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Demystifying Data Science: Understanding Its Definition and Scope

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data science definitoio

In 1991, Moore defined statistics as the science of data. As such, it is a discipline that requires the use of functionalities from different fields, such as mathematics, computer science and statistics.

The aim of Data Science is to enable a company to analyze raw data and transform it into useful information for solving any problems the company may encounter. In this sense, data science uses algorithmic or scientific means to extract this information from ordered or unordered data. It is often associated with Big Data.

Data science includes machine learning, statistical learning, programming, uncertainty modeling, data warehousing and high-performance computing.

What is data science?

Data science is a mixture of data, algorithm implementation and technology. The aim of all these elements is to solve complex problems. This mix includes large quantities of unidentified information stored in corporate data warehouses. More clearly, the aim of data science is to study raw data in order to create value from it.

Firstly, Data Science is about finding insights at the heart of data. By delving into this information, the Data Scientist will be able to deduce complex trends and behaviors. This will bring information to the surface to help companies make smarter decisions.

For example, Netflix uses data science to discover viewing trends for its films and series, so it knows what generates user interest, and can then decide which series to make or not.

Data science: how does it work?

To obtain this information, data experts first explore the data. The Data Scientist, for example, investigates the data to understand the groups to be established within it. From then on, the data scientist must demonstrate an analytical mind. Data science is therefore essential to help the company in its strategy: Data Scientists play the role of consultants.

Data science: what are the qualities you need to make a career of it?

Data Science brings together three major fields: mathematics, technology and business. Firstly, the study of data and the creation of a Data Product requires the ability to understand data sets through a quantitative prism. Relationships between data can be highlighted mathematically. As a result, some of the problems faced by companies need to be solved by analytical models derived from the study of mathematics.

Many people think that data science is all about statistics. However, statistics are not the only form of mathematics necessarily used: many Machine Learning algorithms use linear algebra, for example.

So, generally speaking, a good data scientist needs to have a fairly well-developed knowledge of mathematics.

The Data Scientist must also demonstrate great technological creativity. Indeed, he or she uses technology to study large quantities of data, using algorithms of great difficulty, and to solve difficult problems.

As such, the Data Scientist needs to know how to code, prototype solutions and integrate them into systems. Among the most widely used data science languages are Python and SQL. Less common but still used are Java, Scala and Julia.

I'm interested in data science!

Now you know why and how to join a Big Data training course at DataScientest. For more information, discover tomorrow’s jobs in AI and Big Data and how to choose the right company as a Data Scientist, Data Analyst or Data Engineer.

However, it’s important to remember that academic prerequisites will be required to access the related training courses. Nevertheless, it is very often possible to qualify for our training courses: I therefore advise you to make an appointment with one of our consultants to get the answers to your questions and discuss your data science training projects together.

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