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CourseDataScientist

“Data analysis: integration, exploitation, visualisation & industrialisation” certification.

Registered in the Specific Register under number: RS2858.
Specialised Training Codes (NSF): 326m: computer science, information processing; 326t: programming, software installation.
Formacode: 31067 programming analysis

 

 

At a glance

  • Duration

    798 hours.

  • Profile / admission level

    Jobseekers with advanced scientific training (minimum level: 3 years post-bac with good command of mathematics and statistics, 5 to 8 years post-bac studies in science, physics, biology).

  • Diploma / leaving level

    Data Scientist Certification

  • Admission requirements

    3-stage recruitment:

    – skills assessment test
    – professional motivation interview
    – recruitment interview by the company welcoming the trainee.

  • Cost of course

    Covered by the French Employment Agency (Pôle Emploi).

  • Sequence

    518 hours at the training centre and 280 hours in the workplace.

Presentation

Take the Data Science Specialist course

The Data Scientist course enables jobseekers with advanced scientific training to upgrade their experience and training in order to retrain in the field of digital technology, and more specifically in the data analysis sector.

 

Thanks to data analysis, companies and organisations can exploit, manage, analyse and visualise their data assets

 

Next training session : 18th October 2021

 

Programme

The Data Scientist course prepares trainees for the “Data Analysis: integration, exploitation, visualisation & industrialisation” certification, which validates the four areas of competence needed to implement a data analysis project:

 

  • Data integration
  • Data exploitation
  • Data visualisation
  • Data industrialisation

Detailed programme

Data integration (147 hours)

Introduction to data science (7 hours)

  • Understanding the challenges of corporate data management
  • Being familiar with the specific features of the data analysis market
  • Understanding the specific characteristics of corporate data projects

 

SQL programming (35 hours)

  • Learning the operating principles of relational databases
  • Mastering the programming of complex SQL queries

 

Data processing tools (14 hours)

  • Mastering data models for future processing
  • Learning how to prepare data for processing
  • Knowing how to extract and transform data

Object-oriented programming (28 hours)

  • Understanding object-oriented programming concepts
  • Knowing how to program an application in an object-oriented language
  • Being familiar with, and understanding, the recent technologies used to create Web APIs (such as Spring Boot)
  • Being able to conduct a development project from start to finish
  • Being fully acquainted with the factors that impact the performance of applications

 

Advanced databases (open distance learning*, 35 hours)

  • Relational algebra
  • NoSQL databases

 

“Data integration” project (28 hours)

 

Exploiting and analysing data (147 hours)

Python for data science (21 hours)

  • Learning how to program a simple Python application
  • Knowing how to implement script, procedure and object-oriented programming in Python
  • Being fully acquainted with the data structures that are specific to the language

 

Big Data statistics (28 hours)

  • Being familiar with the statistical models used in big data
  • Understanding and mastering the algorithmic manipulations associated with these models
  • Being capable of selecting the statistical model best suited to the needs of a big data project
  • Being capable of conducting a project for the implementation of statistics for big data

 

Automatic learning (28 hours)

  • Understanding the theoretical principles of machine learning, optimization and anomaly detection
  • Being capable of selecting an algorithm to suit data processing requirements
  • Knowing how to develop and configure your own data analysis algorithms

 

Advanced data analysis (open distance learning*, 35 hours)

  • Choosing a machine-learning algorithm
  • Training a linear predictive model
  • Exploring data with unsupervised algorithms

 

“Exploiting and analysing data” project (35 hours)

 

Data visualisation (126 hours)

Web technologies (35 hours)

  • Understanding the basics requirements to build a web application
  • Understanding recent technologies for web application creation (such as Angular)
  • Being capable of managing a web project from start to finish

 

Data visualisation (35 hours)

  • Being familiar with the specific characteristics of big data visualisation tools
  • Learning how to use a representative tool for big data visualisation
  • Knowing how to implement a data story-telling approach
  • Being capable of conducting a project to implement visualisation
  • Being capable of using data and visualisation in a convincing way

 

Power BI (21 hours)

  • Producing reports that highlight the relevant indicators

 

“Data visualisation” project (35 hours)

 

Industrialising data processing (77 hours)

Information technology and privacy (14 hours)

  • Being familiar with the principles of private data protection
  • Learning how to implement a data protection policy for data processing applications

 

Big data project management (21 hours)

  • Learning about data project management tools
  • Being capable of choosing the right tool for each type of project

 

IT project management (open distance learning*, 7 hours)

  • Organising an agile team
  • Planning an agile project
  • Choosing the project management tools best suited to agile principles

 

Integrated data models and interactive solutions for data analysis (35 hours)

  • Designing the functionalities of an application using a data analysis platform (virtual assistants, ChatBot, e-mail analysis tool)
  • Being able to design data analysis software architecture with integrated platforms

Job search techniques (14 hours)
Internship in a corporate environment (280 hours)

 

* open distance learning.

Educational tools

  • Alternating theory, practical work and study projects.

 

  • Coaching: a “job search techniques” (TRE) module allows students to develop their professional skills and to prepare themselves to use the skills acquired during the course. A “help with finding an internship” module is also provided, during which learners will be assisted in their search for assignments in the corporate world. The modalities will be specified by the training officer during the first session of the module.

92%

satisfied customers

65

professionals trained since 2018

100%

pass rate for certification

Key figures

65 professionals trained since 2018

92% satisfied customers*

100% pass rate for certification**

* Out of a panel of 9 surveys conducted over 2020-2021 / ** Out of a panel of 54 Data Scientist trainees registered for ICAM’Pro certification since 2018

 

21 digital technology trainees in 2020-2021

5% average drop-out rate*

90% response rate to surveys**

*Out of a panel of 21 trainees over 2020-2021 / **Out of a panel of 9 surveys answered out of 10 surveys sent out over 2020-2021

 

3 IT laboratories

200m2 devoted to digital technology training

15 experts

Profile

This course is aimed at jobseekers with an advanced scientific background.

 

Prerequisites

3 years post-bac studies with a good knowledge of mathematics and statistics and 5 to 8 years post-bac studies in science, physics, biology.

Skills required in mathematics, statistics, logic, databases.

Cost and funding

Admission

3-stage recruitment:

  1. Skills assessment test
  2. Professional motivation interview
  3. Recruitment interview by the company welcoming the trainee

 

Funding

This course is paid for by the French Employment Agency (Pôle Emploi) and the local authorities. We invite you to contact your Pôle Emploi advisor in order to compile your file.

I’m more than satisfied with this training. And I’m full of praise for the follow-up and flexibility shown by the whole teaching team during the training, especially when it came to adapting to unexpected events. We felt very privileged.

Alexandre Najjar - Data Scientist

The course was beyond rewarding, both in terms of the data and the contacts made.

Othmene Benazieb - Data Scientist

As far as I'm concerned, this course has been fantastic at many levels.

Bounphathay Chanthasay - Data Scientist

Contact

We're here to guide you

Our entire team is at your disposal to discuss and answer your questions.

Icam, site de Strasbourg-Europe
Espace Européen de l’Entreprise
2, Rue de Madrid
67300 SCHILTIGHEIM
CS 20013
67012 Strasbourg CEDEX

Switchboard: +33 (0)3 90 40 09 63
Admissions: +33 (0)3 90 40 29 90

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