This is the world's sexiest job! – Data ScientistPublished 10 Mar 2017
Just like a craftsman, a data scientist is not much without his/her tools. And good craftsmen take the time to look after and develop their tools. The list of programs and methods can be long, but the important thing about a tool is what function it has and what utility it provides.
Forbes writes that data scientist was the 8th best paid job in 2015. The profession has gained a considerable boost in recent years and has been named in the hype with Big Data as the century's sexiest profession. Some are searching feverishly for a true data scientist who can speed up advanced analysis but they seem to be as rare as unicorns. For those who dive deeper into the term, it becomes clear that the definitions diverge and much of it is just talk. But there is an important core in the term that we want to capture and convey. Data scientists have an important role to play and are here to stay. Let's look closely at what distinguishes a data scientist from a business analyst, what background and character a data scientist has, and how they drive development.
Data Scientist or Business Analyst?
In most companies today, business analysts investigate analysis questions, compile data, interpret and present reports and interactive interfaces in order to analyze business operations. It is essential to make crucial decisions based on facts to develop and improve the business. This has been the job assignments of a business analyst for some time. What is the purpose of the work that a data scientist does then? Exactly the same thing! Turn the question around: what else would the purpose be, if not to develop and improve the business? The big difference lies in that a data scientist thinks more broadly, detects and tackles larger and more complex data sources with new methods, ways of thinking, and tools. A more detailed description of the difference between these two disciplines can be found here. If we ask Data Science Central (with a clear opinion on the matter), they describe data scientists as "those who deal with Big Data".
Data Scientist = data + scientist
If we look at the etymology of the term, data scientist, it is simply data and scientist. Data can be anything but, by interpreting it, information and insight can hopefully be obtained. A scientist, in turn, is a person "involved in a systematic activity to gain knowledge describing the natural world." The term "data scientist" in Swedish becomes very broad, but it also has the strength of a data scientist: the breadth of addressing completely different challenges in which new data is interpreted and transformed into business advantage.
A Universal Genius?
In the picture below we list some of the skills that are vital to a successful data scientist. It has been said that: "Genius generally has no particular direction: it encompasses all or most objects of knowing, called the Universal Genius." During the Renaissance, universal genius was ideal and universities were aimed at developing students' intellectual, artistic, social, and physical sides [lat. universitas: the whole]. In today's society, specialization is the key and a university education in 'general knowledge' is difficult to find. At the same time, it is just this that we require of a data scientist: to handle large amounts of complex data, focus on strategic business goals, evaluate statistical tests, network with everyone from decision makers to IT experts, create cool visualizations, build interactive dashboards, build effective machine learning algorithms, develop new hypotheses, ensure that analyzes are scientific, understand different business activities, and to communicate and convince, in particular, what has been achieved. How do you manage to do this? The secret is that the beginner learns quickly and does not need to be a specialist in all the areas.
Where do data scientists come from?
The main basic foundation for data scientists is undoubtedly curiosity and inspiration combined with intelligence and motivation. A broad technical education with a focus in research is an advantage and it is no coincidence that technical physicists, for example, become successful data scientists. Some have taken a step further and earned a PhD. For a number of specialist areas, a doctorate is a prerequisite, but it must be weighed against the equivalent that can be provided by practical work experience in the business world. Regardless of education and background: everyone can develop into better data scientists! At Knowit, we offer focused courses that raise the data science level of the entire company. For specific topics and techniques, there are also open services such as Coursera, European Data Science Academy, and Data Science Masters. It is at least equally important to explore GitHub, StackOverflow, StackExchange, and to try out the competitions on Kaggle.
The key to growing is to constantly learn new things and try new techniques and theories to broaden your knowledge. A data scientist prefers an open climate with open source codes and is happy to test new solutions. They love teamwork in order to share lessons and experiences and to be inspired to make new discoveries. In an organization, they see themselves as explorers and are driven by the big white areas on the map of exciting data. Now, you may be thinking: "Aha, is it those people who constantly have to google in the middle of dinner to find out everything!?" That may be so in some cases, but social skills are equally as important!
Good craftsmen do not complain about their tools
Just like a craftsman, a data scientist is not much without his/her tools. And good craftsmen take the time to look after and develop their tools. It is necessary to have a toolkit with great diversity for different issues: scripts for scientific computing, library for machine learning & data mining, databases for structured and unstructured data, GIS tools, Java-based visualization tools, cloud services for large-scale analysis, web development interface, Linux functions for data wrangling, various Business Intelligence programs, and not least, methods and processes for project management, organization, business integrity, and strategic work. The list of programs and methods can be long, but the important thing about a tool is what function it has and what utility it provides.
Here to stay
It is obvious that data scientists are here to stay. Their breadth and analytical approach are undoubtedly a big asset in today's world when data is an increasingly important resource. But they need to have a good environment for their work and not get caught up in data cleaning jobs. In the right place, they complement business analysts with new tools and skills, are motivated to develop themselves and business operations, and sow seeds for innovation along the way. However, how data scientists are able to achieve a perfect balance between work and leisure remains a mystery. Perhaps it has something to do with versatility, openness, and curiosity?
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