Why is machine learning so hot now if I'm saying machine learning has been around since at least the 1950s? Why now? Why is it hyped now? I think there are a couple main reasons, one being just advances in technology and research. Of course, I mean computers are cheaper and more powerful. It was in 2006 that one of the first most important deep learning papers was published.
There have been fundamental changes in research and technology that have gotten us here. Another thing is now we have the ability to store a lot of data and cheaply, and in general machine learning algorithms do better with a lot of data. That's not always true, but in general that is true.
Another thing that's going on is there's business demand for machine learning, so things like text mining and recommendation systems. There is demand within business to pay for these kinds of activities. These have led to the rise of data science and the data scientist. I consider myself a data scientist.
Data scientists are likely when solving a problem to reach out to a machine learning algorithm as opposed to maybe just old fashion OLS regression or canonical discriminant analysis. What we're looking here at the screen is how the data scientist has long history going back to 1962, and we have some resources about this history. I just think it's important to keep in mind this history. This is something that's been building for a long time. A lot of factors have conspired to make it important and exciting now.
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Data Scientist Persona
All right, so let's focus on this data scientist persona and how that is affecting machine learning in organizations. Well, we definitely believe that machine learning is being taken more seriously as data science and data scientists are taking more seriously. There's a lot to unpack here, but we've seen over the past few years sort of a segmentation of data scientist where there's different types of data scientists, different levels of data scientists.
This is important as you develop your machine learning strategy in your organization to realize that there are some data scientists who truly need to be given a lot of freedom, access to a lot of data, access to a lot of different tools, freedom within the organization, whereas other data scientists, more junior data scientists need more supervision and need more guidance and possibly need tools that can help them out more.
Another thing that we hear about a lot is the unicorn, and I think these unicorns do exist. They're hard to find. They're hard to keep, but if you can get one, good for you. If not, a lot of people are looking to build teams from preexisting resources and talent in their organization. I think that's a great thing to do. We're also looking at a Data Science Venn Diagram, and I think Another thing that we hear about a lot is the unicorn, and I think these unicorns do exist. They're hard to find. They're hard to keep, but if you can get one, good for you. If not, a lot of people are looking to build teams from preexisting resources and talent in their organization. I think that's a great thing to do. We're also looking at a Data Science Venn Diagram, and I think most organizations could dig up some programmers and statisticians and some business people and put them on a team together and that kind of equals a data scientist.
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The term "unicorn data scientist" refers to an exceptionally skilled and versatile individual who possesses a unique combination of expertise in data science, machine learning, statistics, programming, domain knowledge, and business acumen. Like the mythical creature after which it is named, the unicorn data scientist is rare and highly sought after in the field of data analytics and artificial intelligence.
Here are some key characteristics of a unicorn data scientist:
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Strong Technical Skills: A unicorn data scientist is proficient in a wide range of technical areas, including data manipulation, statistical analysis, machine learning algorithms, programming languages (such as Python, R, or SQL), and data visualization tools. They have a deep understanding of advanced mathematical concepts and are adept at applying them to solve complex problems.
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Versatility: Unicorn data scientists are versatile and adaptable, capable of working with diverse datasets and solving a wide range of problems across different industries and domains. They possess a broad knowledge base and can quickly learn and apply new techniques and methodologies as needed.
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Domain Expertise: In addition to their technical skills, unicorn data scientists have a deep understanding of the specific domain or industry in which they work. Whether it's finance, healthcare, e-commerce, or manufacturing, they are familiar with the unique challenges, trends, and opportunities within their field and can leverage this domain knowledge to drive impactful insights and solutions.
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Business Acumen: Unicorn data scientists understand the business context in which their analyses and insights are applied. They can translate technical findings into actionable recommendations that align with business objectives and drive value for the organization. They are effective communicators who can convey complex technical concepts to non-technical stakeholders and influence decision-making at the executive level.
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Problem-solving Skills: Unicorn data scientists excel at problem-solving and critical thinking. They can frame complex problems, formulate hypotheses, design experiments, and iterate on solutions to achieve optimal outcomes. They are creative thinkers who can approach problems from multiple angles and leverage innovative techniques to overcome challenges.
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Collaborative Mindset: Unicorn data scientists work well in interdisciplinary teams and collaborate effectively with colleagues from diverse backgrounds, including data engineers, software developers, business analysts, and domain experts. They recognize the importance of teamwork and communication in driving successful data-driven initiatives and are able to contribute positively to cross-functional projects.
Overall, the unicorn data scientist represents the pinnacle of expertise and proficiency in the field of data science. While they may be rare, organizations that are able to attract and retain unicorn data scientists stand to gain a significant competitive advantage in today's data-driven economy. These individuals play a crucial role in unlocking the value of data, driving innovation, and shaping the future of business and technology.
This idea of not struggling so hard to find the unicorn but looking within to develop data science capabilities from within, and machine learning capabilities from within, we think that's also a very successful strategy. Now, of course, there's a danger in letting people that don't have that much experience with mathematics starting to analyze data, and we would urge you to look for tools that provide bumper rails for those people, to keep them from getting into too much trouble. In general, we feel it's more important to bring people with business knowledge closer to the data.