In order to be properly trained, an aspiring Data Scientist must have a good knowledge of Python and SQL. But why these two languages in particular?
Python is the fastest growing programming language, it has a wide variety of libraries useful in machine learning, data analysis, dataviz, API integrations … Moreover, it is one of the easiest computer languages to learn.
As far as SQL is concerned, it allows you to better understand, explore and use the data collected by your company. It is also the reference language for database management, both in terms of popularity and efficiency.
That’s why our courses focus on learning and improving these two tools. Learning on R is also possible.
To claim the title of Data Scientist, you must meet certain prerequisites.
Required to work with numbers and large amounts of information, a data scientist must have excellent analytical skills and a solid foundation in statistics. Those with a master’s or doctoral degree in math and statistics or computer science are most likely to shine in this field.
Proficiency in analytical tools such as SAS, R or Python is also important.
For this purpose, university courses have been available for some years. But also, as the need to increase skills has become a performance issue for many sectors of activity, courses on platforms such as DataScientest allow to acquire the essential skills required.
These basic prerequisites allow the Data Scientist to develop a technical approach to better understand, apprehend and manage Big Data.
The competence of data in its understanding as well as in its exploitation and processing by the company is now sought after in all types of sectors, for a wide variety of missions.
Data Scientists can work in large groups, for example in the banking, insurance and finance sectors, in auditing and consulting services, via large conventional industrial groups or in the defense sector to predict the behavior of terrorists for example.
They will also be strongly solicited by startups or new data processing software.
Data centers, Internet service providers, hosting companies or infrastructure manufacturers are all potential recruiters following a course in data science.
According to Glassdoor, in 2020 a data scientist earns an annual salary of over €44k. This salary varies greatly depending on experience. For the most senior profiles, it is around 55k€.
The duration of the course is 280 hours excluding projects or 400 hours including projects.
This takes place in the form of 85% of the course in e-learning (self-training) on our platform and 15% in coaching sessions lasting from 2 to 3.5 hours, which punctuate each training sprint. These sessions, led by our data scientists, take place approximately every 30 hours of the course.
There are two different formats: bootcamp or intensive course, and continuous training.
Bootcamp format: the course lasts 11 weeks and 2 days at a rate of 37 hours per week
Continuous training format: the course will last 9 months and will involve 6 hours of work per week, excluding the project.
To carry out his work, the data analyst must have specific skills, particularly in computer engineering. To exploit the raw data available to the company, he/she must master specific data processing tools such as Hadoop or Spark. A mastery of computer language is crucial to make the data speak, to transform it into insights.
The Data Analyst will also use various statistical tools and methods to identify trends that may affect recommendations on strategies to adopt. Marketing skills will be necessary to enable him/her to advise business leaders in this field. Rigor is essential to be able to correctly process the large amount of data available.
The job of Data Analyst is obviously not fixed and will follow the speed of evolution of Data Science. The digital explosion generates an ever-increasing amount of data to be processed by companies who must ensure the proper management of this flow of information and put it to good use in their business.
Data analytics offers job opportunities in an increasingly wide range of industries. Large financial companies, commerciales, marketing, industrielles, médicales en sont quelques exemples.
According to Glassdoor, in 2020 a data analyst will earn an annual salary of more than 74k$. This salary varies greatly depending on experience. For the most senior profiles, it is around 80k$.
The Data analyst course, which lasts 290 hours in total (220 hours without a project), is also divided into these two formats:
Bootcamp format: 11 weeks and 2 days at a rate of 25 hours per week of involvement.
Continuous course format: The course will be spread over a period of 6 months, at a rate of 6 hours per week of involvement.
The Data Engineer designs systems that enable the processing of large volumes of data and their exploitation by Data Analysts and Scientists. He/she must ensure that the data pipelines deployed are secure and clear enough to be analyzed by Data Analysts and then transformed by Data Scientists who will apply algorithms to them.
The preferred sectors of activity for data engineers are the same as for data scientists. This ranges from large industrial groups (banks, insurance companies, etc.) to data centers, including startups and data processing software publishers.
