UNIVGA Press
Job description
The data analyst and the data scientist are responsible for cross-referencing the data of the company with that made available through the web services and other digital (mobile phone..). Their goal: to make sense of this data and extract value to help the company make strategic or operational decisions.
In this context, they design models and algorithms to collect, store, process, and present the. dataIt is precisely this form of "creativity" that distinguishes the data analyst and the data scientist 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.
The data analyst and the data scientist working on a project must implement the following tasks: translate a business problem into mathematical/statistical problems; find relevant data sources; propose recommendations on the DB to modify, repatriate, outsource, internalize; design "data warehouses" (datawarehouse); evaluate data, process it, and reintegrate it into the target information system.
The data analyst (or data miner) generally inspects only a single data source (for example, the CRM – customer relationship management – of the company) through a defined model. Tasked with increasing the knowledge of a company's clientele, it conducts studies on databases, tracks the data mining tools to analyze the impact of marketing actions.
The data scientiston its side, has a more global view and cross-references data from different dispersed sources.
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).
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.
The data analyst and the data scientist occupy 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...
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.
Studies / Training to become Data analyst / Data scientist
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 big data are still few in number but are being established quickly to meet demand. Examples of training include:
Bac + 3 level
- BUT in computer science
- Professional license in computer professions: information systems and data management.
Different paths: storage infrastructures and analysis of massive data – big data (University of Burgundy).
Bac + 5 level
- Master MIAGE – applied computer methods for business management
- Master in computer science
Different paths: optimization and operational research (University of Nantes), machine learning and data mining (University of Saint Etienne), data and connected systems (University of Saint-Etienne), big data (University of Cergy), embedded artificial intelligence (University of Cergy), data mining (University of Lyon 2), MIASHS: big data and data mining (Paris 8), master of science in informatics – MOSIG (Ensimag) - Master SIAD – information systems and decision support
Different paths: data sciences, business intelligence - Master in mathematics
Different paths: statistics for evaluation and forecasting (University of Reims-Champagne Ardennes), - Master in mathematics and applications
Different paths: applied statistics and decision analysis – SAAD (University of Caen), stochastic methods and computing for decision-making – MSID (University of Pau and the Adour region), master of science in industrial and applied mathematics – MSIAM (Ensimag) - Master MIASHS – applied mathematics to human and social sciences
- Master in data sciences
- Specialized master's in big data (ESGI, Grenoble School of Management),
- MSc (Master of Sciences): statistics for smart data (Ensai), big data for business (Ecole polytechnique – HEC), data sciences & business analytics (Centrale Supelec – Essec Business School), applied data science & big data (Data science institute), data science (Ensae ParisTech), data management (PSB),
- Engineering degree with a specialization in big data:
IAMD – engineering and applications of data masses (Télécom Nancy),
big data & data science (Mines Nancy),
data science (Ensae ParisTECH),
information systems engineering (Grenoble INP Ensimag)
other schools: Isep
bac + 6 level
- Specialized Master's (MS):
Big data – management and analysis of massive data (Télécom ParisTech),
Big data: analysis management and responsible valuation (Ensimag + EMSI Ecole de management de Grenoble)
Data Science (ENSAE)
Career evolutions
After significant experience, the data analyst can evolve towards the functions of data scientist or towards pricing or revenue management services.
He can also take charge of the CRM service.
French 
English