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Data science can also help operational and strategic decision-making thanks to the automation of decision-making processes or their acceleration. This work is used in particular to develop decision support tools for employees and more particularly, customer advisers.
Data science is also used to simplify and facilitate customer relations. Its methods help evolve digital interfaces for customers by making them more dynamic, more personalized and easier to use. For example, in the life of a contract or when it is initialized, data science work makes it possible to gain speed in processing customer requests, to facilitate and simplify the customer relationship - by avoiding, for example, asking him redundant information - or anticipate customer needs rather than staying in reaction by limiting themselves to meeting them.
This job is very new, there is no universal profile, but rather profiles that are adapted to each company.
A data scientist must have in-depth knowledge of the profession in which he works. Thus, a data scientist working in the banking environment will essentially need to know everything about how a bank works. He also has boundless imagination and curiosity, which allows him to ask the right questions.
So the data scientist must be able to work with large amounts of unstructured, uncleaned data from different sources via the internet. He is familiar with data mining techniques for extracting knowledge from large amounts of data. He is also aware of machine learning, which seeks to predict future behavior. However, a data scientist not only collects and reports data, but also has to be able to look at it from all angles, determine what it means, and then recommend recommendations for action. link with this data and of course without forgetting to process and generate it in the form of data usable by users.
In summary, we can say that Data Science is a mixture between three main fields: 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 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 can also help operational and strategic decision-making thanks to the automation of decision-making processes or their acceleration. This work is used in particular to develop decision support tools for employees and more particularly, customer advisers.
Data science is also used to simplify and facilitate customer relations. Its methods help evolve digital interfaces for customers by making them more dynamic, more personalized and easier to use. For example, in the life of a contract or when it is initialized, data science work makes it possible to gain speed in processing customer requests, to facilitate and simplify the customer relationship - by avoiding, for example, asking him redundant information - or anticipate customer needs rather than staying in reaction by limiting themselves to meeting them.
This job is very new, there is no universal profile, but rather profiles that are adapted to each company.
A data scientist must have in-depth knowledge of the profession in which he works. Thus, a data scientist working in the banking environment will essentially need to know everything about how a bank works. He also has boundless imagination and curiosity, which allows him to ask the right questions.
So the data scientist must be able to work with large amounts of unstructured, uncleaned data from different sources via the internet. He is familiar with data mining techniques for extracting knowledge from large amounts of data. He is also aware of machine learning, which seeks to predict future behavior. However, a data scientist not only collects and reports data, but also has to be able to look at it from all angles, determine what it means, and then recommend recommendations for action. link with this data and of course without forgetting to process and generate it in the form of data usable by users.
In summary, we can say that Data Science is a mixture between three main fields: 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 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.
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