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EXPERT COURSES

Analytics Engineer expert course

Bootcamp (6 months)
or
part-time (13 months)

Get a recognized diploma, support until you are hired and a flexible job that is in high demand.

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OUR NEXT ENTRIES ARE:
August 06, 2024
September 10, 2024
October 01, 2024
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Certificate delivered by University La Sorbonne

Training content​

icon 

Introduction to Python (45h)

  • Python fundamentals
  • NumPy
  • Pandas
data-viz 

Data Visualization (35h)

  • Matplotlib
  • Seaborn
  • Art of Storytelling
illu-2 

Machine Learning (35h)

  • Méthodes de classification
  • Méthodes de régression
  • Pipelines
illu-3 

Machine Learning (35h)

  • Scikit-Learn
  • Supervised learning
  • Unsupervised learning
illu-4 

Data mining and management (25h)

  • Text Mining
  • Webscraping
illu-1 

Business intelligence (30h)

  • Power Bi
  • Tableau
  • Looker Studio
illu-2 

Big Data / Database (25h)

  • SQL
  • Data Processing
  • Data Modeling
illu-2 

Object-oriented programming

  • Python
  • Data quality
  • APIs
  • Data profiling
illu-2 

Data integration / ingestion

  • Linux
  • Data ingestion
  • Git
illu-2 

Advanced SQL/NoSQL

  • SQL
  • Datensätze in SQL
  • NoSQL-Databases
illu-2 

Data Warehousing

  • Snowflake
  • General Data Warehousing
  • BigQuery
illu-2 

Streaming & resilient analytics

  • ETL with pySpark
  • Airflow
  • Data Analytics
illu-2 

Business Intelligence & ETL

  • PowerBI
  • Tableau

Throughout the curriculum, projects enable you to tackle & understand the following :

  • development of a data analytics mindset ,
  • the creation of an ETL pipeline,
certified microsoft X datascientest
The Analytics Engineer course will give you the skills you need to qualify for Microsoft’s PL-300 certification: "Analyzing data with Microsoft Power BI".

A hybrid learning format

Combining flexible learning on a platform and Masterclasses led by a Data Scientist, this mix has attracted more than 6000 alumni, and gives our training courses a completion rate of +98%!

Our teaching method is based on learning by doing:

  • Practical application: All our training modules include online exercises so that you can implement the concepts developed in the course.
  • Masterclass: For each sprint, 1 to 2 Masterclasses are organized live with a tutor to address current technologies, methods, and tools in the field of machine learning and data science.
Discover the pedagogy
Discover the pedagogy

The objectives of an Analytics Engineer

The Analytics Engineer specializes in the management,transformation, and modeling of data. This engineer delivers data sets that are understandable to all, while applying visualization best practices. It is essential for strategic data optimization, enabling optimal analysis and exploitation to solve complex problems or even develop predictive models.

Prepare and manage data:

Efficiently clean, process and manage data streams.

Develop and apply machine learning analysis:

Create and optimize models for predictive analysis.

Analyze results:

Interpretation of results and management of data projects with the creation of visualizations.

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Download program

Discover Learn, our learning platform

A user-friendly, comprehensive interface for a tailor-made learning experience. An enhanced platform and premium coaching.

Discover the platform

Our goal is to make our courses affordable and open to everyone - regardless of one's current situation. This means we do our best to offer as many options as possible

If you live in France, you can benefit from several financing options:

  • CPF: If you have already worked in France, you may have accumulated a budget allocated for training, which allows you to finance your training via your CPF account
  • Personal financing: It is possible to spread out your payment in several instalments in order to finance your training.
  • Company financing: If you are an employee, you can have your training financed by your company.
  • Pôle Emploi: If you are a job seeker and registered with Pôle Emploi, it is possible to benefit from total or partial financing via Pôle Emploi.
  • Transitions Pro: Do you want to retrain while keeping your job? You can use the system via Transitions Pro.
  • Region: If you are registered with Pôle Emploi, you can also benefit from funding from your region! Several schemes exist that allow you to finance your training.

Don’t hesitate to make an appointment with one of our advisors to find the funding that best suits you!

