Duration
1 year
Training structure
Faculty of Economics
Presentation
This university diploma (D.U.) was created for students wishing to deepen their knowledge of risk analysis and professionals who need training in Python software. The training covers: big data, data science, database manipulation, high-dimensional modeling for risk analysis such as neural networks and machine learning. Courses are taught by data scientists and professionals specializing in big data.
This university degree was created for students who wish to:
- Become a data scientist, for professionals who need training in Python software for: big data, econometrics, statistics, IT, databases, and risk analysis.
Responsible parties:
Type of training:
- Initial training and continuing education / Please note: The training is a hybrid model combining in-person and remote Zoom sessions.
- Fields of study: Computer science, economics, engineering sciences, statistics
- Type of degree: University Diploma (DU )
To apply: https://ecandidat.umontpellier.fr/ecandidat/#!accueilView
The advantages of the training program
The training is:
- hybrid "in-person + remote Zoom" type
- Allows you to go on an internship (under certain conditions)
Objectives
- Acquire training in the use of Python libraries, including: pandas, sklearn, keras, tensorflow, and mongodb.
- Supplement and enhance VBA/SQL training with Python NoSQL (Mongodb).
- Get an introduction to object-oriented programming for programming discriminant neural networks needed for risk analysis.
- Acquire training (theoretical and practical) in analytics, fraud detection, and big data using Python, including legal analysis of the use of massive databases.
- Acquire the basics of web scraping in order to extract information (database enrichment) and analyze it using text mining and machine learning techniques.
Know-how and skills
Mastering Python software for:
- big data (spreadsheets, pandas, NoSQL),
- econometrics (time series, data analysis, etc.),
- text mining (extracting knowledge from textual data),
- customer risk analysis (neural networks),
- risk of customer anomalies,
- creation of microservices and analytics,
- Python for insurance.
- R for actuarial science
Organization
Program
See information brochure
And after
Professional integration
- Banking risk analyst,
- actuarial risks,
- market risks,
- Data scientist