Data Science Certification
Train in Data and obtain your state-approved RNCP-registered Data Science Designer-Developer certificate.

Data Science has become in recent years an indispensable element for companies to innovate and differentiate themselves and Artificial Intelligence an indispensable performance tool. Management in all sectors are already using these latest innovations. This trend is set to increase exponentially over the next few years. The objective of creating the "Data Science Designer and Developer" qualification 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 in them.
Indeed, the certification gives the candidate the skills to exercise the profession of "Data Science Designer - Developer", 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 organisation to evaluate and adapt the Data needs. It is therefore possible to lead data management projects from start to finish, to report on them, to make proposals 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 or she collaborates with business teams, evaluates and adapts data needs according to the organisation and its core business. He/she thus leads end-to-end data management projects.
It's everywhere:
- Various exercise frameworks: employee in specialised companies, for organisations using AI, as a freelancer, member of a Data team, Data referent of a structure, manager, company director, trainer
- Its analytical work forms the basis for the development of general strategies: it is indispensable in most professional sectors, health, finance, research & development, administration, logistics, security, etc.
- Different technical and managerial functions in the data field.
The life cycle of a Data project
The "Data Science Designer - Developer" certificate is divided into the following 6 blocks. Each of the Jedha courses validates different blocks:
- Data Essentials training validates Block 6, valid for 5 years: Data management project management.
- The Data Fullstack training course validates the entire "Data Science Designer - Developer" certification (all 6 blocks), which is valid for life.
- The Data Lead training course validates Block 1, which is valid for 5 years: Building and feeding a data management infrastructure
General terms and conditions for acquiring certification
Certification is valid for life. In the event of partial validation of blocks, each block of skills remains valid for 5 years. Validation of all 6 skill blocks is mandatory to obtain certification. Partial validation of a block is not possible. Candidates who have validated only some of the 6 blocks of skills in the "Data Science Designer Developer" reference system will be issued with a skills certificate attesting to partial validation of the professional certification, and naming the blocks concerned.
Certification is acquired by capitalization of the blocks of skills and by equivalence. Equivalences to obtain this certification are also possible, the full list of organizations delivering equivalent training can be found on this page, under the heading "Links with other professional certifications, certifications, or habilitations".
VAE procedure
In the event that a person feels they have the skills described in the blocks below, they can apply to go through the VAE (Validation des Acquis par l'Expérience) procedure to obtain the (full or partial) Jedha "Data Science Designer-Developer" certificate. Full details of this procedure are given on this page.
- Design a robust and suitable data architecture by creating data lakes and data warehouses to meet the organization's storage, utilization, security and protection needs
- Integrate the storage and distributed computing dimension into the data infrastructure through the use of tools such as Spark or AWS Redshift in order to adapt it to Big Data management needs
- Collect data from various sources (Web, internal Sage/Excel software or external Google Analytics) via programming libraries such as Scrapy or Beautifulsoup to feed the Data Lake and refine the results of future analyses.
- Cleanse and organize data in the Data Warehouse by writing extraction, transformation and loading (ETL) processes to make it available and understandable to other business teams.
- Assessment: A case study on real data
- Evaluation topic: Construction of a Cloud infrastructure hosting Big Data (web data collection, integration of data into a Data Lake, cleansing and loading of data into an AWS Redshift-type database by parallelized processing if necessary via the construction of an ETL process).
- Process databases using descriptive and inferential statistical analyses via programming libraries such as Numpy or Pandas, to organize and clean them in order to normalize them with respect to the population under study.
- Perform univariate and multivariate analyses on structured databases to specify relationships between several variables and establish statistical links between them.
- Optimize statistical analysis through parallelized processing, using tools such as Spark to speed up computer processing time, so you can analyze massive volumes of data (Big Data)
- Present the results of a statistical analysis of structured data, whether massive or not, using programming libraries such as Plotly or Matplotlib to synthesize these results for a lay audience in order to facilitate decision-making and support their operational applications.
- Evaluation: Two case studies on real data
- Assessmenttopic:
- Handling missing values and outliers from a non-massive database and then analysing them to determine and present trends through graphs.
- Analysis of a massive unstructured database (using Spark) adapted to a defined problem.
- Process structured data by creating a processing pipeline using programming libraries like Scikit-Learn to encode, normalize and slice data to make it interpretable by a Machine Learning algorithm
- Perform predictive analyses on a structured dataset using adapted supervised machine learning algorithms in order to automate tasks related to the prediction results of these algorithms
- Develop an unsupervised machine learning algorithm to segment a database into different homogeneous groups or reduce the size of the database in order to understand observations in a granular way and allow their visualisation
- Evaluate the predictive performance of machine learning algorithms by determining the influence of different variables so that they can be improved to demonstrate their usefulness to business departments, in relation to processes already established in the organization.
- Assessment: three practical case studies drawn from real cases
- Evaluation theme:
- Optimization of lead qualification marketing processes through supervised learning algorithms
- Optimization of supervised machine learning algorithms on unbalanced databases
- Localization of geographic density zones through the development of unsupervised machine learning algorithms.
