Today's world relies, for almost everything, on data. Every time we can collect, store and analyze more amount of data, from multiple sources. Thanks to what we can extract from the data, we can develop very advanced technologies, from artificial intelligence to the autonomous car.
In a scenario in which the mastery of data is key to differentiate yourself from the competition, it is necessary to have tools, methodologies, and, in short, a science that dictates how the data should be treated to obtain the maximum amount of information useful of them.
Until approximately 2010, the main concern and the great challenge for companies was to be able to store ever-larger amounts of data; Once this stumbling block was overcome, having already a framework and solutions to store the data, the focus has shifted to the processing of this data. And this is where Data Science comes in.
Data Science, or data science, is the future of artificial intelligence. Data Science is a scientific discipline that specializes in the analysis of large data sources, theoretically very different, to extract information from them. Thus, it seeks to better understand reality and discover patterns among these unconnected data with which decisions can be made.
Data Science is a mix of various machine learning tools, algorithms, and principles to discover hidden patterns from raw data. We could say that it is something like what is done in statistics, but with new tools, among which artificial intelligence stands out.
What does a Data Scientist do?
The Data Scientist, or data scientist, is the professional who is in charge of deciphering problems, finding patterns, and, in short, extracting useful information from the apparent chaos that is the data collected and stored.
Their work is based on using tools and elements related to mathematics, statistics, and computing, mainly, and especially using machine learning tools (machine learning, a subset of artificial intelligence).
They do not have to be experts in all these fields, far from it, but they do have to have enough knowledge and skills to be able to take advantage of these tools, so, so to speak, they have good training in those disciplines.
They use cutting-edge technology to find solutions and reach conclusions that are crucial for the growth and development of an organization or a company. The scientific data are also responsible for presenting data in a more useful form, once processed, as opposed to raw data which have shaped both structured and unstructured.
How to study Data Science?
As you can imagine, the knowledge that a data scientist must handle is vast and complex. These include knowing how to handle a large number of big data platforms and tools of all kinds, such as Hadoop. It is important to have a solid knowledge of programming languages such as SQL, Python, Scala, and Perl, and you will also need to know statistical languages, such as R.
Also, it is desirable and necessary to know about data mining, machine learning, deep learning, and have the ability to integrate structured and unstructured data. Other skills that enter the data scientist's baggage include statistical research techniques, data modeling, clustering, data visualization techniques, predictive analytics, and many more.
In this complete infographic, you have much more detail about the skills of a data scientist.
Therefore, to study Data Science, you must have a good scientific base and that is why statisticians, mathematicians and programmers are the perfect professional profiles to qualify for postgraduate and master's degrees and specialize in the data processing.
It must be said that there are many options for self-taught training, and in fact, that is how many of today's professionals in Data Science has been trained. There are no specific university majors for this, so, as with many new technology professions, a good starting point is to study a degree such as Mathematics, Statistics, or Computer Engineering.