Our events are people-focused, bringing brands, influencers, and talent into one space with one goal: to solve all the problems worth solving. We plan conferences that are fun and relaxed on the front end and organized and optimized on the back end.
Here are 3 key traits that differentiate between a data scientist and a great data scientist, starting with – great data scientist is obsessed with solving problems, not new tools.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
While some opponents still hold the misconception that the 'science is not yet in' on the culprit, the scientific community has long reached a consensus to the drivers behind the increase in global temperatures.
We see beginnings of both standardization and specialization, with graduate analytics curriculum that covers math, statistics, CS, IT systems, and communications. We also see specializations in data science and BI, and verticals like marketing and healthcare analytics.
The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.
Learning about what these people do made it clear that when you are deeply involved in A/B testing at scale, there is a tremendous rush from doing so many different things that matter.
During a rally in February, President Trump had these disparaging words about Sweden’s humane immigration policy... but nothing of note actually happened the previous night in Sweden.
We examine the connection between Climate Change Denial and CO2 emissions and find a strong correlation - countries with higher CO2 emissions/capita also have higher percentage of climate skeptics.
Beware of online and market research studies which can lead to false or spurious claims. We examine several notable examples including Google Street View and Argentina inflation.
There is no one profile for the Data Scientist, but I tried to make a few generic job profiles that can somewhat fit job descriptions of different companies. I think there is way too much variety, but I had to narrow down on a set of profiles. Check out the list.
We ask UN Global Pulse Director about the 'Data For Climate Action' Challenge, the best sources of climate data, examples of using data for climate mitigation and climate adaptation, and resources for convincing climate change skeptics.
Are you a data science professional and want to advance your career as Data Science Unicorn? Here we provide important business concepts and guidelines required for a data science techie to become a Unicorn.
Job hunting is challenging and sometimes frustrating task and we all experience it in our career. Here we provide a very specific and practical guide to get your dream job in Data Science world.
If Big Data is to realize its potential, people need to understand what it is capable of, what information is out there and where every piece of data comes from. Without such transparency and understanding, it will be difficult to persuade people to rely on the findings.
Thomas Dinsmore critical examination of Gartner 2017 MQ of Data Science Platforms, including vendors who out, in, have big changes, Hadoop and Spark integration, open source software, and what Data Scientists actually use.
The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.
Our predictions include: 2017 will be the year of Deep Learning (DL) technology, Artificial General Intelligence is still far away, Software and Hardware Progress will accelerate, and AI will have unexpected socio-political implications.
Three years ago, looking beyond Hadoop was insanity, and there was little else that could come close. Recently, adoption of Hadoop has slowed down considerably. We examine why.
Standardization and Specialization in Analytics, Data Science, and BI
6 Business Concepts you need to become a Data Science Unicorn
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