- Process unstructured data (image, text, audio) by creating processing functions using programming libraries such as TensorFlow or Numpy to transform them into matrices for interpretation by a deep learning algorithm
- Develop adapted neural networks (classical, convolutional or recursive) by superimposing neural layers via programming libraries such as TensorFlow to analyze unstructured data in order to detect signals on the latter
- Create a robust and accurate algorithm by configuring a deep pre-trained neural network to address prediction problems on massive data volumes
- Create unstructured data by building adversarial neural networks to construct new training bases for artificial intelligence applications
- Evaluate the performance of a deep machine learning algorithm by assessing indicators on training and validation data in order to industrialize its use
- Evaluation: a practical case study on unstructured data
- Evaluation topic: Sentiment analysis, by developing an algorithm to determine a user's feelings about a product (with the possibility of creating new data to enhance the database).
- Standardize the construction and computing environment of a machine learning algorithm using production tools such as MLflow and Docker to make it easier to put artificial intelligence projects into production on all types of platforms
- Create an application programming interface using tools such as AWS SageMaker to give all relevant business teams access to machine learning algorithm predictions at scale
- Deploy a web application integrating predictive statistics algorithms (Machine Learning and Deep Learning) thanks to tools such as Flask, Heroku or AWS Sagemaker to make them usable by all business teams in order to automate their decision-making process
- Evaluation: a practical case study on the deployment of a machine learning algorithm
- Assessment topic: web dashboard, construction and production of an artificial intelligence web application.
- Translate business challenges into mathematical/data issues thanks to an understanding of the needs specific to each data project in order to meet the organization's objectives
- Mastering the most recent and adapted technologies on the market thanks to technological watch and constant practice to develop expertise. The objective is to be able to propose to business departments the most appropriate solutions to a problem and the constant improvement of data management processes already in place
- Define specifications, a retroplanning schedule and a budget in order to defend and detail to business departments a data project that meets the organization's needs
- Manage a data analysis and management project (descriptive statistical analysis, Machine Learning, Deep Learning, Big Data or not) by developing appropriate indicators and dashboards, in order to monitor and assess the action, as well as the operational implementation of its results
- Communicate the information extraction and data analysis process to the business units, making it accessible to the general public in order to support the implementation of future strategy and actions.
- Lead a data management project, from its conception to the implementation of solutions, in order to see it through to completion, to be the key person with all the information on the project at all times, and to support other departments in the organization in all activities relating to it
- Evaluation: a data project designed from A to Z.
- Assessment topic: free. Learners can prepare the data project of their choice. This can be personal, developed by the candidate as part of his/her professional activity, or defined by a partner company.
Success rate
The pass rate for the "Data Science Designer-Developer" certification is calculated on the basis of students attending their certification session. We distinguish between the overall pass rate, which includes candidates who have partially passed the certification, and the total pass rate, which only includes candidates who have passed all the certification blocks:
Overall success rate 2022
95%
Total success rate 2022
79%
Professional integration rate
The overall labour market integration rates of the "Data Science Designer-Developer" graduates are as follows for integration at 6 months, 1 year and 2 years:
- 71%, of which 58% in the intended occupation 6 months after graduation
- 83%, of which 63% in the intended occupation 1 year after graduation
- 95%, of which 84% in the intended occupation 2 years after graduation
The average entry-level salary is €45,230 gross / year or equivalent.
The impact of Jedha on our learners' careers
The impact of Fullstack training on the careers of graduates
55%
Expected impact
18%
Greater impact than expected
26%
No impact
Did they change direction completely after the course?
54%
Yes
46%
No
The majority of Jedha's "Data Science Designer Developer" holders (55%) stated that the training had already had a direct impact on their professional career; 18% of them declaring that this impact was greater than they had expected.
53.6% of these holders said that the "Data Science Designer Developer" training and certification had enabled them to make a complete career change.
The majority of learners (78%) start the training with the objective of finding a job in data.
Other objectives are also cited: going freelance (16%), setting up a company (12%), moving up internally in the organization where the learner currently works (17%).
Learners' objectives at the start of training
78%
Find a job in Data
16%
Going freelance
12%
Internal career development
12%
Setting up a Tech business
2%
Other
Our learners: who are they?
Our trainees come from a wide variety of sectors, educational backgrounds and situations at the start of their training.
Learners' level of education at start of training
Learners' professional sectors at the start of training
Learner status at the start of training
The vast majority of learners already hold a Master's degree (Bac + 4 to 6: 71.7%). Of the remainder, 13.3% have doctorates (Bac + 8) and 13.3% have a Bac + 3 or lower.
At the start of their training, the majority of learners are looking for work (26.5%) or are already employed (46.9%). The remaining 20% are students, self-employed or entrepreneurs.
The majority of learners stated that they had already worked in IT or new technologies at the start of their training (36%). Marketing and sales is also a preponderant sector from which future "Data Science Designer Developer" graduates come (22%). The remaining 42% of learners, however, come from increasingly diverse fields that are already very open to the practice of Data Science, but whose needs are still growing: finance, administration, research, healthcare and logistics in particular.)