Mar
2017 Apr Opinions, Interviews
http likes 146All (110) | Courses, Education (8) | Meetings (19) | News, Features (23) | Opinions, Interviews (21) | Software (3) | Tutorials, Overviews (28) | Webcasts & Webinars (8)
- Hadoop is Not Failing, it is the Future of Data
- Apr 27, 2017.
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 .
- The Analytics of Emotion and Depression
- Apr 26, 2017.
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.
- The Data Science of Steel, or Data Factory to Help Steel Factory
- Apr 25, 2017.
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.
- AI & Machine Learning Black Boxes: The Need for Transparency and Accountability
- Apr 25, 2017.
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.
- Industrial Asset Management – Slaying hurdles to get the most from your assets
- Apr 24, 2017.
A well-structured asset performance management (APM) plan can give real-time visibility of equipment reliability while predicting possible failures.
- How to Battle the Data Wheel of Death
- Apr 24, 2017.
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.
- They Want to Get Rid of Me! (Data Scientist Lament)
- Apr 21, 2017.
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.
- How to Lie with Data
- Apr 20, 2017.
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.
- Putting Alexa to Work: Moving Conversational UI from Hype to Reality
- Apr 19, 2017.
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.
- How Big Data Helps Today’s Airlines Operate
- Apr 19, 2017.
Companies all over the world have placed a lot of value on getting more insights from big data analytics. That’s not without good reason.
- The dynamics between AI and IoT
- Apr 18, 2017.
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).
- What Makes a Good Analyst?
- Apr 14, 2017.
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?
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Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions
- Apr 14, 2017.
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. -
Top mistakes data scientists make when dealing with business people
- Apr 13, 2017.
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. - The Evolution of a Productive Data Team
- Apr 11, 2017.
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.
- The Librarian, the Scientist, the Alchemist and the Engineer: Anatomy of a DataOps Expert
- Apr 10, 2017.
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.
- Stuff Happens: A Statistical Guide to the “Impossible”
- Apr 6, 2017.
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.
- How to stay out of analytic rabbit holes: avoiding investigation loops and their traps
- Apr 6, 2017.
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.
- Putting Together A Full-Blooded AI Maturity Model
- Apr 5, 2017.
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.
- Does the Muslim Ban Make Us Safer?: Data Science vs Fake News
- Apr 5, 2017.
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.
- Beware of Two Data Obfuscation Tactics
- Apr 3, 2017.
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.
Automating Every Aspect of Your Python Project
Autograd: The Best Machine Learning Library You’re Not Using?