data. The interface is consistent over all of these methods, so…

Out of curiosity, I was checking recent articles published in Journal of the American Statistical Association, as I used to publish in such journals 20 years ago, during my post-doctorate years. I did find some interesting articles, but when I tried to access them, I was asked to fork over $40 to get online access for 24 hours, and $226 to get online access for 30 days.…

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. 

17 Great Blogs Posted in the last 12 Months…

Introduction

The quarterly earnings call is a critical event for publicly traded companies. Each call serves multiple purposes. It is both an important source of information for investors and an opportunity for a company to present a narrative of operational performance, financial health, and strategic vision in their own terms. It’s also an ideal opportunity for executives seeking to manage and optimize outcomes.

The advent of AI makes it plausible for…

There seems to be a lot confusion about the role of programming in relation to the Data Science platforms that research firms Gartner and Forrester have identified as the future of Data Science in large corporations.  For example, numerous people have stated that SAS is pushing a drag-and-drop platform (Enterprise Miner) that somehow limits choices and is destined to fail due to the fact that using programming (that is, R) allows greater flexibility.

It’s certainly…

Here is our selection of featured articles and resources posted since Monday.

• Overview of AI and Role of NLP in Big Data 
• Which classifier has the best performance? …