If you are living in Germany you have multiple ways to finance your training courses depending on your professional situation.

Employees:

  • Funding from your employer: You can check with your employer to see if there is a possibility of having your training paid for (totally or partially paying for your training).
  • Payment by installments: If you are unable to pay the entire amount at once, you may be interested in our installment plan (pay the costs over a period of up to 12 months).

Your company may also be able to benefit from the Qualifizierungschancengesetz and get funding from the state.

Unemployed, job seekers, self-employed or students:

  • Bildungsgutschein: If you are looking for work, threatened by unemployment, self-employed or even a student, you have a good chance of receiving an education voucher (Bildungsgutschein). Contact your advisor at the employment agency or the job center and check whether there is a possibility of funding your training course.
  • Self-financing: If you have no chance of receiving the education voucher, you can pay the remaining amount by bank transfer, direct debit or credit car.
  • Payment by installments: If you are unable to pay the entire amount at once, you may be interested in our installment plan (pay the costs over a period of up to 12 months).

Get more information about the process and the next steps by downloading our Bildungsgutschein guide.

The DataScientest team will help you find the best funding for your personal circumstances.

Different types of financing can be applied depending on your current situation:

  • Fundae: Thanks to our close links with companies and our high employment rate, you can subsidise our courses with Fundae.
  • Pledg: Finance our courses in up to 12 months.
  • Quotanda: Finance the course with Quotanda interest-free (+12 months).
  • Student Finance: You pay nothing until you find a job.

For further information, please check this page and book an appointment with our team.

Book an appointment

You have questions ? We have the answers!

Accordion Content

The Analytics Engineer, an booming profession theorized only in 2018, is positioned at the intersection between Data Analysts and Data Engineers, thus making them a crucial asset in data management within companies.

Specializing in data management, transformation, and modeling, this engineer delivers data sets that are understandable to all, applying best visualization practices such as version control and continuous integration.

Compared with traditional data professions, the Analytics Engineer sets himself apart by working closely with Data Analysts and Data Scientists to develop analytics solutions tailored to business needs.

Facing increasing demand for its advanced skills in code analysis and business decision-making, this role, sometimes shortened to “AE”, is essential for strategic data optimization, enabling optimal analysis and exploitation with a view to solving complex problems and developing predictive models.

Unlike the Data Analyst, who focuses primarily on data analysis, the Analytics Engineer sees his or her work centered on modeling data to facilitate access to it by end users. This approach enables users to answer the majority of their data-related questions on their own. An experienced Analytics Engineer will transform, test, deploy and document the data for which he or she is responsible.

Here’s a more detailed list of an Analytics Engineer’s tasks:

  • Data modeling and transformation: Analytics Engineers must structure, cleanse and prepare data for analysis to ensure its accuracy and reliability.
  • Data integration and pipeline development: To meet business needs, they design pipelines to extract, transform and load higher-quality data.
  • Data validation and testing: Engineers carry out unit, integration and performance tests to assess the reliability of data pipelines, they then implement validation procedures to guarantee the reliability of their data.
  • Collaboration with stakeholders: To deliver relevant and actionable data sets, they work closely with end-users and other stakeholders.
  • Data documentation: Responsible for documenting data processes, Analytics Engineers ensure the transparency and reproducibility of data transformations and workflows implemented.
  • Use of software engineering best practices:They apply practices such as modularity, code reusability, and version management, to ensure efficient and up-to-date analysis solutions.
  • Continuous improvement: Committed to continuous improvement, Analytics Engineers keep abreast of the latest technologies and trends in their market and practices.

To do their job properly, Analytics Engineers need to master key skills in programming, analysis, visualization along with general communication.

