Training: Data Science Professions, A Sure Bet On The Job Market
DataScientest offers a catalog of data science training courses, including a data analyst course to learn how to process data and extract information from it to meet the challenges of companies.
Data science training, profiles sought by companies
Businesses around data continue to develop with the rise of digital technologies. “Data collection and management have become strategic economic issues for many companies,” confirms Aurélia Fellous, growth product manager at DataScientest. In recent years, they have realized their potential in terms of business and optimizing marketing and production strategies. These practices have spawned a whole new sector and new jobs: data science. As this new branch is at the heart of the data processing chain, this explains the increase in demand for skilled profiles in this field. “
|What is Data Science?|
Most industries are currently looking for experts to exploit this new source of information. DataScientest, which has studied the needs of recruiters in data science, offers specialized training to become a data analyst, data scientist, data engineer, or follow a course in data management. They are aimed at professionals wishing to acquire data skills or those who wish to go further, for example to create machine learning models. The profiles are very varied and come from all types of sectors of activity (aeronautics, bancassurance, shareholders, journalism, etc.). “It is essential to clearly define each profession in order to better understand the current expectations of companies and thus align the training that one wishes to follow and the hiring opportunities”, adds Aurélia Fellous.
3 paths to become a data science expert, including a Data Analyst course
Data science revolves around 3 major professions: data analyst, data scientist and data engineer. The data analyst has a central role: he processes the data to extract information that will make it possible to respond to specific challenges. For his part, the data scientist solves his business problems through data analysis and the implementation of models (Machine and Deep Learning). Responsible for the data infrastructure, the data engineer develops and implements the data collection, organization, storage and modeling processes. For less scientific profiles, a data management course is also offered to master the issues related to data science and acquire basic programming concepts.
The missions of the data analyst
Specializing in data analysis and their use, the data analyst has different missions:
determine the data necessary for the analyzes of future users,
perform data cleansing to uncover trends and actionable insights,
control the quality of the data available to their organization,
highlight trends while identifying new opportunities,
create reports on his observations to communicate them to his company,
participate in raising the awareness of the various teams to the world of data.
Data analyst skills
Mastering programming languages such as Python, but also R and SAS, is one of the skills required of data analysts. “He must also be able to carry out dataviz, make the data speak, create dashboards, express needs in relation to observed trends. He operates independently on the exploration and highlighting of trends or figures from data, thanks to dashboards and visual productions that he can communicate to the rest of the company “, explains the growth product manager. The average salary of a data analyst, according to a study carried out by DataScientest among chief data officers of around 30 CAC 40 companies, is between 35,000 and 60,000 euros per year.
To follow the data analyst training, the next session of which is scheduled for April 6, you must have a baccalaureate with an appetite for mathematics. A test makes it possible to check the level of the candidates and to direct them towards the training which corresponds to them best. The diploma obtained is issued with a training certificate from the University of Paris la Sorbonne. Each training is eligible for the CPF.
Data science training offered by DataScientest, including training that provides skills to become a data analyst, are presented in a hybrid and flexible format. It allows you to benefit from both face-to-face masterclasses, with the support of a data scientist trainer in videoconferencing, and remotely, in order to be able to manage your schedule according to your pace. “Having previously worked with companie
s has allowed us to create flexible training paths, so that employees can train on schedules outside of the office. “
Practice at the heart of DataScientest learning
The teaching provided by the data science training organization is based on the learning by doing method, in order to materialize the learning of knowledge through practice. “To remember the concepts, it is important to practice,” says Aurélia Fellous. For this, several devices are available to learners:
courses, combining theory and practice, with a ready-to-code environment included on a secure platform,
many exercises and business use cases,
a live chat run by a team of data scientists, reachable on a daily basis, to quickly answer learners’ questions.
A data project to put into practice the technical skills acquired
A common data project is carried out throughout the training in pairs or trinomials, from a list of subjects preselected from the news on relevant data sets. Learners also have the option of submitting their own project. “At the end of the course, they will have a concrete data project that they can put on their CV. »Among the projects that have emerged within data science training: connected glasses to display the subtitles of their interlocutor’s words to the hearing-impaired, the detection of plant diseases through the use of algorithms, or the optimization of itineraries for tourists in Paris. This project was presented during the first edition of Data Days.
Beyond technical skills, the data project also allows learners to acquire essential soft skills in the exercise of their profession. “In data science, technologies are constantly evolving, so you have to be able to look for information on code platforms to be up to date. By working as a team, they exchange, share and make their code accessible and understandable to other members of their team. These notions of communication and oral expression are also important because these profiles represent the core of many data-related projects within the company “, continues Aurélia Fellous. An effective formula since the completion rate of data science training offered by DataScientest is greater than 90%.