“95% of the purchase decisions happen in the subconscious”

Yes, you read it right! 95% of the purchase decisions happen in our sub-conscious — according to …

Here is our selection of featured articles, technical resources, and forum questions posted since Monday:

New Technical Resources

• 21 Statistical Concepts Explained in Simple English - Part 4 …

There is a library called threading in Python and it uses threads (rather than just processes) to implement parallelism. This may be surprising news if you know about the Python's Global Interpreter Lock, or GIL, but it actually works well for certain instances without violating the GIL. And this is all done without any overhead -- simply define…

Our world is fuelled by information, today more than ever before. Social media, technology, the internet at large – all of these contribute to a curious society who will not tolerate a knowledge gap, especially when it comes to business and government. The amount of data online is staggering, and if unlocked, could contribute to a more efficient, responsive and effective society, spurring on economic growth and…

The third annual University of San Francisco (USF) MBA class Golden State Warriors analytics exercise provided an opportunity to test the students’ ability to “Think Like a Data Scientist” with respect to identifying and quantifying variables that might be better predictors of performance for the Golden State Warriors professional basketball team. This was also an opportunity to test and fine-tune the Hypothesis Development Canvas, and boy was that an eye-opener for me. The…

This article was written by Luis Verde Arregoitia on his personal research page. 

For various kinds of analyses, we often end up plotting point data in two dimensions for two or groups. This includes Principal Component Analyses,…

A couple of quintillion of bytes are generated every day. Data is all around you. And every day we perceive new data and our brain processes it constantly. But we cannot constantly work with huge data volumes efficiently, that is where special algorithms for processing Big Data come in. Bis Data contributes to many fields of activity, and today it is rapidly being integrated into the real estate sector.…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…

This article is an excerpt from “Natural Language Processing and Computational Linguistics” published by Packt.…

This article was written by Tristan Handy.

This post is about how to create the analytics competency at your organization. It’s not about what metrics to track (there are plenty of good posts about that), it’s about how to actually get your business to produce them. As it turns out, the implementation question  - How do I build a…

Summary:  There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways.  If you’re going to explain or recommend AI to a non-data scientist, it’s important to understand the difference.

For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming.  “Big Data” – totally misleading…

Every day, we create 2.5 quintillion bytes of data and over the last two years alone, we have created over 90% of the world’s data! The amount of data we produce is staggering making it difficult for the data analysts all over the world to analyze data and project the results of such analysis into an…

There are transformative technologies in the world today with consistent effect and reliability in their promise to alter or change the ecosystem. Industries have transformed, and early adopters with it, while others race to understand how best to adapt or integrate said emerging technologies into their organizations in an effective and seamless…

R is a software programming language developed in 1993.  In New Zealand, two professors of Auckland University Ross Ihaka and Robert Gentleman first conceived R. The most stable beta version of R was made in 2000. Here ‘R’ holds an extensive catalog produced of statistics and graphics methods. These methods include a machine learning algorithm, time series, linear regression, statistical inferences and many more.…

The information technology (IT) infrastructure in traditional organizations, particularly in the insurance industry, typically contains three components: 1) the legacy system, e.g. IBM mainframe for storing transactional data; 2) the distributed system, e.g. Microsoft Azure or Amazon Web Services for data governance and…

Two weeks ago, I was invited to present about Machine Learning and its applications in Quantitative Finance at a conference in London, UK.

Without a break, I went through four research papers which I completed over the last two years with my co-author; here is one page of the presentation. 

1. CDS Rate Construction Methods by Machine Learning Techniques: Methodology and Results; Brummelhuis and Luo, forthcoming in…

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. To subscribe, follow this…

Independently published (November 20, 2018). 78 pages.

This book intends to provide an overview of Machine Learning and its algorithms & models with help of R software. Machine learning forms the basis for Artificial Intelligence which will play a crucial role in day to day life of human beings in the near future. A basic understanding of machine learning is required, as its application is widely seen in different fields such as banks and financial sectors,…

New book, in progress. By Andriy Burkov, Machine Learning Team Leader at Gartner.

The following chapters are currently available:

Foreword

Chapter 1: Introduction

Part I: Supervised Learning

Chapter 2: Notation and Definitions

Chapter 3: Fundamental Algorithms

Chapter 4: Anatomy of a Learning Algorithm

Chapter 5: Basic Practice

Chapter 6: Neural Networks and Deep…

My last University of San Francisco School of Management class of the semester is coming up this week. However instead of a normally boring presentation from me to cap the semester, we are going to review a few infographics to summarize our lessons from the semester. 

I also want to use this blog to give credit to Arielle Winchester for her creativity and patience to work with me in the construction of these infographics.  Here are my Top 10 infographics from…