Here’s a detailed list of the skills required to be a recognized Analytics Engineer:

  • Experience in the data industry: For Analytics Engineers, experience in data-centric environments is crucial. Those wishing to become Analytics Engineers are, for the most part, Data Analysts or Data Engineers looking to specialize in data modeling.
  • Advanced SQL skills:Mastering SQL is essential for an Analytics Engineer, given that the majority of their tasks involve querying, manipulating and transforming data within databases. SQL is crucial for extracting accurate information, which prepares data for subsequent analysis.
  • Programming skills:In addition to SQL, mastery of programming languages such as R and Python is crucial. These languages are essential for visualizing data, as well as for developing predictive models and machine learning algorithms.
  • Mastery of DBT technology: Dbt (Data Build Tool) is a data transformation tool that facilitates the implementation of analytics code via SQL. It enables Analytics Engineers to create and manage data pipelines efficiently.
  • Software Engineering knowledge: It is essential for an analytics engineer to know and apply best practices in software engineering, such as modularity, code reusability, documentation, unit testing and release management. Adopting these practices not only improves code robustness, but also enables the development of more efficient data pipelines. They also facilitate change management and strengthen collaboration with other members of the development team.
  • Knowledge of BI and data engineering tools:It’s crucial for an analytics engineer to have a good grasp of data engineering and Business Intelligence (BI) tools. This includes knowledge of data warehouses such as Snowflake, Amazon Redshift and Google BigQuery, ETL tools such as AWS Glue and Talend, as well as BI platforms such as Tableau and Looker. Hands-on experience with these technologies not only increases his versatility, but also enables rapid adaptation to the business environment.
Accordion Content

In order to participate in the Machine Learning Engineer course, you should have a good knowledge of mathematics or statistics, which can be demonstrated by a bachelor’s degree, for example.
These prerequisites exist because although the training is centered on data science, and not mathematics, these are necessary for a good understanding of the logical principles of the concepts covered.Furthermore, programming is essential for the development and production of any machine learning project. For this, a certain level of programming knowledge is an advantage. Since the terminology, documentation and online resources are in English, you should speak at least a B1 level English.

After your registration on the site, we contact you for the first time for a presentation of what DataScientest is, what we can offer you but also your background and your wishes. The idea is to align your expectations from that moment with our training courses.

You can of course also make an appointment directly by clicking here ! 

Then we redirect you to a technical positioning test that we use to know what bases you are starting with . These are essentially mathematical questions dealing mainly with basic notions (L1/L2 level) in probability, statistics, analysis and algebra.

Once this test has been passed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project.

Once your project is confirmed, you go to the registration phase with our teams who will take care of initiating your Machine Learning Engineer training and setting it up with you in all its aspects.

At the end of your training you will know:

  • Prepare data, set up a data analysis strategy and master Python programming;
  • Train predictive Machine Learning models and automate the updating of these models;
  • Manipulate neural networks and implement Computer Vision or Natural Language Processing algorithms;
  • Exploit artificial intelligence models in a production context;
  • Implement a classic Machine Learning algorithm and use it on text data
  • Deploy models on cloud solutions;
  • Build Data Visualizations allowing the valuation of results.

There are no official prerequisites although we highly recommend a bachelor’s level in a scientific field. These prerequisites exist because although the training focuses on data science, and not mathematics, the latter is necessary for a proper understanding of the concepts covered.To enter the course, every one must validate a pre requisite test before hand (free of charge).

 

In order to follow the course, learners are also required to have a computer with an Internet connection and a webcam.

The training consists in a total of 600 hours of training, of which 150 hours are allocated to projects, 85% of your training takes place on a personalized coaching platform, while the remaining 15% is in the form of masterclasses, where an experienced teacher leads a course and answers all your questions. The curriculum is made up of 2 modules: Data Analyst & ETL Developer.

Au-delà de la plateforme et des masterclass, vous travaillerez ainsi sur des projets professionnalisants qui vous permettront d’être pleinement opérationnel au sortir de votre formation.

The 150 hours to be allocated to projects are broken down as follows:
– Data Analyst project: 90h; – ETL Developer project: 60h; – 

The Analytics Engineer course enables you to choose a training schedule to suit your needs: – Bootcamp format, intensive schedule of 35h per week for 5 months – Part-time format requiring involvement of 10h per week for 12 months.
Book an appointment to find out more

Assessment of results is made through the implementation of an assessment procedure to determine whether the learner has acquired the skills required for the role of Analytics Engineer

There are two aspects assessed by the pedagogical team: 

– Projects to put the learner in a professional situation

– Online practical cases to progressively apply your theoretical learning.

