Data Science: definition, application areas and skills required for data science

Data science is the science of data. It is the discipline that allows a company to explore and analyze raw data to transform it into valuable information to solve business problems. Learn the precise definition of the term Data Science, as well as an overview of the skills needed to become a Data Scientist.

Data science Definition: What is data science?

Data Science is a disciplinary mix between data inference, algorithm development and technologywhich is aimed at solving complex analytical problems. At the heart of this great mix are the data, the massive amounts of raw information stored in companies’ data warehouses. In concrete terms, data science is about using data creatively to generate value for companies.

Data Science helps discover insights within data sets

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First of all, Data Science helps to discover insights within data. By delving into this information at a granular level, the user can discover and understand complex trends and behaviors. The goal is to bring to the surface information that can help companies make smarter decisions.

For example, Netflix mines data to discover viewing patterns of its content to understand what interests users, and uses this information to decide which series to produce. Target identifies its main customer segments and buying behaviour to be able to address new audiences. Proctor & Gamble relies on data to predict future demand in order to optimize its output.

To extract this valuable information, Data Scientists start by first exploring the data. Faced with a complex question, the Data Scientist becomes a detective. He conducts the investigation and tries to understand patterns within the data. This requires analytical creativity. The search for information based on data is essential for strategic guidance of the company. In fact, Data Scientists act as consultants.

Data Science is used to create a Data Product

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A data product is an asset that is based on data and processes it to generate results using an algorithm.. The classic example of a data product is a recommendation engine, which ingests user data and generates personalized recommendations based on this data.

Among the most relevant concrete examples we can cite Amazon’s recommendation engine, or Netflix’s recommendation engine.. Similarly, Gmail’s spam filter is a data product, since an algorithm processes incoming mail and determines whether or not it is spam. Computer vision, used by autonomous cars, is also a data product. Its learning machine algorithms are able to recognize traffic lights, detect other cars or pedestrians, etc. The computer vision is also a data product.

Unlike Data Insights, the Data Product is not intended to advise the executives of a company in their decisions. The accompanying algorithm is designed to be directly integrated into core applications. Examples of Data Science applications include Amazon’s homepage, Gmail’s mailbox, or the autopilot software for the driverless car.

The Data Scientists play a key role in the development of data products. They develop the algorithms, test, refine and deploy them in production systems. This is why data scientists are also technical developers.

Data Science: What talents are needed to become a Data Scientist?

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The Data Science is a mix of three main areas: mathematical expertise, technology, and business.. First of all, data mining and the development of a data product requires the ability to see data through a quantitative prism. Textures, dimensions and correlations between data can be expressed mathematically. Many of the problems facing companies can be solved using analytical models based on pure mathematics. Understanding the mechanics of these models is the key to success. Reading Mooc dedicated to Data Science is a first introduction to this field of expertise.

Data science: advanced mathematical training required

From many people make the mistake of thinking that data science is all about statistics…. Statistics are important, but they are not the only form of mathematics used. For example, many machine learning algorithms are based on linear algebra. In general, a good data scientist must have a solid knowledge of mathematics.

Secondly, the data scientist must be endowed with some form of technological creativity. For good reason, uses technology to explore huge datasets and work with complex algorithms to solve complex problems. To do this, the data scientist must be able to code, create prototypes of rapid solutions, and integrate them into complex data systems. Some of the major languages associated with data science include SQL, Python, R, and SAS. Other peripheral languages include Java, Scala, and Julia. Master’s level courses and training in data science are provided by Grandes Ecoles such as Polytechnique Paris Saclay or the Master M2MO of the University Paris Diderot Paris 7. However, knowledge of these languages alone is not enough.

Data science: The challenges of a multi-tasking job

The data science specialist must be able to navigate skillfully between these languages, think algorithmically, and have the ability to solve complex problems.. These faculties are critical because the data scientist must be able to understand the complexity of the data and its flow. A lucidity with regard to the connections between these different elements is indispensable.

I mean, come on, it is paramount for a data scientist to be a tactical consultant for the company.. The data scientist works close to the data, and can therefore learn more from the data than anyone else. It is therefore their responsibility to translate their observations and share their knowledge to help solve the company’s problems. They must be able to use data to tell a coherent story using insights as a stepping stone.

This business relevance is as important as mastering the technology and algorithms.. Business objectives must be aligned with data science projects. In concrete terms, the value of a data scientist comes not only from his or her mastery of mathematics, data and technology, but from a combination of all three.

For For all companies that want to use data to drive business growth, data science is key.. Data science projects can generate significant returns on investment. However, recruiting people with the necessary skills is not an easy task. Once a talented data scientist is hired, it is necessary to keep them motivated by providing them with the necessary autonomy and offering them challenges commensurate with their skills. Learning data science requires a reward commensurate with the tasks involved. This is why data scientists are paid between 40,000 and 60,000 euros per year in Europe. In the United States, this salary can rise to as much as $150,000 per year depending on the data science requirements of companies.

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