Description of targeted jobs and activities
The development of information systems now provides access to massive and complex data, the exploitation of which requires multidisciplinary approaches with a focus on statistics and computer science. The Data Science, Statistics and Econometrics program trains students for careers in data. In addition to teaching machine learning and deep learning, the program focuses on natural language processing, social network analysis, econometrics and time series forecasting. It covers different types of data: individual, temporal, spatial, networks, text and images.
In addition to machine learning and deep learning techniques that can be applied to large-scale or image data, emphasis is also placed on text data processing (NLP), network data analysis such as social networks, multidimensional data analysis such as mixed, panel or spatial data, and finally time series analysis for forecasting purposes.
The program aims to train data science experts, data scientists, and specialists in complex data requiring the use of advanced statistical techniques such as artificial intelligence, who are proficient in the IT and digital tools needed to implement them. This course provides additional skills in statistics, econometrics and forecasting techniques.
Description of assessed and certified skills
Students are trained in the latest statistical analysis methods, artificial intelligence algorithms, and the IT and digital tools essential for working as a data scientist or data analyst. This course also offers training modules in forecasting techniques and individual behaviour modelling.
Graduates will be able to:
- use programming and modelling tools;
- design and implement a statistical study from the initial data collection phase to the presentation of results in the form of dashboards and digital indicators;
- implement data processing and analysis methods using specialized software and programming languages (Python, R, SAS, etc.) in an appropriate digital environment;
- propose and develop a relevant statistical or numerical strategy (indicators and models) to model a complex phenomenon and analyze its suitability in relation to experimental data;
- implement different methods of data visualization, machine learning and deep learning appropriate to the context;
- interpret/present results for discussion with non-mathematicians.
- use distributed IT infrastructures to store and process data (SQL, NOSQL, etc.);
- lead projects that incorporate legal and ethical constraints in order to disseminate best practices.
Assessment methods
The assessment methods used enable the acquisition of all the skills, knowledge, competencies and skill sets required for the degree to be verified. These elements are assessed either through continuous and regular assessment, a final examination, or a combination of both.
Sectors of activity and types of employment
The master’s degree in data science, Statistics and Econometrics has a very high employment rate and opens up a large and growing number of opportunities for statistical managers in all sectors of activity (consulting, commerce, intelligent systems, media, banking, insurance, industry, biology, health, administration, research, sport, etc.).
Graduates can pursue careers in a variety of sectors:
- Data Scientist
- Data Analyst
- Forecaster
- Economist Statistician
- Econometrician
Graduates can also pursue doctoral studies to become lecturers or researchers at universities, the CNRS or EPSTs (INRIA, INRA, IFREMER).
Targeted audience
The Master for Smart Data Science is open to students of all nationalities. All applicants must have at least a four-year degree equivalent to 240 ECTS credits (at least a four-year bachelor’s degree or the first year of a master’s degree).
A solid knowledge of statistics, mathematics and computer science is required. Applicants with additional professional experience are welcome. Successful candidates are selected based on their qualifications, academic results and skills.
Master Smart Data Prerequisites
This program is taught entirely in English. Applicants who are not native English speakers must demonstrate a minimum level of B2 in English (CEFR scale) by providing the results of an official certification such as TOEIC, TOEFL, IELTS, CLES, etc.
Non-French-speaking students are strongly encouraged to take intensive French courses (Summer University) in August before the start of the semester. They can also take weekly French courses offered in the evenings at ENSAI.
The Master’s program is accessible through initial training, continuing education and the validation of prior learning.
