Data Science Online Training: Whether you’re starting out on a new learning path or deciding to improve your skills, you need to choose the teaching method that seems best suited to your needs. Whether you’re learning a new language or Data Science (not so far from a new language to explore), choosing the right pedagogical method is crucial! One of the elements to be defined is the “route of administration” of this training. Whether distance or face-to-face, the diversity of players involved makes it difficult to decide. Let’s take a quick look at the advantages and disadvantages of each of these schools.
Data science training in the classroom or Data Science Online Training: Is the traditional way out of fashion?
To follow a classroom-based data science course is to evolve in an environment animated by shared intellectual emulation. The healthy competition that develops between students pushes everyone to give their best, and also enables mutual support and knowledge sharing.
Easy access to a teacher who can answer questions ensures that every learner has a clear understanding. That’s why this type of course is taken in its entirety, with a diploma obtained at the end in almost all cases.
Nevertheless, there are a number of drawbacks to traditional learning, particularly when it comes to data science:
There’s always the question of upgrading PCs: you need a good computational load and RAM memory,
There are also issues of administrative rights, software installation and libraries…
In addition to the problems of layout, there’s the issue of flexibility.
Face to face courses impose precise time slots, and sometimes time-consuming travel that is often incompatible with the exercise of any other function.
There is also no possibility of taking a break from training, a handicap for some in managing their projects.
The COVID-19 crisis recently demonstrated an inherent weakness of this system: A face ti to face presence cannot always be guaranteed, even if videoconferencing courses have been able – more or less successfully – to replace physical classes. Lastly, while emulation is undoubted thanks to the physics class principle, it is also responsible for an inequality of pace: either that given by the leading pack in the class or that given by those who are struggling.
Data Science Online Training: The wrong idea?
To compensate for the rigidity of face-to-face courses, 100% online courses have been developed over the last few years. They have the advantage of being highly flexible in terms of timetable, pace and location.
Everything is left to the student’s choice.
Unfortunately, this new freedom brings with it a number of pitfalls
These courses, which are often less fast-paced, generally take longer to complete, as the student manages his or her own learning schedule without always making it a priority.
What’s more, these entirely Data Science online training courses often lose out on quality and follow-up, as they lack a real teacher to ensure that the class is understood.
Course material is not always adapted to the student’s needs, and questions are too often left unanswered, or the answers they receive are cruelly lacking in personalization.
Added to this is a high degree of automation, in particular standardized tests which are automatically corrected, with little benefit to the student.
These difficulties encountered by learners explain why the completion rate for online courses is significantly lower than for face-to-face courses. According to Le Figaro Étudiant, only 10% of students complete an online Mooc course…
These difficulties encountered by learners explain why the completion rate for online courses is significantly lower than for face-to-face courses.
According to Le Figaro Étudiant, only 10% of students complete an online Mooc course…
Is hybrid learning the best solution for data sciences?
To meet the new challenges of learning, DataScientest has set up a hybrid training program.💡
For the first time in data science, the teaching provided allows students to benefit from the advantages of both methods, while minimizing their disadvantages.
Personalized follow-up is ensured by the creation of cohorts, which serve as classes and for which one of our teachers is responsible. This individualized follow-up means that each learner enjoys the advantages of a teacher who listens, who can be questioned at any time, and who will boost him or her at the slightest slackening.
What’s more, concrete skills are developed thanks to a practical data project that punctuates the course, with deliverables due at regular intervals: skills don’t remain theoretical, but are put into practice.
Last but not least, all exams are corrected by hand by our teachers, with individual feedback for each student.
What’s more, the 100% full Saas platform means you can enjoy all the advantages of distance learning. Aside from the learning schedule specific to each class (launch and end of sprint accompanied by coaching sessions), our training courses offer the flexibility of distance learning.
Finally, as everyone has their own projects and does not have the same amount of time at their disposal, our data science training courses are available in continuous or boot camp format. Learners can then decide whether to devote themselves to the course full-time or less intensively.
In short, this is the course that combines the advantages of both methods without the disadvantages: the completion rate among our 1,500 alumni is 100%. It’s a method that’s particularly well-suited to data science, and it’s already bearing fruit!
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