In a modern environment that is increasingly driven by information, data science has become a strategic discipline for decision-making. However, behind every predictive model, graph or dashboard, there is a set of fundamental competencies: STEM skills.
These skills—related to science, technology, engineering, and mathematics—not only make it possible to analyze data, but also to turn it into actionable knowledge. In this article, we explore what the key STEM skills are for working in data science and why they are essential in today’s work environment.
Science – Critical Thinking
Beyond any technical tool, there is a basic skill that is critical or analytical thinking. This approach seeks to generate conclusions from objective information, formulate questions and compare data using clear and formulated criteria.
Science – Scientific Method
In the search to generate solutions and discover new information, the ability to generate scientific experiments is vital, with control points and variations so that by contrasting results a conclusion based on data can be obtained. Data science is used in various fields of science such as biology, medicine, climate, geology, and industrial problems. The scientific method provides a common basis to apply to these interdisciplinary experiments.
Technology – Programming
The programming skill is one of the most demanded in data science, it is the engine that allows you to connect data with formulas, probabilities and advanced techniques. Among the most widely used programming languages are Python, R, Scala, and SQL.
Technology – Databases
The processing of large amounts of information is done with database techniques, this technology provides integrity, security, backup, search engines and rules engines. Complex data in shapes, sizes, and qualities become manageable with this technology.
Engineering – Software Development
The use of engineering project management techniques in software construction aims to increase the quality of the results. By dividing projects into phases such as analysis, development, testing, and production, the tasks and resources to be used become easier to manage when working in teams of various professionals.
Engineering – Electronics
A complementary skill to data science is electronics. Basic data science projects are based on information already collected and structured, they do not require new data. But sometimes the information does not exist, it is necessary to capture data by some technical means. Sensor electronics and the Internet of Things bring new capabilities to capture information, such as physical data, temperature, humidity, distance, electrical properties, magnetic properties, and more. There are properties that are not visible to the human senses, but are visible to sensors. Electronics provide the means to augment visible reality to data science.
Mathematics – Statistics and probabilities
Statistics help describe data to understand its distribution by viewing grouped data. It is common to analyze data using averages, minimums, maximums, variance, and standard deviation. More complex equations are built on this.
This is why artificial intelligence, predictive and classificatory models have a strong theoretical basis in statistics and probabilistic regressions.
Providing an additional approach, statistical inference techniques manage to understand how representative of reality the data used to create artificial intelligence are, without these bases, intelligence could learn inaccurate ideas. This indicates that AI solutions might require more representative data and not necessarily new calculations or models.
Mathematics – Calculus and Algebra
Within AI models, the representation of information is carried out in mathematical matrices, which in themselves have the properties of linear algebra that allow calculations such as addition, subtraction, multiplication and division matrices, all advanced mathematics is built on these pillars. In addition, a cornerstone of machine learning is to decrease the error between results when learning data, this is done with derivative formulas in the algorithm known as gradient descent.
Conclusion
STEM skills aren’t just a technical requirement for working in data science; they are the foundation that allows information to be transformed into intelligent decisions. From mathematics and programming to administrative skills, these competencies make up the profile of the professionals who today drive technological innovation.
In an increasingly data-driven world, developing and strengthening these skills is not an option, but a strategic necessity to compete and grow.