Many organizations approach data science as though it was a marketing tool — relabeling things that they already do as ‘data science’ as it involves the use of data. That is not real data science, and it completely misses the point of engaging in data science.
Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.
The possibilities for humanity's future very likely includes at least one in which computers will exceed human abilities. Artificial General Intelligence (AGI) does not necessarily have to be all doom and gloom. However, we must begin now to understand how this technical evolution might progress and consider what actions to take now to prepare.
About 70% of KDnuggets readers think that the demand for Data Scientists will increase, and 50% think it will increase significantly. At the same time, over 90% think the role of Data Scientist will change. What will the Data Scientist role be in 10 years?
Data freelancers trade hours for dollars while data entrepreneurs have found a way to make money while they sleep. Ready to make the transition? Keep reading to learn how to do it as SEAMLESSLY and PROFITABLY as possible.
Maybe it seems that everyone wants to become a data scientist and every organization wants to hire one as quickly as possible. However, a mismatch often exists between what companies tend to need and what ML practitioners want to do. So, it's time for the field to take another step toward maturity through an enhanced appreciation of the broad range of technical foundations for an organization to become data-driven.