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First, Data Science uncovers insights within the data. By delving into this information at a granular level, the user can discover and understand complex trends and behaviors. It's about bringing information to the surface that can help companies make smarter decisions.

For example, Netflix mines the data to uncover patterns of viewing its content to understand what is of interest to users, and uses that information to decide which series to produce. Target identifies its main customer segments and buying behavior to be able to reach new audiences. Gamble relies on data to predict future demand in order to optimize production.

To extract this valuable information, Data Scientists first explore the data. Faced with a complex question, the Data Scientist becomes a detective. He is investigating and trying to understand the patterns within the data. To achieve this, it is necessary to show analytical creativity. Data-driven information retrieval is essential for strategic business guidance. In fact, Data Scientists act as consultants.

Data Science is a blend of three main areas: mathematical expertise, technology, and business. First, data mining and data product development requires the ability to see data through a quantitative lens. Textures, dimensions and correlations between data can be expressed mathematically. Many of the problems facing businesses 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 area of expertise.

Data science: advanced math training required

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. Many machine learning algorithms are based, for example, on linear algebra. In general, a good data scientist must have solid knowledge of mathematics.

Second, the data scientist must be gifted with a form of technological creativity. For good reason, he uses technology to explore huge data sets and work with complex algorithms to solve complex problems. To do this, the data scientist must be able to code, prototype rapid solutions, and integrate them into complex data systems. Among the main languages associated with data science are SQL, Python, R, and SAS. In the periphery, we also find Java, Scala, and Julia. Master level data science courses and courses are provided by grandes écoles such as Polytechnique Paris Saclay or the M2MO master at Paris Diderot University in Paris 7. However, knowledge of these languages alone is not enough.

Data science: The challenges of multitasking

The Data Science specialist must be able to navigate skillfully between these languages, think algorithmically, and have the ability to solve complex problems. These abilities are critical because the data scientist must be able to understand the complexity of the data and its flow. A clear understanding of the connections between these different elements is essential.

Finally, it is essential for a data scientist to be a tactical consultant for the company. The data scientist works close to data, and can therefore learn more from this data than anyone else. It is therefore incumbent on him to translate his observations and share his knowledge to help resolve the company's problems. He must know how to handle data to tell a coherent story by using insights like a landing.

This relevance for the business is as important as the mastery of technology and algorithms. The company's objectives must be aligned with data science projects. Concretely, the value of a data scientist comes not only from his or her mastery of mathematics, data and technology, but from a combination of the three.