According Glassdoor, in 2020 a data engineer will earn an annual salary of more than 43k€. This salary varies greatly depending on experience. For the most senior profiles, it is around 50k€.
The duration of the course is 280 hours excluding projects, i.e. 400 hours including projects.
This takes place in the form of 85% of the course in e-learning (self-training) on our platform and 15% in coaching sessions, the duration of which varies from 2 hours to 3.5 hours and which punctuate each of the training sprints. These sessions, led by our data scientists, take place approximately every 30 hours of the course.
It is therefore important to distinguish between two formats: the bootcamp or intensive course and the continuing education course.
Bootcamp format: the course lasts 11 weeks and 2 days, 37 hours per week.
Continuous training format: the course will last 9 months and will involve 6 hours of work per week, excluding the project.
The prerequisites are different depending on the chosen course:
Our bootcamps are divided into several modules that are evaluated by a certification exam. Thus, each module is certified individually.
A certification from the University Paris 1 Panthéon Sorbonne will be delivered to you at the end of the DataScientest course for Data Analyst, Data Scientist and Data Engineer.
In addition to the course dates communicated on our site, departures in courses are regularly made for intra-company cohorts. To start a cohort, the minimum number of employees is 10. The associated pricing is studied on a case-by-case basis, on request.
Throughout the course, whether the chosen format is bootcamp or continuing education, live chat support is available every working day from 9am to 7pm. Our data scientists are available to answer your technical or pedagogical questions.
The Cohort Manager and the Daniel team are the first to detect any difficulties, they check the connection times of the beneficiaries, identify any learning difficulties. If a learner is in difficulty, the Cohort Manager then triggers the remediation process involving the entire monitoring team.
The remediation process is divided into 3 stages:
The beneficiary will be proposed during an interview with the monitoring officer various solutions according to the problem, the monitoring officer will activate all possible levers to help the beneficiary to remedy this situation and allow him to devote himself to his course.
If the learner has a drop in motivation that is neither linked to learning difficulties nor to a particular personal situation, an interview with the Career Manager will be proposed. During this interview, the Career Manager will identify the reasons for this loss of motivation and will try to re-motivate the beneficiary by focusing on the job prospects at the end of the course. Following the application of a solution, the follow-up team measures 2 weeks after the results of their intervention with the beneficiary in a dropout situation.
Our team, which is responsible for the creation of content and the correction of exams, masters all the ins and outs of the course in order to be able to answer your questions as accurately as possible.
On the other hand, the coaching sessions that punctuate each of the training sprints are also an excellent way to get a more global overview of the course. Your cohort leader will be available to answer all your questions and ensure the most personalized follow-up possible.
Finally, cohort operation ensures an emulation similar to that of a classroom and it is very important that the users of each cohort progress together along the way.
These different building blocks now ensure an average course completion rate of 100%.
Before dealing with this subject, 2 types of recognition should be determined. Informally, DataScientest courses and certifications are very widely recognized by influential players in the data world, at least in France. Indeed, the thirty or so groups who have benefited from our courses are, for the overwhelming majority of them, from the CAC 40, and the expertise of our content is now well known.
In addition, our diplomas are recognized by the University of Paris I, Panthéon Sorbonne. Indeed, after a thorough audit of our content, tests, and certification process, the prestigious university has deemed DataScient to be eligible for a Paris 1 Panthéon Sorbonne certification. This particular certification is available upon request and will generate a slight additional cost.
Finally, we have applied to the French Ministry of National Education and Competence for our diplomas to be recognized by the State (and at the same time, eligible for the CPF) . Given the confinement and the recent reform of education, the deadlines have been considerably extended, but we are confident that we will obtain this recognition before the end of 2020.
Depending on your professional situation, several options are available to you.
To help you see more clearly, we have listed the different possibilities in this article.
If you are a job seeker, you can actually apply for funding from your advisor by mobilizing the AIF (Aide Individuelle à la Course) scheme. Find our courses and all our sessions directly on your personal Pôle Emploi space and request a quote directly online.
