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 DSC. Previous editions can be accessed here: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. 29 Statistical Concepts Explained in Simple English… Read More »29 Statistical Concepts Explained in Simple English – Part 12

The ubiquity and sheer volume of data generated today give experts in virtually every domain ample information to track everything from financial trends, disaster evacuation routes, and street traffic, to animal migrations, weather patterns, and disease vectors. But using this data to build visualizations of complex predictive models using machine learning is a challenge to experts who lack the requisite computer science skills. A team at the NYU Tandon School of Engineering's Visualization and Data Analytics (VIDA) lab, led by Claudio Silva, professor in the department of computer science and engineering, developed a framework called VisFlow, by which those who may not be experts in machine learning can create highly flexible data visualizations from almost any data. Furthermore, the team made it easier and more intuitive to edit these models by developing an extension of VisFlow called FlowSense, which allows users to synthesize data exploration pipelines through a natural language interface. The research, "FlowSense: A Natural Language Interface for Visual Data Exploration with a Dataflow System" won the best-paper award at this year's IEEE Conference on Visual Analytics Science and Technology (VAST).

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 DSC. Below is the last article in the series Statistical Concepts Explained in Simple English. The full series is accessible here. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. Previous editions, in alphabetical order, can be accessed here: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. Previous editions can be accessed here: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. Previous editions can be accessed here: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7. Also, check out our upcoming course Learn Machine Learning Coding Basics in a Weekend. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

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 DSC. Previous editions can be accessed here: Part 1 Part 2 Part 3 Part 4 Part 5. Also, if you downloaded our book Applied Stochastic Processes, there is an error page 64, that I fixed. The new version of the book can be found here. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.