Data training: get your certification

Train in Data and obtain your diploma of "Designer-Developer in Data Science" recognized by the State

Jedha Campus Lyon
Data Science has become an essential element in the last few years to enable companies to innovate and differentiate themselves and Artificial Intelligence is an essential performance tool. Business departments in all sectors are already using these tools, and this trend is set to increase exponentially over the next few years. The objective of creating the "Data Science Designer-Developer" title is to train data science specialists with the ability to have a global vision of data science projects, capable of designing and managing them as a whole as well as intervening at a specific point of the latter.

Indeed, the certification gives the candidate the skills to exercise the profession of "Data Science Designer-Developer", which is both technical and transverse. All of these skills, described in the reference framework, enable him/her to create robust and adapted data management infrastructures, to feed them, to develop artificial intelligence algorithms, to put them into production, but also to collaborate with the various business teams of an organization to evaluate and adapt data needs. It is therefore possible to lead end-to-end data management projects, to report on them, to be a force for proposal and to adapt them to the needs of the professional environment.

The certified candidate will have acquired all the skills required to work and be operational quickly.
The "Data Science Designer-Developer" is both:
  • A technician: can create robust data management infrastructures, develop artificial intelligence algorithms and put them into production.
  • A manager: he/she collaborates with business teams, assesses and adapts data needs according to the organization and its core business, and therefore leads end-to-end data management projects.
It is everywhere:
  • Various exercise frameworks: employee in specialized companies, for organizations using AI, as a freelancer, member of a data team, data referent of a structure, manager, company director, trainer
  • His analytical work is the basis for the development of general strategies: it is essential in most professional sectors: ce (marketing, sales), health, finance, research & development, administration, logistics, security, etc.
  • Different technical and managerial functions in the field of data.

Details of items

Data Scientist

An expert in the management and analysis of massive data ("Big Data"). He/she determines indicators that allow the implementation of a strategy to respond to a problem from multiple and dispersed data sources. He is therefore specialised in statistics and IT and knows the sector or the application function of the analysed data.

Data Analyst

In charge of the analysis of a single data source via a defined model. He/she will be in charge of a specific mission, such as increasing the knowledge of a company's clientele: to do this, he/she will have to conduct studies on the databases and follow the datamining tools to analyse the impact of marketing actions.

Data Engineer

In charge of designing the infrastructure for storing and processing potentially massive amounts of data. His work is upstream: he ensures that the storage and data management tools are sufficiently robust, secure and clear to be analysed by the Data Analysts and transformed by the Data Scientists.

Full-Stack Developer (or Software-Engineer)

An expert in the design of web applications coded in Python. This person is able to design and maintain programs accessible from the Internet, to cooperate with other developers on the same project and to draw up specifications for the technical needs of an organization.

Machine Learning Engineer

At the crossroads between the Data Scientist and the Data Engineer, he/she builds artificial intelligence algorithms and puts them into production in the organization's infrastructure. He/she interacts with the latter to industrialize AI algorithms and to extend their application over a large perimeter (such as all of a company's customers or a web or mobile application).

Product Manager Data

He/she plays the role of coach and intermediary between the purely technical data teams and the management teams. The Product Manager Data specializes in coordinating the teams that design data products, and then reports the results of progress to the business divisions, which will make decisions on the strategy to be followed.

Business Analyst

His functions are similar to those of the Data Analyst. The latter will have applications directly linked to the business teams. He will therefore manage smaller volumes of data and, above all, will be very specialized in the business he covers.

Data Project Manager

His role is to monitor and manage one or more data projects. He must gather all the elements necessary for his technical teams to move forward with a data project. He/she must therefore have in-depth technical knowledge to determine the feedback describing the project follow-up.

Artificial Intelligence Consultant

Its goal is to accompany an organization in its Data transition. This transition can be the design or update of a data management infrastructure, the production of algorithms or the development of performance indicator tables. The AI consultant popularizes complex concepts and educates and trains his clients.

Its professional activities: the life cycle of a Data project

Block 1 - Building and powering a data management infrastructure

Block 2 - Exploratory, descriptive and inferential data analysis

Block 3 - Predictive analysis of structured data using artificial intelligence

Block 4 - Predictive analysis of unstructured data using artificial intelligence

Block n°5 - Industrialization of a machine learning algorithm and automation of decision processes

Block #6 - Data Management Project Management

Professional integration

The overall insertion rate (classes of 2019 and 2020) of "Data Science Designer Developer" graduates on the job market:
  • 80%, of which 65% in the target occupation 6 months after graduation
  • 89%, of which 84% in the target trade 1 year after graduation
The average entry-level salary is €43,200 gross/year or equivalent.

The impact of Jedha on the careers of our learners

Impact of Fullstack training on the career of certified employees

Expected impact
Greater than expected impact
No impact

Did they completely reorient themselves after the training?

Don't know
The majority of Jedha's "Data Science Designer Developer" incumbents (77%) stated that the training had already had a direct impact on their professional career; 19% of them stating that this impact was greater than they had expected

43% of these incumbents stated that the "Data Science Designer Developer" training and certification had enabled them to make a complete career change

The majority of learners (63%) start the training with the objective of finding a job in data.

Other objectives are also cited: going freelance (14%), creating a company (8%), moving up internally in the organization where the learner currently works (8%)

Objectives of learners at the beginning of the course

Find a job in Data
Going freelance
Internal career development within the company
Setting up a Tech business

Our learners: who are they?

Our learners come from a wide range of sectors, levels of study and situations at the beginning of their training

Level of education of learners at the beginning of the course

Professional sectors of learners at the beginning of the training

Status of learners at the beginning of the course

The vast majority of learners already hold a Master's degree (4 to 6 years of higher education - 75%). Among the others, 12% have a doctorate (Bac + 8) and 12% have a Bac + 3 or less

At the beginning of the course, the majority of learners are looking for work (39%) or are already employed (39%). The remaining 20% are students, self-employed and entrepreneurs.

The majority of learners stated that they had already worked in IT or in the field of new technologies at the beginning of their training (30% and 13% respectively). Marketing and sales is also a predominant sector from which the future "Data Science Designer and Developer" graduates come (18%). The remaining 40% of learners come from increasingly diverse fields, already very open to the practice of Data Science, but whose needs are still growing: finance, administration, research, health and logistics in particular.)