Your quote will be processed by our teams, and your advisor will be notified as soon as it is sent. Concerning financing, your advisor will decide whether or not to grant you financing, based on several criteria including your motivation and the suitability of the course with your professional project. Our teams are at your disposal to help you in these steps.
Starting from the observation that there was a lack of a purely B2B solution for data science courses, we created DataScientest more than 4 years ago.
Very quickly, we chose a hybrid format: 90% remote and 10% face-to-face.
The course takes place on a secure platform and is complemented by support, face-to-face coaching sessions and a big data project.
If most courses on the Internet are rather a combination of video courses and quizzes, DataScientest has bet on a device at the opposite of this method.
Our active pedagogy revolves around our platform, which provides the learner with a ready-to-encode environment requiring no installation.
This technology is made possible thanks to the hosting of our GPU and cluster CPUs in AWS servers and allows us to deliver a learning-by-doing course notebook, the theory being based on the exercises that the learner will be asked to solve.
The curricula are divided into sprints, themselves composed of modules.
For example, most Python curricula start with Sprint 1 Introduction to Python which is composed of the following 4 modules: “Introduction to Python” “Numpy for Data Science”// “Pandas for Data Science”// “Introduction to Scikit learn”.
Each sprint is closed by an unlocked evaluation after the validation of all the modules composing it. This will be done directly on the platform and timed.
The correction will be done, by hand, by our data scientists. Far from an automated and impersonal correction, they will take into account the quality of the reasoning, the comments added to the codes as well as the time management (copy historized every 5 minutes.)
A real cornerstone of our course, the Big Data project is used for courses lasting more than 6 months. It will be carried out by bi or tri nome and its selection will lead to a fully dedicated coaching session. Intended to be put into production at the end of the course, it will be carried out with the company’s data to which we do not, of course, have access.
The project will therefore have a double advantage: Not only will it provide the company with a real POC, but it will also be the best motivation vector for the learners who will immediately apply the theoretical notions acquired on the platform.
The project is not imposed, it is chosen and then defended by the users. It is therefore a vector for promoting intra or entrepreneurship depending on the context. Projects are then chosen according to a selection grid that takes into account their scientific viability, access to data and the interest of other participants and sponsors in the chosen issue. Indeed, an interesting and well conducted project can be put into production as soon as the course is over.
Every week, exhaustive reports with all types of quantitative indicators (hours, exercises, certifications) are sent to HR and business line managers.
Every 5 to 6 weeks, the cohort director on the DataScientest side of the group gets in touch with his contacts on the group side in order to provide them with individual follow-up information as well as the progress of each of the group’s projects.
Our partnership with the fume cupboard is built around short certifications. Indeed, after an audit of the platform and the content of our modules, the prestigious university decided to grant us an over-certification. Concretely, following the data scientist certification obtained after the assessments, these are sent to the university, which will then serve a paper diploma at a certain cost that will remain unchanged regardless of the number of certifications per person.
The curricula are built in collaboration with the groups. Indeed, they will depend on a multitude of criteria such as the needs of the group, the skills of the learners or the strategic choices made by the group (language, bookshops etc…).
Firstly, we offer a business-oriented content with a theoretical skills development associated with practical business use cases. While DataCamp is a platform that has been designed for students and adapted for business, the DataScientest platform has been designed for companies and to increase the skills of employees in Data Science. In addition, our architecture is similar to the architecture of a Data Lab.
As far as our platform is concerned, our content is available in both English and French.
We provide live chat support (days and working hours) supported by the professors who created our courses. This support allows us to ensure 100% completion of our courses!
In order to evaluate users, we have set up certification exams. These certifications are delivered by the University Paris La Sorbonne. As for the exams, they have a real value on the market because our platform is used as a recruitment tool by large groups such as Allianz or BCG.
The e-learning courses are delivered on our full Saas secure platform.
The chosen format is the Jupyter notebook, which means that the course does not require any prior installation. This means that you can start coding as soon as you receive your login and password.
If you have any questions, live chat support will be available via Slack to answer all your questions about the course.
If a technical problem occurs during your course, do not hesitate to contact firstname.lastname@example.org who will try to answer your questions as soon as possible.