Author: admin

The Postman API Network is one of the recent movements in the API discovery space I’ve been working to get around to covering. As Postman continues its expansion from being just an API client to a full lifecycle API development solution, they’ve added a network for discovering existing APIs that you can begin using within Postman in a single click. Postman Collections make it ridiculously easy to get up and running with an API. So easy, I’m confounded why ALL APIs aren’t publishing Postman Collections with Run in Postman Buttons published in their API docs.

The Postman API Network provides a catalog of APIs in over ten categories, with links to each API’s documentation. All of the APIs in the network have a Run in Postman button available as part of their documentation, which includes them in the Postman API Network. It is a pretty sensible approach to building a network of valuable APIs, who all have invested in there being a runtime-ready, machine-readable Postman Collection for their APIs. One of the more interesting approaches I’ve seen introduced to help solve the API discovery problem in the eight years I’ve been doing API Evangelist. https://goo.gl/gtKas2 #DataIntegration #ML

This is a new series, featuring great content from our top contributors. Some of these articles are rather technical in nature, but many are business-oriented and written in simple English. The entire series consists of about 120 articles. We intend to publish a new set every two weeks or so. Click here to check out the previous edition. To read more articles from a same author, read one of his/her articles and click on his/her profile picture to access the full list. Some of these articles are curated or posted as guest blogs.

Selected Recent Articles from Top DSC Contributors

* How to Lie with Data – By Karolis Urbonas
* AI vs Deep Learning vs Machine Learning – By Bill Vorhies
* Naive Bayes Classification explained with Python code – By Ahmet Taspinar +
* How To Implement ML Algorithm Performance Metrics From Scratch With Python – By Jason Brownlee
* Building a Deep Learning Model for Process Optimisation – By Sankaran Iyer
* Making data science accessible – Markov Chains – By Kevin Chisholm
* How To Interpret R-squared and Goodness-of-Fit in Regression Analysis – By Jim Frost
* An example machine learning notebook – By Randal S. Olson
* How to create a Twitter Sentiment Analysis using R and Shiny – By Diego Lescano
* From DevOps to DataOps – By Andy Palmer
* 8 Types of Data – By Bob Hayes
* xda: R package for exploratory data analysis (plotting, univariate, bivariate) – By Ujjwal Karn
* More data beats better algorithms – By Tyler Schnoebelen – By Tyler Schnoebelen
* Data Scientist Core Skills – By Mitchell A. Sanders
* Implementing the Gradient Descent Algorithm in R – By S. Richter-Walsh
* Making data science accessible – Logistic Regression – By David Robinson
* 10 Common NLP Terms Explained for the Text Analysis Novice – By Mike Waldron

Source for picture: article flagged with a +

DSC Resources

* Subscribe to our Newsletter
* Comprehensive Repository of Data Science and ML Resources
* Advanced Machine Learning with Basic Excel
* Difference between ML, Data Science, AI, Deep Learning, and Statistics
* Selected Business Analytics, Data Science and ML articles
* Hire a Data Scientist | Search DSC | Classifieds | Find a Job
* Post a Blog | Forum Questions https://goo.gl/mgA9YB #DataScience #Cloud