Due to the accelerated pace of technology, young people today have to start preparing their studies for the future with professions that do not yet exist or are beginning to exist due to technological advances.
Studies have already shown that two out of every three young people belonging to the ‘millennial’ generation are convinced that they will devote themselves in the future to professions that do not yet exist due to technological advances.
Professions previously in-demand are no longer necessary and new ones are born each day. To get on the wave successfully, it’s essential to train and do it consistently.
Data scientist
Big data is here to stay. Data science takes advantage of the advances of connectivity and Internet penetration to generate, record, and model vast volumes of information following the scientific method. Its objective is to identify, process, and convert large amounts of data into valuable information for decision-making in any field.
What skills do you need to master to be a data scientist?
- Mathematical and statistical skills.
- Big data architecture through the use of software such as Hadoop, relational and non-relational databases, and using programs and languages such as Cassandra, MongoDB, MySQL or PostgreSQL.
- Programming languages ​​such as R, Python, S, C, SAS.
- Management of databases such as SQL and programming in HIVE.
- Data visualization programs with software such as Kibana, Tableau, Clip View, or even Excel.
- Being curious to look for relationships between data points that do not necessarily seem related.
A fundamental ability to be a data scientist that is considered a “soft skill” is to be curious to look for relationships between data that are not connected or logical to each other — an intuitive, exploratory mind is key.
Expert in artificial intelligence
It is not a secret that, in the technological sector, AI experts receive astronomical salaries due to the high demand of this profile and the shortage of specialists.
Artificial intelligence creates systems capable of learning and prediction from reading data — either from other systems or directly from the environment. This information is processed and stored in the form of “knowledge” that is then used to issue recommendations and actions.
As with the introduction of office computing, artificial intelligence will not replace workers as much as it will force them to acquire skills to complement it. As technology changes the skills needed for each profession, workers will have to adjust. That’s why it’s essential to learn about artificial intelligence now, while it’s still in its relative infancy.
What requirements do you need to become a sought-after AI expert?
- Know the basics of data processing.
- Master the development of applications or software with programming languages like ​​R, Python, C #, and C++, among others. Unlike traditional software, whose objective is limited and focused on a series of specific tasks, the one used in AI is focused on constant learning.
- Mastery of big data architecture.
- Extensive knowledge of machine learning and machine learning software.
The possibilities of developing AI can be grouped into:
- Specific– focused on reading information of a single type and provides solutions based on a specific purpose.
- General– seeks to copy the multiple ways of thinking and acting, emulating a human being. The AI then decides on their learning patterns and decisions — although this is still not fully developed because it is a vast and complex problem and requires more robust technological solutions.
Society is changing, and that’s why we have to be prepared for the future before it happens. There are new developments in biotechnology, genetic engineering or robotics; these also begin to provide new forms of employment that will be decisive for innovation in the societies of the future.
For entering the world of AI, it is advisable to have studied some software engineering and have a high command of mathematics, statistics, and programming. With the mastery of these skills, you can create systems that use information to generate knowledge and make decisions in the mode of patterns and probabilities. These talents will serve well in the AI-driven economy of the future.
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