Demand for data scientists is always high. In fact, a a recent study by LinkedIn found that US companies need more than 150,000 jobs with scientists. And now they need them.
The reason for this requirement lies in the enormous amount of data that companies in each industry can now gather through digitization and technology. New data is available all the time, amounts are increasing and companies need to use this data to optimize their functions and, frankly, they are even started. Proper use of these data can mean the difference between business success and failure, and the data scientist is the key to unlocking the story behind the data.
This request fueled the saturation of programs that quickly enriched people to start working on the field, but simply reading a book or taking $ 40 on the web does not make the data available to scientists. People in that role need years of training and experience to make the job successful and effective.
In fact, a career in data science should be compared to any professional background that requires advanced training, education and experience – such as a doctor, attorney or architect. Each of them requires not only a basic job ability, but also a large amount of training, education and knowledge gained through field experience.
Data science needs to be accessed with the same amount of rigidity as this role can be responsible for providing a database based on very real, very expensive business decisions.
Think of the emergence of this kind of business, focusing on early scientists. A good example for this was Tycho Brahe, who carefully described the astronomical diaries in detail, observing the planet's locations from night to night.
These detailed data, collected over many years, allowed Johannes Kepler to disclose his three laws of planetary motion, which include the now well-known fact that Earth is circling around the Sun's ellipse. He gagged a bunch of data, tested the hypotheses, and came up with revolutionary evidence (the intention of dossiers) that Copernicus and Galileo were right.
Today's scientists are doing much of the same work processes today, from problems ranging from understanding consumer behavior to predicting disease progression, but with the benefit of significant computer resources. Large amounts of data come from digital sources, and ambitious analyzes can be carried out with the clever use of maths, statistics, software engineering and technologies such as automation, machine learning and artificial intelligence.
Marketing and product development can show a more modern example of data science.
Imagine the brand wants to see how good the product is doing. The company must understand who is buying it, when, where, why and how often. All information is stored in the data.
Data scientist applies computer and statistical techniques to all current customer information to find patterns and groups within these data. This analysis can advise the brand of who you should be marketing or even if you need to change your product offer.
The data scientist finds the stories that the data say and separates the right patterns and trends of coincidence, just as Kepler did when he was studying the planet's motion. The job basically lies at the intersection of probability / statistics and software engineering.
Since data points often come from more fragmented and noisy sources, the data scientist must understand the context of data, set up integration and data pipelines, and apply rigorous statistical methods to extract what the data is trying to say.
It is of crucial importance to do their job properly and accurately because, as mentioned above, the stories that data scientists make of data directly invest in business decisions related to real money and risk. Correctly, data science can be a transformative role within any organization by transforming the database data summary into real-world insight and effective action.
When viewed in the light of the basic supply and demand, it is easy to see that there is a gap in talent that does not show any signs of reduction. Those entering the labor market to fill the void are inexperienced workers who ultimately can cause short-term pain. In the long run, specialists will emerge and recruiting managers will become more wondering what skills to look for.
In the meantime, an advisory model that is interested in statisticians / mathematicians, software engineers, and project management under one roof can be the solution. By engaging a team of qualified professionals to improve their own data science program, the company achieves lower project costs and execution risk, efficiently exploiting the latest achievements in that field, and quickly coming to the heart of what their data is trying to tell them.
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Posted April 7, 2019 – 7:30 AM UTC