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  • by Team Handson
  • may 1st, 2022

What is Data Science and its importance in the future.

If you are interested in technology and its applications on a business scale, you have to know what Data Science is. Also known as data science, it is knowledge that is applied to the large volumes of information that new forms of digital collection allow. Through a combination of statistical methods, mathematics, information technology, interpersonal relationships and knowledge of user consumption patterns, data can be interpreted for commercial purposes. Basically, making decisions to improve the parameters of a company. Data science achieves that a person has the ability to analyze the information obtained through different channels. Sometimes we may not be aware of the situation, but every time we navigate, we leave a trail. This information footprint can be captured to draw interesting conclusions. Even, you may not have known what data science is, but at some point, you have implemented a strategy to interpret the information. In other words, if at any time you came to conclusions about the metrics of a service, you will have realized that you have generated a kind of user "profile" on which to base your decisions. As we mentioned, the digital world allows each movement made by Internet users to generate a trail. This can be applied in social networks, e-commerce stores, surveys and even the use of mobile phones or computers on web pages. The problem is that sometimes the amount of data can be overwhelming and difficult to interpret.
In any case, Data Science is not a new concept. The term for data science actually began in about the 1970s. While the information was not identical to what exists today, the meaning was the same, a person in charge of processing the data to through different methodologies. One of the main mistakes is associating the work of a data scientist with the concept of Big Data. In other words, to understand what a Data Scientist is, we will also need the second term, although they are not the same thing. Precisely, Big Data implies the set of data on a large scale, which the professional will have to interpret. In other words, it is the raw material on which the work is done. These are not synonyms, but the appearance of Big Data makes the work of a data scientist possible. By discovering the patterns of this enormous volume of information, an ideal processing can be achieved so that the client discovers a new vision, providing knowledge and values that are very useful at a business level. In the same way, it must also be differentiated from Data Mining. When we talk about Data Mining, we refer to the process of collecting and storing useful data. Although the information is segmented according to interests, this does not mean that it is transformed into applicable knowledge to make decisions, something that Data Science can do through analytical interpretation.

Who is a Data Scientist and what does he do?
By this time, you may not yet know what a data scientist is. The first thing to clarify is that it is a job profile widely demanded by companies. It is in charge of translating an enormous volume of information, which is available in Big Data. 1.- Work on SEO positioning
To understand what a data scientist does, you have to keep in mind that all this information comes from different massive information sources. Therefore, he has the difficult task of segmenting and interpreting each of the elements that may be relevant to a business, providing reliable answers to different problems. The profile of the Data Scientist implies having great mathematical and statistical knowledge. In addition, they have to be pioneers in the use of great technologies, which are constantly being updated to offer optimal services. Likewise, they must take into account massive data analysis systems, such as Machine Learning. Precisely, this last concept refers to a scientific discipline that allows generating systems that learn automatically. This implies recognizing different complex patterns in millions of data, something especially relevant for large companies or those that handle different variables on which changes and improvements can be made. When we understand what a Data Scientist does, we realize that information is not only obtained from a single source, as is the case with a classic data analyst. In reality, different systems converge to have a global vision of the problem. No possible solution is excluded, but all can be useful to solve problems. Therefore, it should be noted that a data scientist not only extracts the information, but rather interprets and values it.

The work methodology is as follows:
Extract the data. It does this regardless of whether it's multiple sources, huge volumes, or hard-to-process data. Generate a clean-up. Sometimes, the information must be filtered according to the specific objectives requested. Start processing. In other words, it applies different methodologies based on statistics. Makes a redesign of the information. This will depend on who you are trying to present them to. For this reason, he sometimes gives them a "makeover" to make them easier to understand. More than 600 technical courses, taught by professors with decades of experience in their field, so that you become an unbeatable professional

Data Science Applications
Data science and Big Data have a wide variety of applications. The largest companies are betting on this profile.
So, the Data Science applications could be the following:
Extract information in an efficient way. This can involve breaking down huge data sources, which can often be confusing to interpret, to give them significant added value for a company.
Visualize the problems in a clear way. Sometimes, the main problem that organizations have is not knowing how to read their own data. Therefore, having a person specialized in this function will be extremely useful in making decisions.
Understand why things happen. As many of these works are based on information patterns, there may be several trends that you were not aware of. This applies both to solving problems and to the possibility of optimizing your resources.
Get solutions and constantly improve. While some companies will implement it to avoid risk, the reality is that it is an essential feature for growth. In other words, costs can be reduced and profits increased only by taking into account the variables that come into play in the company.
You make objective, well-founded and projected decisions. One of the most relevant points of this work. By having a data scientist, you will be evaluating all these elements to take advantage of them, improving your present, but also projecting for the future.