 

Finally, online assessments are hand-corrected by our panel of qualified teachers: everything is done to ensure that each learner can progress efficiently and at his or her own pace. At DataScientest, we’re convinced that only personalized follow-up ensures quality learning!

You will be able to, by the end of the training course: 

  • Sort, clean and process data for analysis
  • Develop Machine Learning models for predictive analysis
  • Interpret results and create dashboards
  • Master data modeling and transformation techniques.
  • Design and manage data pipelines.
  • Manage a data project

Throughout your training, and as your skills are developed, you will carry out several projects in groups, according to the breakdown of the curriculum:

Module

Project

Data Analyst

Development of a data solution.

ETL Developer

Create an ETL pipeline, from raw data recovery to modeling and visualization.




These projects can be drawn from our catalog, which includes a wide range of subjects based on technical business issues. You can also propose your own projects, as long as the data is accessible and our teaching team validates them.

This is an extremely effective way of putting theory into practice and ensuring that you apply the topics covered in class.

These projects are highly appreciated by companies, as they ensure the quality of the training and the knowledge acquired at the end of the Analytics Engineer course, since the use of soft-skills is also very present. These projects will teach you to :

  • transmit information ;
  • present and popularize your work;
  • highlighting data with interactive tools (Dashboard, Streamlit…).

In short, these projects will require a real investment, representing at least a third of your training time.

The 150 hours to be allocated to the projects that make up the curriculum can be broken down as follows:

  • Data Analyst project: 90h ;
  • ETL Developer project: 60h ;
  •  

The projects are supervised by DataScientest mentors who will be in regular contact with you to monitor your progress and provide guidance.

If you’d like to strengthen your skills, DataScientest has set up a number of expert courses and publisher certifications (AWS or Microsoft Azure) to help you deepen your knowledge and perfect your data skills.

You can also take the full Power BI training course as a 100% synchronous path if you wish to consolidate your skills and increase your chances of achieving “Microsoft Power BI Data Analyst Associate” status following the Analytics Engineer training course.

A the B2B leader in data science training, DataScientest enjoys a high level of recognition among the companies that entrust us with the data science training of their teams. This trust forges a fortiori the recognition of its diplomas.

Accordion Content

The salary of an Analytics Engineer in Europe is influenced by several key factors such as experience, skill level, and geographical location. According to Talent.com, the median annual salary for this position is around €54,000. However, this amount can vary considerably depending on experience: beginner professionals can expect to start with a salary around 43,625 € per year, while those more experienced can reach or exceed 78,000 € annually.

Demand for Analytics Engineering skills, which can fluctuate according to market needs, plays a role in determining salaries. In addition, geographical location is a significant factor: salaries tend to be higher in major cities, countries and regions with a well-developed technology sector. Finally, accumulated experience, advanced skills and certifications can also increase the earning potential of these professionals.

On your first day of training, you will be presented with a dedicated career services platform containing all the essential workshops for your job search. You can access it continuously, even after your training has finished. The Career Management Pole is entirely dedicated to you throughout your training. It is possible to book an individual appointment with one of them to support you and answer your questions about your career project.

Each month: – A full day is organized to help you optimize your job search with various topics on presentation, career change, salary negotiation and technical test practice. These topics are supplemented by other workshops to be defined according to individual needs. – You benefit from a career workshop with theintervention of an expert senior consultant. – Various topics to help in the job search are addressed: how to combat imposter syndrome, how to build a network, how to write a good CV and Data-oriented Linkedin. – Take part in anAlumni Talk. An alumni takes the floor to share his or her experience of training, job hunting and giving you tips. On the other hand, concrete actions are put in place to support you in your job search: the recruitment fair organized by DataScientest with its partner companies, organization of Webinars with expert data speakers, communication actionsto boost your visibility (CV Competition, DataDays, Project Articles published on the blog and external reference media).

Finally, you should know that a specific slack channel has been set up, for people looking for work, on which all workshop information and job offers pass. To find out about all DataScientest’s career support actions, click on this link.

