Big Data is a valuable asset as long as it gives meaning and value to the data collected. It is the role of the data scientist to sort and interpret them so that they can be used to steer the company.
As a reminder, big data is all the digital data collected by websites, applications, physical sensors, social networks... It can be written information but also videos and images. Artificial intelligence feeds on data to increase its performance.
THE DATA SCIENTIST: PRESENTATION AND MISSIONS
The data scientist is the broadest profile in the data professions. It is also called chief data scientist or dataminer. His job is to exploit megadata once they have been retrieved, stored and cleaned by the data engineer: sorting them, structuring them, extracting the most useful ones and making forecasts. He is in charge of choosing data acquisition tools, sorting and interpretation algorithms, as well as conservation solutions (data warehouse).
His work is absolutely useful to the management of a company since the data gives functional and strategic orientations (HR, management, marketing...).
For example, in the telecommunications sector, thanks to consumption indicators and forecasts, the sales department is able to offer customized packages.
Often confused with the data analyst – specialized in a category of data around a business or strategic issue – the data scientist has a more global vision and a more transversal role.
The data scientist works on many subjects and in various fields: energy, telecommunications, media, music, fashion, banking, insurance, tourism, medical, transport, finance, agriculture... Big data is a real tidal wave for all sectors, which have understood its strategic and financial stakes.
The DPMR, the law that provides a framework for data retention and protection, has made a lot of noise. Ethical issues related to data are indeed numerous (information leaks, cybercrime, commercial incentives, environmental impacts...). The data scientist must obviously be aware of them and take them into account during his work. Data and AI lawyers and ethics officers provide solid support for specific and sensitive cases.
Aivancity programs integrate into their learning all the components of artificial intelligence and its challenges, whether technical, technological, commercial, ethical or legal. These are global and hybrid training courses that allow future engineers to benefit from a maximum level of knowledge and a wide know-how.
The data scientist is an excellent statistician and a very good analyst. He masters at least one programming language (Python, Java, Perl, C/C++...) and machine learning. Algorithms are his cup of tea.
In data science, curiosity is not a bad flaw: asking questions increases the level of expertise. Identifying the problems to be solved and the possibilities that data can offer are essential skills. Patient and agile, the data scientist knows how to democratize his conclusions so that they are understandable to all his interlocutors. Technological watch is necessary and experimentation contributes to the evolution of practices in his profession.
TRENDS AND FACTORS OF EVOLUTION
In small companies, the data scientist has other roles such as data miner and data analyst. He can also be part of a service company, such as a software company.
In a larger company, he works with the big data team. Becoming scrum master, i.e. project manager in big data is one of the possible evolutions. Managing a team and a department is a logical continuation in his career. Entrepreneurship as a consultant is another.
Systematic automation of collection and sorting processes should save 80% of data scientists' time. A shift in their tasks that should leave more room for creativity and strategy.
Between huge potential and big stakes, big data is a sensitive resource. Only experts such as data scientists can handle it by combining technological advances and respect for ethics.