Data scientist, un métier à la mode !
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Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning,computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, andhigh performance computing with the goal of extracting meaning from data and creating data products. Data science is a buzzword, often used interchangeably with analytics or big data, that is often abused for marketing anything involving data processing, in particular to re-brand existing competitive intelligence and business analytics approaches. Data Science need not be always for big data, however, the fact that data is scaling up makes big data an important aspect of data science. …
So what does a data scientist do? A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization….
Data Scientist: The Sexiest Job of the 21st Century … Who Are These People? If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive. None of those tasks is as straightforward as it is with other, established organizational roles. Start with the fact that there are no university programs offering degrees in data science. There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.