According to the data managers of the biggest Fortune 500 companies, knowing how to communicate both orally and in writing is more important than mastering the company’s core business for an analytics engineer. So we’ve taken this into account in our curriculum, which also puts the emphasis on soft-skills with: – Oral defenses of the project ,which help develop these skills.
– masterclasses dedicated to project management and interpretation of results. – Masterclasses on best practices on dedicated tools. You’ll also have the opportunity to take part in CV workshops and career coaching via DataScientest’s careers managers.

Accordion Content

Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure . 

Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted among 30 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni . All the partner companies undertake to include all our students at the end of their training in their recruitment process : this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Mathilde and Morgane, our career managers are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

  • A workshop to help you write a good CV and data-oriented Linkedin
  • A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.

 

In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link .

The job
Accordion Content

The Analytics Engineer, an booming profession theorized only in 2018, is positioned at the intersection between Data Analysts and Data Engineers, thus making them a crucial asset in data management within companies.

Specializing in data management, transformation, and modeling, this engineer delivers data sets that are understandable to all, applying best visualization practices such as version control and continuous integration.

Compared with traditional data professions, the Analytics Engineer sets himself apart by working closely with Data Analysts and Data Scientists to develop analytics solutions tailored to business needs.

Facing increasing demand for its advanced skills in code analysis and business decision-making, this role, sometimes shortened to “AE”, is essential for strategic data optimization, enabling optimal analysis and exploitation with a view to solving complex problems and developing predictive models.

Unlike the Data Analyst, who focuses primarily on data analysis, the Analytics Engineer sees his or her work centered on modeling data to facilitate access to it by end users. This approach enables users to answer the majority of their data-related questions on their own. An experienced Analytics Engineer will transform, test, deploy and document the data for which he or she is responsible.

Here’s a more detailed list of an Analytics Engineer’s tasks:

  • Data modeling and transformation: Analytics Engineers must structure, cleanse and prepare data for analysis to ensure its accuracy and reliability.
  • Data integration and pipeline development: To meet business needs, they design pipelines to extract, transform and load higher-quality data.
  • Data validation and testing: Engineers carry out unit, integration and performance tests to assess the reliability of data pipelines, they then implement validation procedures to guarantee the reliability of their data.
  • Collaboration with stakeholders: To deliver relevant and actionable data sets, they work closely with end-users and other stakeholders.
  • Data documentation: Responsible for documenting data processes, Analytics Engineers ensure the transparency and reproducibility of data transformations and workflows implemented.
  • Use of software engineering best practices:They apply practices such as modularity, code reusability, and version management, to ensure efficient and up-to-date analysis solutions.
  • Continuous improvement: Committed to continuous improvement, Analytics Engineers keep abreast of the latest technologies and trends in their market and practices.

To do their job properly, Analytics Engineers need to master key skills in programming, analysis, visualization along with general communication.

Here’s a detailed list of the skills required to be a recognized Analytics Engineer:

  • Experience in the data industry: For Analytics Engineers, experience in data-centric environments is crucial. Those wishing to become Analytics Engineers are, for the most part, Data Analysts or Data Engineers looking to specialize in data modeling.
  • Advanced SQL skills:Mastering SQL is essential for an Analytics Engineer, given that the majority of their tasks involve querying, manipulating and transforming data within databases. SQL is crucial for extracting accurate information, which prepares data for subsequent analysis.
  • Programming skills:In addition to SQL, mastery of programming languages such as R and Python is crucial. These languages are essential for visualizing data, as well as for developing predictive models and machine learning algorithms.
  • Mastery of DBT technology: Dbt (Data Build Tool) is a data transformation tool that facilitates the implementation of analytics code via SQL. It enables Analytics Engineers to create and manage data pipelines efficiently.
  • Software Engineering knowledge: It is essential for an analytics engineer to know and apply best practices in software engineering, such as modularity, code reusability, documentation, unit testing and release management. Adopting these practices not only improves code robustness, but also enables the development of more efficient data pipelines. They also facilitate change management and strengthen collaboration with other members of the development team.
  • Knowledge of BI and data engineering tools:It’s crucial for an analytics engineer to have a good grasp of data engineering and Business Intelligence (BI) tools. This includes knowledge of data warehouses such as Snowflake, Amazon Redshift and Google BigQuery, ETL tools such as AWS Glue and Talend, as well as BI platforms such as Tableau and Looker. Hands-on experience with these technologies not only increases his versatility, but also enables rapid adaptation to the business environment.
Training
Accordion Content