Future of Data Science
Understanding what data science is for means understanding the future of this discipline. We are talking about a way of acquiring knowledge that is revolutionary, not only in technological terms, but also in terms of the possibilities of maximizing income for any organization. One of the main areas that draws on this methodology is marketing. When implementing campaigns to maximize revenue, you will always need to do so based on data. Sometimes an overwhelming amount of information could overwhelm a simple marketing analyst. Instead, Data Science provides a fundamental component to be able to make decisions: objectivity. This does not mean that the choices on the road are always objective, but the truth is that there is a support based on having accurately analyzed each of the variables that come into play. Leveraging data science is essential for companies, especially in these times, where competition is extensive. The same category can have thousands of companies that are struggling to reach the same segment of the public.

So how can you take advantage?
A Data Scientist could answer that question by analyzing each of the behaviors that customers and users in your sector have with your brand and their respective products. In other words, a behavior is nothing more than structured data, which can be broken down to understand what is in the background. Hence, more and more companies are valuing this type of professionals. Hierarchical managers fully understand that the data exists, but that it must also be understood correctly to make decisions. At the end of the day, what is sought is to maximize profit, something that this worker profile can achieve in incredible ways

What to study to be a Data Scientist
At this point, you are probably wondering how to be a data scientist. The reality is that the technological boom has created a gap between the needs of companies and the training of candidates. For this reason, there is a huge offer for this sector, but there are not so many professionals trained to practice. This optimistic professional horizon has allowed many people to wonder what to study to be a data scientist. Although there is no career that exclusively teaches this task, the reality is that there are a series of skills or knowledge that must be learned.

The most common are:
Scientific skills. Among them, the possibility of using different statistical methods, but that's not all. You also need structures to extract, clean, and process data. In the first instance, this job requires methodological meticulousness, so it is essential that the candidate is used to working with large databases.
Math and computer skills. Although it is not an exclusive requirement, mathematical knowledge is very important for a data scientist, such as statistical modelling or A/B tests. All this must be complemented with training in computer databases, such as SQL and NoSQL, as well as others, such as Pig, Hive, MapReduce or Hadoop.
Design, communication and marketing skills. Once you have interpreted all this data, you are going to have to present it in a nice way. For this reason, knowledge of the Adobe suite (Photoshop, Premiere, or After Effects) may be required, in addition to other presentation systems such as Excel or PowerPoint. Many want to know how to become a Data Scientist, but believe that communication is not important.

Quite the contrary:
it will be essential to carry out a communicational reading of the public to whom this data is directed, in such a way that you know what their needs are, their tastes and the possible impact of the decisions that will be made.

Data Science Tools
You have already seen what to study to be a Data Scientist. However, there are also some Data Science tools that you can get to know and that will be extremely useful in your future projects within a company.

The most important can be:
Scikit-learn. In this case, we are talking about a Python module, a popular programming language. It is a software library based on Machine Learning, which implies an automatic learning method. There you can discover different algorithms to classify, analyze and interpret groups. At the same time, different software packages are included, such as Pandas, SymPy, ScyPy, among others.
Tensorflow. Again, we have an open-source library, designed particularly for the field of Artificial Intelligence. Through a relationship of the data, patterns and reasoning can be generated, just as a human being would do. It may be a good idea to understand it, to emulate what are the logical structures underlying the interpretation of the information.
R. Another programming language, although more focused on statistics. Ideal to be able to visualize, analyze and understand the data, calculating and statistically analyzing the groups that are of interest for your objectives. Basically, a function designed particularly for Data Mining, but also for Data Science.

Data Science Tools
You have already seen what to study to be a Data Scientist. However, there are also some Data Science tools that you can get to know and that will be extremely useful in your future projects within a company.

The most important can be:
Scikit-learn. In this case, we are talking about a Python module, a popular programming language. It is a software library based on Machine Learning, which implies an automatic learning method. There you can discover different algorithms to classify, analyze and interpret groups. At the same time, different software packages are included, such as Pandas, SymPy, ScyPy, among others.
Tensorflow. Again, we have an open-source library, designed particularly for the field of Artificial Intelligence. Through a relationship of the data, patterns and reasoning can be generated, just as a human being would do. It may be a good idea to understand it, to emulate what are the logical structures underlying the interpretation of the information.
R. Another programming language, although more focused on statistics. Ideal to be able to visualize, analyze and understand the data, calculating and statistically analyzing the groups that are of interest for your objectives. Basically, a function designed particularly for Data Mining, but also for Data Science.

How much does a Data Scientist make?
Finally, there are many questions about how much a data scientist earns. In reality, as with any job, the salary will depend on the company, the workload, the specific sector in which it operates and the experience that the employee has. In general terms, it could be said that a junior data scientist does not usually have a differentiated salary from any junior who handles analytics. In theory, the minimum range in which they move is about 23,000 or 32,000 euros per year. If you want to start developing in this discipline, you already know how much a Data Scientist earns. However, if you are interested in projecting for the future and knowing how much a senior Data Scientist earns, the amount usually increases between 35,000 and 80,000 euros per year. Of course, it will depend on your knowledge, experience and the company you work for, but these are very well-paid jobs.
Therefore, we could see that Data Science implies enormous job opportunities. This is an area of work that all companies need to make a difference, through a quantitative and qualitative analysis of all the variables that are of interest to them. Manage a company's resources and make smart decisions based on data science!