{"id":2485,"date":"2021-03-04T09:28:39","date_gmt":"2021-03-04T09:28:39","guid":{"rendered":"https:\/\/staging.univga.org\/?p=2485"},"modified":"2021-03-04T09:30:09","modified_gmt":"2021-03-04T09:30:09","slug":"data-analyst-data-scientist","status":"publish","type":"post","link":"https:\/\/univga.org\/en\/data-analyst-data-scientist\/","title":{"rendered":"Data analyst \/ Data scientist"},"content":{"rendered":"
The data analyst and data scientist are senior professionals responsible for managing and analyzing \"big data\". These specialists in numbers, statistics, and computer programs process a company's data to extract information that can assist in decision-making. Unlike the data scientist, who has a cross-functional vision, the data analyst focuses on a specific type of data.<\/div>\n
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Job description<\/h2>\n
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The\u00a0data analyst<\/strong>\u00a0and the\u00a0data scientist\u00a0<\/strong>are responsible for cross-referencing the\u00a0data\u00a0<\/strong>of the company with that made available through the\u00a0web services<\/strong>\u00a0and other\u00a0digital\u00a0<\/strong>(mobile phone..). Their goal: to make sense of this data and extract value to help the company make strategic or operational decisions.<\/p>\n

In this context, they design models and algorithms to collect, store, process, and present the.\u00a0data<\/strong>It is precisely this form of \"creativity\" that distinguishes the\u00a0data analyst<\/strong>\u00a0and the\u00a0data scientist\u00a0<\/strong>from the pure statistician: they are capable of imagining new analysis models to process raw and heterogeneous data that cannot be analyzed using traditional database management tools.<\/p>\n

The\u00a0data analyst<\/strong>\u00a0and the\u00a0data scientist<\/strong>\u00a0working on a project must implement the following tasks: translate a business problem into mathematical\/statistical problems; find relevant data sources; propose recommendations on the\u00a0DB\u00a0<\/strong>to modify, repatriate, outsource, internalize; design \"data warehouses\" (datawarehouse); evaluate data, process it, and reintegrate it into the target information system.<\/p>\n

The\u00a0data analyst\u00a0<\/strong>(or data miner) generally inspects only a single data source (for example, the CRM \u2013 customer relationship management \u2013 of the company) through a defined model. Tasked with increasing the knowledge of a company's clientele, it conducts studies on databases, tracks the\u00a0data mining tools<\/strong>\u00a0to analyze the impact of marketing actions.<\/p>\n

The\u00a0data scientist<\/strong>on its side, has a more global view and cross-references data from different dispersed sources.<\/p>\n

These professionals combine a triple skill set: statistical and computer expertise, knowledge of databases and computing, and industry experience in their field (marketing, finance, for example).<\/p>\n

These professions require rigor and organization as the monitoring of company data is carried out regularly according to very targeted procedures. Of course, one must be passionate about numbers and statistics and adhere to confidentiality rules because the data handled by the data analyst and the data scientist are inherently sensitive and strategic.<\/p>\n

The\u00a0data analyst<\/strong>\u00a0and the\u00a0data scientist<\/strong>\u00a0occupy a central place within an organization as their analysis work is part of the organization's strategy. They can thus identify purchasing or consumption trends, develop customer profiles, determine their expectations...<\/p>\n

Linked to the emergence of big data, these specialists are recruited by industries, large companies, retail, companies in the finance sector, or even medical or paramedical organizations.<\/p>\n<\/div>\n<\/div>\n

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Studies \/ Training to become\u00a0Data analyst \/ Data scientist<\/span><\/h2>\n

A Bac + 4 or Bac + 5 in computer science, management, statistics, or marketing is essential to hold this position. Some Bac + 3 level training allows for assistant positions. The training programs that prepare for the profession of\u00a0big data\u00a0<\/strong>are still few in number but are being established quickly to meet demand. Examples of training include:<\/p>\n

Bac + 3 level<\/h3>\n