In order to participate in the Machine Learning Engineer course, you should have a good knowledge of mathematics or statistics, which can be demonstrated by a bachelor’s degree, for example.
These prerequisites exist because although the training is centered on data science, and not mathematics, these are necessary for a good understanding of the logical principles of the concepts covered.Furthermore, programming is essential for the development and production of any machine learning project. For this, a certain level of programming knowledge is an advantage. Since the terminology, documentation and online resources are in English, you should speak at least a B1 level English.

After your registration on the site, we contact you for the first time for a presentation of what DataScientest is, what we can offer you but also your background and your wishes. The idea is to align your expectations from that moment with our training courses.

You can of course also make an appointment directly by clicking here ! 

Then we redirect you to a technical positioning test that we use to know what bases you are starting with . These are essentially mathematical questions dealing mainly with basic notions (L1/L2 level) in probability, statistics, analysis and algebra.

Once this test has been passed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project.

Once your project is confirmed, you go to the registration phase with our teams who will take care of initiating your Machine Learning Engineer training and setting it up with you in all its aspects.

At the end of your training you will know:

  • Prepare data, set up a data analysis strategy and master Python programming;
  • Train predictive Machine Learning models and automate the updating of these models;
  • Manipulate neural networks and implement Computer Vision or Natural Language Processing algorithms;
  • Exploit artificial intelligence models in a production context;
  • Implement a classic Machine Learning algorithm and use it on text data
  • Deploy models on cloud solutions;
  • Build Data Visualizations allowing the valuation of results.
The curriculum

There are no official prerequisites although we highly recommend a bachelor’s level in a scientific field. These prerequisites exist because although the training focuses on data science, and not mathematics, the latter is necessary for a proper understanding of the concepts covered.To enter the course, every one must validate a pre requisite test before hand (free of charge).

 

In order to follow the course, learners are also required to have a computer with an Internet connection and a webcam.

The training consists in a total of 600 hours of training, of which 150 hours are allocated to projects, 85% of your training takes place on a personalized coaching platform, while the remaining 15% is in the form of masterclasses, where an experienced teacher leads a course and answers all your questions. The curriculum is made up of 2 modules: Data Analyst & ETL Developer.

Au-delà de la plateforme et des masterclass, vous travaillerez ainsi sur des projets professionnalisants qui vous permettront d’être pleinement opérationnel au sortir de votre formation.

The 150 hours to be allocated to projects are broken down as follows:
– Data Analyst project: 90h; – ETL Developer project: 60h; – 

The Analytics Engineer course enables you to choose a training schedule to suit your needs: – Bootcamp format, intensive schedule of 35h per week for 5 months – Part-time format requiring involvement of 10h per week for 12 months.
Book an appointment to find out more

Assessment of results is made through the implementation of an assessment procedure to determine whether the learner has acquired the skills required for the role of Analytics Engineer

There are two aspects assessed by the pedagogical team: 

– Projects to put the learner in a professional situation

– Online practical cases to progressively apply your theoretical learning.

 

Finally, online assessments are hand-corrected by our panel of qualified teachers: everything is done to ensure that each learner can progress efficiently and at his or her own pace. At DataScientest, we’re convinced that only personalized follow-up ensures quality learning!

You will be able to, by the end of the training course: 

  • Sort, clean and process data for analysis
  • Develop Machine Learning models for predictive analysis
  • Interpret results and create dashboards
  • Master data modeling and transformation techniques.
  • Design and manage data pipelines.
  • Manage a data project

Throughout your training, and as your skills are developed, you will carry out several projects in groups, according to the breakdown of the curriculum:

Module

Project

Data Analyst

Development of a data solution.

