AI has snuck into our daily lives and there is nothing to stop it. Not only does AI power autonomous vehicles, but AI already decides what products you should buy, what movies you should watch, what music you should listen to, and whom you should date. If you talk to your iPhone, Google Home, and Amazon Echo, you are talking to an AI engine that powers these personal virtual assistants. AI decides whether you are approved for a loan, determines the outcome of a job applications, identifies…

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.

• Webinar: Job Trends in Data Science -- Learn about the hiring trends, top skill sets, industry predictions, and career tips in data science at this…

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis…

For some organizations the title might not be too meaningful, but it is meant to emphasize a conceptual point.  The managers in an organization do the managing.  They do this at least theoretically by managing resources – including people.  Then in the end, if fortune is smiling, the markets will adhere to the game plan, and the company will succeed.  However, companies routinely fail, creating products and services that the markets don’t care to purchase.  They sometimes use methods…

This question was recently posted on Quora: What is a full stack data scientist? Below is my answer.

Most answers focus on the technical skills a full stack data scientist should have. This is only the tip of the iceberg. Put it differently, a full stack data scientist is also able to start a company, any company, from the ground-up, and leverage analytics in all aspects that makes a business succeed. It requires the ability to run a business, hire people, automate, find the…

Here I am writing my first post, I posponed it for a long time… In this article I would like to share my experience installing and testing basic Apache Kafka features. If you are new in the Big Data ecosystem let me give you some short concepts.

Kafka is a distributed streaming platform which means is intended for publish and subscribe to streams of records, similar to a message queue or…

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

Technical Resources

• Sequence Modeling with Neural Networks - Part I 
• R, Python, Julia - and Polyglot …

What is K Means Clustering?

Clustering means grouping things which are similar or have features in common and so is the purpose of k-means clustering. K-means clustering is an unsupervised machine learning algorithm for clustering ‘n’ observations into ‘k’ clusters where k is predefined or user-defined constant. The main idea is to define k centroids, one for each cluster.

The K Means algorithm…

TensorFlow 1.x Deep Learning Cookbook

By Antonio Gulli, Amita Kapoor

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x

In this book, you will learn how to efficiently use TensorFlow, Google's open…

Oh,the lowly data engineer.  Harvard Business Review declared the role of the data scientist as “the sexiest job in the 21^stcentury.” But the data engineer labors away in near obscurity acquiring, transforming, enriching, munging and preparing data for the data scientist to do their black magic.

In addition to building data pipelines –…

Blockchain, which is a form of a decentralized digital ledger technology, has gained enormous attention in the recent years, precisely, since 2004. In fact, it has disrupted how major industries operate both locally and internationally.

Blockchain and security,…

Guest blog post by Zied HY. Zied is Senior Data Scientist at Capgemini Consulting. He is specialized in building predictive models utilizing both traditional statistical methods (Generalized Linear Models, Mixed Effects Models, Ridge, Lasso, etc.) and modern machine learning techniques (XGBoost, Random Forests, Kernel Methods, neural networks,…

There are so many confusing and sometimes even counter-intuitive concepts in statistics.  I mean, come on…even explaining the differences between Null Hypothesis and Alternative Hypothesis can be an ordeal.  All I want to do is to understand and quantify the cost of my analytical models being wrong.

For example, let’s say that I’m a shepherd who has bad eyesight and have a hard time distinguishing between a wolf and a sheep dog.  That’s obviously…

Blockchain technology is fast emerging as one of the most promising innovations in the information technology (IT) sector. It offers a host of advantages, such as enabling faster settlement, omitting the need for financial institutions to authenticate transactions, minimizing duplicative recordkeeping, eliminating reconciliation, and reducing error rates. These benefits not only hold the ability to revolutionize the banking, financial services, and insurance (BFSI) sector, but also offers a…

Introduction

In a regular neural network, the input is transformed through a series of hidden layers having multiple neurons. Each neuron is connected to all the neurons in the previous and the following layers. This arrangement is called a fully connected layer and the last layer is the output layer. In Computer Vision applications where the input is an image, we use convolutional neural network…

Last week, the financial times wrote that there was a sharp fall in MBA applications in the USA.

The Elite MBA programs were not affected by this trend, but most others were 

Many factors contribute to this fall in applications – including Visa/immigration issues, rising…

Summary:  If you’re still writing code to clean and prep your data you're missing big opportunities for efficiency and consistency with modern data prep platforms.

Two things are true. 

• Data prep still occupies about 80% of our model building time – the least enjoyable part.
• If you’re still writing code…

A poll released recently showed Python increasing its lead over R as the language of choice for analytics professionals. Setting aside questions of the representativeness to the analytics practitioner population of…

Proper delivery of healthcare requires clear, accurate and timely communication between medical professionals, caregivers and patients. The challenge for many organizations is protecting the data from unauthorized access and privacy breaches while achieving this. Let’s discuss the importance of data security in healthcare communications before addressing how this can be accomplished.

The Legal Mandate

The Health Insurance Portability and Accountability Act,…

This post came out of the inspiration I got after I read Rafael Knuth's Learning Sabbatical. I read part 1 of his sabbatical too and I felt compelled to put my experience and future plan .

Background:  After…