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 …

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…

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…

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

New Resources

• An Application of Data Science and Mathematics in Finance 
• AI (Deep Learning) explained…

Steve has 40 years experience in consulting services surrounding BI, statistics, analytics, and data science. His most recent position was President of Inquidia Consulting and EVP of BI/Analytics at Braun Consulting. He has been a writer for Information Management, Dataversity, and BeyeNetwork for 12 years.

Below…

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 link.  

Announcement…

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. In the upcoming months, the following will be added:

• The Machine Learning Coding Book
• Original Math, Stats and Probability Problems - with Solutions
• Experimental Math for Data Scientists
• Detecting and Leveraging Patterns in Data
• Career…

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

Resources

• AI and Deep Learning Books - October 2018 
• Machine Learning with C++ -…

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 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, dataviz, and many more. To keep receiving these articles, …

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.

Announcements

• Jump-start your career as a data scientist, data engineer, or analytics manager in…

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

Resources

• Is It Too Late For Me To Learn Coding? 
• San Diego Water Pollution Map by Stations …

The answer to this question is not black and white, and also depends on where you live, what you did during your PhD program, how much time and money you spent on it, what kind of jobs you are interested in, and what other experience you have. You could say the same thing about earning an MBA instead.

If your PhD took place abroad as in my case, you may have not spent much money to earn it (you might even have been well paid.) Depending on your school, it might have been…

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.

Announcements

• November 14^th, 10:00am CST: Exclusive Insurance Nexus webinar: “…

Another question recently posted on social networks. Below is my answer, with link to the original post. 

The main reason is the exponential growth of data science candidates, while the growth in job openings, even though exponential as well, is increasing at a lower pace than the number of applicants. In some ways, this is similar to the explosion of PhD people, while the number of jobs for these people is shrinking. One would wonder: why so many people want to get a PhD…

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

Resources

• New Book: Enterprise AI - An Applications Perspective 
• 25 Statistical…