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The Quant Crunch: The demand for data science skills
This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.
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A Data Analyst guide to A/B testing
A/B testing is key to improving results in any marketing campaign. We examine the issues involved in its 3 main components: message variants, user group selection, and choosing the winning version.
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Data Science & Machine Learning Platforms for the Enterprise
A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.
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42 Essential Quotes by Data Science Thought Leaders
42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.
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Did you know cavemen were already dealing with “Big Data” issues?
We know Big Data & Analytics are new & cutting edge technologies; but actually, human started using data & analytics techniques 5000 years ago. Let’s take a look.
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 Deep Learning – Past, Present, and Future
By Prasad Pore Editor, KDnuggets on May 2, 2017 in Andrew Ng, Big Data, Deep Learning, Geoff Hinton, Google, GPU, History, Neural Networks, NVIDIAThere is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
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How Not To Program the TensorFlow Graph
Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.
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 Keep it simple! How to understand Gradient Descent algorithm
In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.
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The Analytics of Emotion and Depression
Analytics can be used to provide a boost to the cure of depression. How analytics is being adopted by companies like Microsoft, Facebook to handle and detect vulnerable targets of depression.
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The Data Science of Steel, or Data Factory to Help Steel Factory
Applying Machine Learning to steel production is really hard! Here are some lessons from Yandex researchers on how to balance the need for findings to be accurate, useful, and understandable at the same time.
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Deep Learning – Past, Present, and Future
Keep it simple! How to understand Gradient Descent algorithm