The author disagrees with a previous KDnuggets post on “Why Hadoop is Failing” and argues that the Darwinian Open Source Ecosystem ensures Hadoop is a robust and mature technology platform .
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.
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.
When something goes wrong, as it inevitably does, it can be a daunting task discovering the behavior that caused an event that is locked away inside a black box where discoverability is virtually impossible.
Data not Constantly Maintained ->Data Becomes Irrelevant -> People Lose Trust -> Use Data Less. We examine 4 reasons for such wheel of death, and what can you do about it.
We examine “citizen” data scientists and debate between Jeffersonians, who seek to empower everyday worker with data science tools, and Platonists who argue that democratizing data science leads to anarchy and overfitting.
We expect data scientists to be objective, but intentionally or not, they can produce results that mislead. We examine three common types of “lies” that Data Scientists should be aware of.
The rise of conversational UI signals exciting progress for the BI world but there are pitfalls to be avoided. This blog presents 3 considerations for guiding your conversational UI implementation to ensure success and maximize the value of your data analytics.
We see the need for a new type of Engineer who will combine knowledge from Electronics & IoT with Machine learning, AI, Robotics, Cloud and Data management (devops).
Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?
Who leads in Data Science, Machine Learning, and Predictive Analytics? We compare the latest Forrester and Gartner reports for this industry for 2017 Q1, identify gainers and losers, and strong leaders vs contenders.
There are no cover articles praising the fails of the many data scientists that don’t live up to the hype. Here we examine 3 typical mistakes and how to avoid them.
Successful data teams at companies of any size are able to produce results because they develop gradually through a series of stages and acquire skills along the way that help them stay efficient and effective.
We know various job profiles in data science – data engineer, data scientist, data analyst etc. Here we explain how these roles fits in a real world data science team and what they do.
Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.
Data scientists tend to think that their main job is to answer complex questions and gain in-depth insights, bu in reality it is all about solving problems – and the only way to solve a problem is to act on it.
Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.
An obvious metric we can look at for how much harm terrorists from the banned countries do to America is looking at the number of people killed on American soil by terrorists from these countries.
We examine 2 common tactics by data "skeptics": demanding more precision and demanding unanimity. These techniques are especially effective against data scientists, who should be aware of them, and able to counteract them.
Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions
Top mistakes data scientists make when dealing with business people
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