Considered the “sexiest job of the 21st century” according to the Harvard Business School, Data Scientist offers great career prospects. Have you always wondered how to become a Data Scientist? We show you the skills you need to train!
Let's start with the mathematics first:
It is necessary to master concepts such as statistics, algebra and probability.
Indeed, the mastery of statistics is essential. The tools mastered through this discipline make sense when used in data sciences. In this sense, the verification of hypotheses, for example, made possible by statistical science, allows us to reach concrete conclusions.
Thus, the mastery of concepts such as the properties of variance, error calculations without forgetting the linear regression model and the Theories of Estimation are essentials to be a good Data Scientist.
To become a Data Scientist, it is necessary to master algebra in its globality. It allows an efficient management of the collected data. Whether it is linear or bi-linear, algebra allows the apprehension in a concrete way of the spaces where the data is processed.
Without the mastery of these applications such as matrix transpositions and decompositions or systems of equations, understanding the concepts covered in the course can be difficult. For example, the knowledge of vector spaces and scalar products allows the visualization of the spaces where Machine Learning models run.
Finally, probability tools remain essential to become a Data Scientist. We can mention for example the different laws (uniform, normal, binomial, fish), or the concepts of conditional probability …
All these elements will allow you to take the first step towards becoming a Data Scientist !
Let's now discuss the second part related to programming:
To become a Data Scientist, it is necessary to be introduced to programming tools such as Python, a language of the main programming languages. However, don’t worry, some Data Scientist trainings take these languages with you since they include them in their courses!
In addition to this programming prerequisite, it is also desirable to have some knowledge in Machine Learning for your training. Of course it is not a question of knowing with technicality the functioning of decision tree forests or k-means, but rather of distinguishing the difference between them and their uses according to different situations. These notions are also included in on-line professional training courses.
Finally, beyond these “academic” prerequisites, curiosity is also valuable since it will push you to improve your models. Good communication skills are also valuable since you must be able to communicate and explain your work and your added value.
It should be remembered that some people do not necessarily have the academic prerequisites to apply for one of our courses. However, it is possible to be eligible for the training action. Therefore, I invite you to make an appointment with one of our consultants to answer your questions precisely and to accompany you in the best way.