In the times of algorithms and massive data collections to sell products and make decisions, it is necessary to draw from the huge accumulations of data and information. Data Science is the art and science of drawing more accurate conclusions and data.
You have most likely seen it on job search websites for jobs related to advanced computer science. Many technology companies ask for experts in Data Science, but what does this computing discipline consist of and why is it requested?
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A brief history of Data Science
In fact, this term began to be used in the 70s to refer to data processing methods, either entered by users or by machines. From there, ideal behavior patterns can be derived for engineers, digital marketing experts, and more. In 2011 the concept was recognized as an independent discipline from other fields and today there is no shortage of centers and specialized courses in data science training.
The difference between Big Data and Data Science
With that short definition, it's easy to think that Data Science is synonymous with Big Data, but it has its differences. Big Data is about the huge, high-volume, raw data set, which has been entered and collected in huge files, and solving data management and storage problems. With this, behavior and data patterns can be generated.
Data Science instead, are the tools with which all this data is transformed into useful information for all types of clients around the world. Reconstructing data and drawing useful conclusions from huge files, rows and columns is what has made Data Science one of the most in-demand professions in recent years.
The concepts of Data Science
Now we go with a more exact definition of what Data Science is, with frequently used concepts and at what times and scenarios they are used, and its purposes for clients.
Data Mining
Or data mining in Spanish. This is the process of collecting data and storing it as long as it is useful. In this process, data patterns are analyzed in different batches using different data collection software. With this, it is possible to obtain information about the clients of a company and with it develop strategies either product, service or marketing that are more effective, such as launching a campaign specialized in clients who buy but not enough despite having intention to buy more of a product or hire a greater service.
Deep Learning
The so-called neural networks or artificial intelligences play a large part in Data Science. In the first step, the information is captured, the desired calculations are carried out and finally the information is shown in the last step. With this they are able to process text and recognize images.
Machine Learning
This goes in conjunction with Deep Learning, but in the Machine Learning part, the errors that the recognition program can make are corrected. In the rough, Machine Leaning is teaching a program to have the desired results following a pattern desired by the client, such as the Youtube algorithm who wants to recommend videos for you to stay on his website.
Artificial Intelligence
With everything said above, artificial intelligences are created that follow the patterns and instructions that we give them, EL Data Science is the process in which all the information, patterns, errors and successes are obtained to make artificial intelligences effective. It is all this that makes them, for example, able to advertise products to us simply by reading a recent location after simply agreeing to show us personalized advertisements.
What jobs are there in data science and why have they grown so much?
Simply put, right now we live in a technological world full of recommendations, algorithms and patterns that feed millions of artificial intelligences with different intentions. Some of the jobs offered by a Data Science training:
- Data Scientists are the ones who extract the data by building software and algorithms
- Data Engineer, who make data accessible and manipulable by Data Scientists
- Chief Data Officer, responsible for data management
- Data Analyst, interpret data extracted after being analyzed
