You could be forgiven for thinking that magicians are tinkering away behind the scenes of these wondrous services, but the real magic makers are data scientists, and these guys are very difficult to find across the region.
Niche Skills in Short Supply
Silicon Valley is practically on its knees begging for a new wave of data scientists, so millennials and young people in Latin America have a great opportunity to join some really exciting companies if they get their act together.
So, what are the core aspects of a good data scientist? To understand this, we need to look at data science itself.
On the outside, data science sounds pretty complex, but it’s actually very straightforward. Essentially, data science is the analysis of data that can lead to new insights and discoveries, helping people and companies to make better decisions based on that information.
As simple as that sounds, these skills are in very high demand and still considered niche, despite that fact that prestigious universities like Harvard, Stanford, and MIT, and massive tech companies like Apple, Google, and Facebook are embracing the fact that data scientists are essential to the future of technology.
Becoming a Great Data Science Pro
A good data scientist is able to take the unstructured data and finds order, meaning, and value in it. The science aspect is the ability to provide insights into people’s mind and behavior, giving businesses a distinct competitive advantage.
Data scientists require four core skills: hacking and programming ability, creativity, mathematical knowledge, and a familiarity with statistics.
The hacking and programming side is required to gather and prepare data in mixed and unusual formats, due primarily to the masses of unstructured and wild data out there in the digital space.
Creativity, knowledge of mathematics, and statistical familiarity give data scientists the power to diagnose issues, improve methods of data collection, and develop new procedures for analyzing the end results.
Taking Those Skills to the Next Level
When engaging in and conducting a data science project, data scientists generally have four basic tasks to perform: planning, data preparation, modeling, and follow-up.
- Planning requires data scientists to set goals, organize resources, coordinate people, and create a project schedule.
- Data preparation involves the acquisition of data from various sources, followed by the cleaning up and refining of that data.
- At the modeling stage, data scientists must create a data model, validate the data, and evaluate it in order to further refine the original model.
- Finally, the follow-up stage sees that model being deployed and revisited, before archiving the assets for future use.
Not Just Data Scientists…
Now that you had a basic idea of the data science process, we can look at the other potential roles for people in this arena, and that value that each of them bring to the table.
Firstly, you have developers who focus on the hardware and software to support the data scientists from the backend. Then there are data specialists who have computer science and mathematics backgrounds that lend themselves well to the use of machine learning. Data Researchers place a focus on domain-specific research and have expertise in the statistical aspects of data science. Then you have data analysts, who focus on day-to-day analysis, which can include looking at data from just about anywhere.
Despite all those technical roles, just as important are the business people and entrepreneurs who manage the projects and have ambitions to create successful startups in the data science domain.
The LatAm Opportunity
Young people in Latin America could be seizing the opportunity to become part of this niche field, either starting their own businesses to provide data science services, or studying hard to become the next great data artists.
It’s also vital that universities in Latin America see the opportunity that data science can bring young people. They need to start offering more courses that can develop the skills of the future, so that the fast-growing companies of the world can have more access to these niche skills.
After all, without great data scientists leading us into a more exciting future, how will any of us decide what to watch next?