Data Science Certification
Train in Data and obtain your state-recognised "Data Science Designer-Developer" certificate
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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.
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 is 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 field of data.
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:
- The Data Essentials course validates the 5-year Block 6: Data Management Project Management
- The Data Fullstack training course validates the entire certification (the 6 blocks) "Data Science Designer - Developer" valid for life
- The Data Lead training course validates Block 1, which is valid for 5 years: Building and feeding a data management infrastructure
- The Cybersecurity Essentials course validates the 5-year Block 1: Building and powering a data management infrastructure
- General terms of acquisition of the certification: the certification is valid for life. In case of partial validation of the blocks, the validity period of each block of skills is 5 years. The validation of the totality of the 6 blocks of competences is compulsory to obtain the certification. The partial validation of a block is not possible. Candidates who have only validated some of the 6 blocks of competences of the "Data Science Designer and Developer" reference system are given a certificate of competence attesting to the partial validation of the professional certification, and naming the blocks concerned.
The certification is acquired by capitalisation of the blocks of competences and by equivalence. Equivalences to obtain this certification are also possible, the complete list of organisations delivering equivalent training can be found on this page, under the heading "Links with other professional certifications, certifications, or habilitations".
VAE procedure
If a person considers that he/she has the competences described in the blocks below, he/she can ask to go through the VAE procedure (Validation des Acquis par l'Expérience) to obtain the (full or partial) certificate of "Designer-Developer in Data Science" from Jedha. The complete information about this procedure is described on this page.
VAE procedure
If a person considers that he/she has the competences described in the blocks below, he/she can ask to go through the VAE procedure (Validation des Acquis par l'Expérience) to obtain the (full or partial) certificate of "Designer-Developer in Data Science" from Jedha. The complete information about this procedure is described on this page.
- Design a robust and appropriate data architecture by creating data lakes and data warehouses to meet the storage, usage, security and protection needs of the organization
- 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 different sources (Web, internal software such as Sage / Excel or external software such as Google Analytics) via programming libraries such as Scrapy or Beautifulsoup to feed the Data Lake in order to refine the results of future analyses.
- Clean and organize data in the Data Warehouse by writing extraction, transformation and loading (ETL) processes to make the data available and understandable to other business teams.
- Evaluation: A case study on real data
- Evaluation topic: Construction of a Cloud infrastructure hosting Big Data (web data collection, data integration in a Data Lake, cleaning and loading of data in a database such as AWS Redshift by parallelized processing if necessary via the construction of an ETL process).
- Process databases through descriptive and inferential statistical analysis 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 identify relationships between several variables and establish statistical links between them.
- Optimize statistical analysis through parallelized processing using tools such as Spark to accelerate computer processing time in order to analyze massive volumes of data (Big Data)
- Present the result of a statistical analysis of structured data, massive or not, thanks to programming libraries such as Plotly or Matplotlib to synthesize this result in front of a lay public in order to facilitate the decision-making and to support their operational declensions.
- 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 such as Scikit-Learn to encode, normalize and slice data in order to make it interpretable by a Machine Learning algorithm
- Perform predictive analysis on a structured dataset using adapted supervised machine learning algorithms to automate tasks related to the results of the predictions 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 in order to improve them and demonstrate their usefulness to business departments, in relation to the processes already established in the organization.
- Evaluation: three practical case studies drawn from real cases
- Evaluation topic:
- Optimization of marketing processes for prospect qualification through supervised learning algorithms
- Optimization of supervised machine learning algorithms on unbalanced databases
- Localization of geographical 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 in order to make them interpretable 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 build 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 the sentiment of a user towards 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 facilitate the production of artificial intelligence projects 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
- Evaluation topic: Web dashboard, construction and production of an artificial intelligence web application
- Translate business challenges into mathematical/data issues by understanding the specific needs of 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 a specification, a retroplanning and a budget in order to defend and detail a data project that meets the needs of the organization
- Manage a data analysis and management project (descriptive statistical analysis, Machine Learning, Deep Learning, Big Data or not) thanks to the elaboration of adapted indicators and dashboards, in order to follow up and evaluate the action, as well as the operational declination of its results
- Transmit the information extraction and data analysis process to the business units by popularizing it in order to support the implementation of a strategy and future actions.
- Lead a data management project from conception to solution implementation, 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 organisation in all activities related to the project
- The evaluation: a data project designed from scratch.
- Assessment topic: free. Learners can prepare the data project of their choice. This can be personal, developed by the candidate in the context 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:
- 99% certification success rate.
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 salary for the target occupation is €45,230 gross/year or equivalent.
The impact of Jedha on the careers of our learners
Impact of Fullstack training on the career of certified employees
55%
Expected impact
18%
Greater than expected impact
26%
No impact
Did they completely reorient themselves after the training?
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, with 18% stating that this impact was greater than they had expected.
53.6% of these holders stated 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 organisation where the learner currently works (17%)
53.6% of these holders stated 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 organisation where the learner currently works (17%)
Objectives of learners at the beginning of the course
78%
Find a job in Data
16%
Going freelance
12%
Internal career development within the company
12%
Setting up a Tech business
2%
Other
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 (Bac + 4 to 6: 71.7%). Among the others, 13.3% are doctors (Bac + 8) and 13.3% have a Bac + 3 or lower.
At the beginning of the course, the majority of learners are looking for work (26.5%) or are already employed (46.9%). The remaining 20% were students, self-employed and entrepreneurs.
The majority of learners stated that they had already worked in IT or new technologies at the beginning of their training (36%). Marketing and sales is also a dominant sector from which future Data Science Designer and 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, health and logistics in particular.)
At the beginning of the course, the majority of learners are looking for work (26.5%) or are already employed (46.9%). The remaining 20% were students, self-employed and entrepreneurs.
The majority of learners stated that they had already worked in IT or new technologies at the beginning of their training (36%). Marketing and sales is also a dominant sector from which future Data Science Designer and 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, health and logistics in particular.)