Ten years ago Harvard Business Review named data scientists as having the "sexiest job of the 21st century." This month they said it's "still the sexiest job" of this century. I guess that depends on one's view of sexy. Sure, it's difficult to source data science talent, given a scarcity of supply. There are strategies for improving this, like looking to adjacent job functions to upskill them.

Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to experience more growth than almost any other by 2029. But the job has changed, in both large and small ways. It’s become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown. How it operates in companies — and how executives need to think about managing data science efforts — has changed, too, as businesses now need to create and oversee diverse data science teams rather than searching for data scientist unicorns. Finally, companies need to think about what comes next, and how they can begin to think about democratizing data science.

Data science has been called "the sexiest job of the 21st century" -- a sentiment I'd believe if I saw more business leaders hiring data scientists into environments where we can be effective. Instead, many of us feel misunderstood and invisible. We are the people who help inspire new directions for your business, reduce your risk of setting important decisions on fire, and automate the ineffable through machine learning and AI. We make your data useful, yet you make us live in resource squalor. Unskilled leadership -- If you don't have personnel skilled at leading and managing the data science function, we'll have a miserable time.

Thomson Reuters has a series, AI experts, where they interview thought leaders from different areas - including technology executives, researchers, robotics experts and policymakers - on what we might expect as we move towards AI. As part of that series I recently spoke to Paul Thies of Thomson Reuters, and here are the excerpts from the interview: Anticipating the next move in data science Thomson Reuters: For timely information concerning developments in data science, data mining and business analytics, KDnuggets is widely regarded as a leading outlet in the field. Created in 1993 by founder, editor and president Gregory Piatetsky-Shapiro, it is frequently cited as one of the top sources of data science news and influence by various industry watchers. Thomson Reuters: What are some use cases of data science that you find to be particularly valuable to organizations in this age of Big Data? GREGORY: Where people typically apply data science, probably not surprisingly, are in the areas of customer relationship management (CRM) and consumer analytics. Data science allows you to predict consumer behavior better and usually make incremental improvements and predictions, but those incremental improvements could translate to significant revenue.

In 2012, data science was described as the'Sexiest job of the 21st century' by Harvard Business Review. Six years later, is it still true? With explosive growth in the volume of data in recent years and the resulting disruption in business, it's no surprise that the ability to capture, analyse and use data -- particularly to power artificial intelligence -- continues to be highly sought after. The data scientist role has become an increasingly critical one, uncovering patterns and insights that help businesses stay competitive by allowing them to quickly respond to trends and customer needs. While still sexy, the role has most certainly evolved.

Microsoft Research has a recent paper (Machine Teaching: A New Paradigm for Building Machine Learning Systems) that explores the eventual evolution of Machine Learning. The paper makes a clear distinction between Machine Learning and Machine Teaching. The authors explain that Machine Learning is what is practiced in research organizations and Machine Teaching is what will eventually practiced by engineering organizations. If you have been actively following this blog, it should be apparent by now that it has a distinctly software engineering spin towards the application of Deep Learning technology. We are inundated on a daily basis with plenty of astonishing discoveries in Deep Learning.

Machine Learning is hottest subject of today's time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. Machine Learning is the hottest subject of today's time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. The real need for today's time and business is to clarify, demonstrate and extract real values to benefit every one from this golden key word "Machine Learning". As on date sadly most of the machine learning methods are based on supervised learning. Which means we still have long long way to go.

Data scientists are a happy bunch overall, but they do worry about ethical issues such as human bias and prejudice being programmed into machine learning (ML) and the use of artificial intelligence (AI) and automation in warfare and intelligence gathering. That's a finding in the new "2017 Data Scientist Report" just published by AI specialist CrowdFlower Inc. "Read any article on AI (and there is no shortage) and shortly behind, you'll likely find mention of ethical issues," the report said. "From the White House to the Wall Street Journal to the World Economic Forum, the question of how we program the future is one of the most critical issues facing not just data scientists but society as a whole. In perhaps the most important question in this year's survey, we asked, 'Which of the following do you personally think might be issues regarding ethics and AI?' " The top concern raised in answering that question was "human bias/prejudice programmed into machine learning" (listed by 63 percent of respondents), followed by "use of AI and automation in warfare/intelligence" (49 percent). "Unease on the displacement of human workforces and the impossibility of programming a commonly agreed upon moral code also ranked high on the radar of ethical issues for data scientists tallying in at 41 percent and 42 percent respectively," CrowdFlower said.

The rise of Artificial Intelligence (AI) and automation is no longer seen as a threat to just menial, repetitive jobs. Already systems are being developed and deployed which have the potential to carry out work traditionally left to highly educated and skilled humans, such as doctors, lawyers and architects. As computers become faster and analytics more sophisticated, due to advances such as machine learning and neural networks, they come closer and closer to emulating the processes of the human brain. This is by design – machine learning was conceived around the principle of teaching machines to ingest and classify data in the same way we do. Now, it's becoming increasingly apparent that one of the professional, white collar jobs under threat is the one which made all of this possible in the first place – that of data scientist.

The rise of Artificial Intelligence (AI) and automation is no longer seen as a threat to just menial, repetitive jobs. Already systems are being developed and deployed which have the potential to carry out work traditionally left to highly educated and skilled humans, such as doctors, lawyers and architects. As computers become faster and analytics more sophisticated, due to advances such as machine learning and neural networks, they come closer and closer to emulating the processes of the human brain. This is by design – machine learning was conceived around the principle of teaching machines to ingest and classify data in the same way we do. Now, it's becoming increasingly apparent that one of the professional, white collar jobs under threat is the one which made all of this possible in the first place – that of data scientist.