ETL Developer

Create an ETL pipeline, from raw data recovery to modeling and visualization.




These projects can be drawn from our catalog, which includes a wide range of subjects based on technical business issues. You can also propose your own projects, as long as the data is accessible and our teaching team validates them.

This is an extremely effective way of putting theory into practice and ensuring that you apply the topics covered in class.

These projects are highly appreciated by companies, as they ensure the quality of the training and the knowledge acquired at the end of the Analytics Engineer course, since the use of soft-skills is also very present. These projects will teach you to :

  • transmit information ;
  • present and popularize your work;
  • highlighting data with interactive tools (Dashboard, Streamlit…).

In short, these projects will require a real investment, representing at least a third of your training time.

The 150 hours to be allocated to the projects that make up the curriculum can be broken down as follows:

  • Data Analyst project: 90h ;
  • ETL Developer project: 60h ;
  •  

The projects are supervised by DataScientest mentors who will be in regular contact with you to monitor your progress and provide guidance.

If you’d like to strengthen your skills, DataScientest has set up a number of expert courses and publisher certifications (AWS or Microsoft Azure) to help you deepen your knowledge and perfect your data skills.

You can also take the full Power BI training course as a 100% synchronous path if you wish to consolidate your skills and increase your chances of achieving “Microsoft Power BI Data Analyst Associate” status following the Analytics Engineer training course.

A the B2B leader in data science training, DataScientest enjoys a high level of recognition among the companies that entrust us with the data science training of their teams. This trust forges a fortiori the recognition of its diplomas.

The career
Accordion Content

The salary of an Analytics Engineer in Europe is influenced by several key factors such as experience, skill level, and geographical location. According to Talent.com, the median annual salary for this position is around €54,000. However, this amount can vary considerably depending on experience: beginner professionals can expect to start with a salary around 43,625 € per year, while those more experienced can reach or exceed 78,000 € annually.

Demand for Analytics Engineering skills, which can fluctuate according to market needs, plays a role in determining salaries. In addition, geographical location is a significant factor: salaries tend to be higher in major cities, countries and regions with a well-developed technology sector. Finally, accumulated experience, advanced skills and certifications can also increase the earning potential of these professionals.

On your first day of training, you will be presented with a dedicated career services platform containing all the essential workshops for your job search. You can access it continuously, even after your training has finished. The Career Management Pole is entirely dedicated to you throughout your training. It is possible to book an individual appointment with one of them to support you and answer your questions about your career project.

Each month: – A full day is organized to help you optimize your job search with various topics on presentation, career change, salary negotiation and technical test practice. These topics are supplemented by other workshops to be defined according to individual needs. – You benefit from a career workshop with theintervention of an expert senior consultant. – Various topics to help in the job search are addressed: how to combat imposter syndrome, how to build a network, how to write a good CV and Data-oriented Linkedin. – Take part in anAlumni Talk. An alumni takes the floor to share his or her experience of training, job hunting and giving you tips. On the other hand, concrete actions are put in place to support you in your job search: the recruitment fair organized by DataScientest with its partner companies, organization of Webinars with expert data speakers, communication actionsto boost your visibility (CV Competition, DataDays, Project Articles published on the blog and external reference media).

Finally, you should know that a specific slack channel has been set up, for people looking for work, on which all workshop information and job offers pass. To find out about all DataScientest’s career support actions, click on this link.

According to the data managers of the biggest Fortune 500 companies, knowing how to communicate both orally and in writing is more important than mastering the company’s core business for an analytics engineer. So we’ve taken this into account in our curriculum, which also puts the emphasis on soft-skills with: – Oral defenses of the project ,which help develop these skills.
– masterclasses dedicated to project management and interpretation of results. – Masterclasses on best practices on dedicated tools. You’ll also have the opportunity to take part in CV workshops and career coaching via DataScientest’s careers managers.

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Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure . 

Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted among 30 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni . All the partner companies undertake to include all our students at the end of their training in their recruitment process : this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Mathilde and Morgane, our career managers are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

  • A workshop to help you write a good CV and data-oriented Linkedin
  • A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.

 

In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link .

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