Now published. Enterprise AI: An applications perspective takes a use case driven approach to understand the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a practical and straightforward roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk's new…

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…

Here are a few off-the-beaten-path problems at the intersection of computer science (algorithms), probability, statistical science, set theory, and number theory. While they can easily be understood by beginners, finding a full solution to some of them is not easy, and some of the simple but deep questions below won't be answered for a long time, if ever, even by the best mathematicians living today. In some sense, this is the opposite of classroom exercises, as there is no sure path that…

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 

• Top Data…

You will find here a few tables of random digits, used for simulation purposes and/or testing or integration in statistical, mathematical, and machine learning algorithms. These tables are particularly useful if you want to share your algorithms or simulations, and make them replicable. We also provide techniques to use in applications where secrecy is critical, such as cryptography, bitcoin or lotteries: in this case, you don't want to share your table of random numbers; to the contrary you…

Below is my contrarian answer to one question recently posted on Quora.

It depends on what you mean by “no experience”. An NASA scientist who has processed petabytes of data and found great insights, for example discovered exoplanets, is de facto a data scientist and may have no interest in having his job title changed.

Then there is a bunch of people who call themselves “data science enthusiasts” and know nothing other than what they learned in a two-hour…

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

Resources

• 29 Statistical Concepts Explained in Simple English - Part 1 
• The Math Behind Machine…

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 question was recently posted on Quora. Below is my answer.

It depends what kind of data scientist you want to become. I think many university curricula include material that is advanced but that you don’t really need. Also, they offer not enough practical, professional coding and big data manipulation that would help you right away when starting a career. It also depends on your background: mine was math, stats, data analysis, and applied computer science, so the…

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.

Announcement…

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

Resources

• How to Choose a Machine Learning Model – Some Guidelines 
• Expert Insight Book on Machine…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting,…

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.

• Verizon—Solutions Engineers (Analysts) - Las Colinas, TX & Cary, NC
  
  
  
  Verizon is looking for Analysts who can use their technical and sales…

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

Resources (Technical)

• Mixing & Matching in R for Data Science 
• Hacking the real estate market, with data…

By Ajit Jaokar and Cheuk Ting Ho.

Exclusively for Data Science Central members, with free access. You can download this book (PDF) here. 

Introduction

Enterprise AI: An applications perspective takes a use case driven approach to understanding the deployment of AI in the Enterprise. Designed for strategists and…

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.

Announcement

• Big data is a big buzzword in the business world. But it’s a serious concept, referring to data sets so large and complex that traditional data-processing applications simply…

Ajit's research is focused on Data Science for IoT. He teaches the same at Oxford University and UPM in Madrid (@forumoxford + @citysciences). Ajit is also launching a course / certification in Data Sciences for Industrial IoT. His personal research interests include Deep Learning algorithms for IoT/future city domains.

The selection below…

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

Technical Resources

• Lots of Free Open Source Datasets to Make Your AI Better 
• Run Deep learning…