The demand for data scientists is at an all time high. In fact, a recent LinkedIn study found that companies based in the US UU they need more than 150,000 jobs from data scientists. And they need them right now.
The reason for this demand lies in the enormous amounts of data that companies in all industries can now collect through digitization and technology. New data is available all the time, volumes are increasing and companies need to use that data to optimize functions and, frankly, to function. Proper use of this data can mean the difference between success and failure of the business, and a data scientist is the key to unlocking the story behind the data.
This demand has stimulated a lot of "get rich quick" schemes for people to start in the field, but simply reading a book or taking a $ 40 online is not a data scientist. The people in this role need years of training and experience to do the job effectively and efficiently.
In fact, a career in data science should be compared to any qualified profession that requires advanced training, education and experience, such as a doctor, lawyer or architect. Each of these requires not only a basic aptitude for work, but also a large amount of training, education and knowledge acquired through experience in the field.
The science of data should be approached with the same amount of rigor, since this role may be responsible for providing the data-based basis for very real and very expensive business decisions.
Think of the genesis of this type of work, remembering the first scientists. A good example of this was Tycho Brahe, who kept meticulously detailed astronomical journals, observing the location of the planets night after night.
These detailed data, collected over many years, are what allowed Johannes Kepler to discover his three laws of planetary motion, which include the now well-known fact that the Earth orbits in an ellipse around the Sun. It collected piles of data, tested hypotheses and found the revolutionary evidence (word attempt) that Copernicus and Galileo were right.
Data scientists today are doing many of the same workflows, in problems ranging from understanding consumer behavior to predicting the progression of the disease, but with the benefit of significant computing resources. Large amounts of data come from digital sources, and ambitious badyzes can be made through the intelligent use of mathematics, 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 that a brand wants to see how well a product is doing. The company needs to understand who is buying it, when, where, why and how often. This information is stored in the data.
A data scientist applies computational techniques and statistics to all client data that flows to find patterns and groups within that data. This badysis can advise the brand on who they should market or even if they should change their product offer.
A data scientist finds the stories that the data tell, and separates the real patterns and trends from randomness, just as Kepler did when he studied planetary motion. The work is essentially at the intersection of probability / statistics and software engineering.
Because data points are often derived from multiple fragmented and noisy sources, the data scientist must understand the context of the data, establish conduits to integrate and clean up the data, and apply rigorous statistical methods to convince the data they are attempting. tell us
It is vital that you do your work correctly and accurately because, as mentioned above, the stories that data scientists weave from the data source directly to business decisions related to real money and risk. Done correctly, data science can be a transformative role within any organization, converting abstract data found in databases into real-world perspectives and effective actions.
When viewed in light of basic supply and demand, it is easy to see that there is a talent gap that shows no signs of decline. Those who are entering the labor market to fill the gap are inexperienced workers that can ultimately cause a lot of pain to companies in the short term. In the long term, experts will emerge and hiring managers will be smarter as to what skills to look for.
Meanwhile, the solution can be a consulting model that combines statistics / mathematics, software engineers and project management under one roof. By involving a team of qualified experts to advance an internal information science program, companies incur lower project costs and execution risk, take advantage of the latest advances in the field efficiently and reach the heart of the business more quickly. what your data is trying to tell they.
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Published on April 7, 2019 – 07:30 UTC.