Duration
1 year
Training structure
Faculty of Economics
Presentation
This DU has been created for students wishing to deepen their knowledge in risk analysis and professionals who need training on Python software. The training covers: big data, data science, database manipulation, high dimensional modelling for risk analysis such as neural networks and machine learning. The courses are given by Datascientists and professionals specialised in Big Data.
This University degree has been created for students wishing to :
- Become a data scientist, for professionals who need training in Python software, for: bigdata, econometrics, statistics, data processing, databases, risk analysis.
Leaders:
Type of training :
- Initial and continuing education / To know : The training is of hybrid type "face-to-face + distance zoom".
- Teaching fields: Economics, Law
- Type of diploma: DU (University Diploma)
The advantages of the course
The training is:
- hybrid "face-to-face + remote zoom" type
- Allows you to go on a work placement (under certain conditions)
Objectives
- Acquire training in the use of python libraries including: pandas, sklearn, keras, tensorflow, mongodb.
- Complement and enrich the VBA/SQL training with python NoSQL (Mongodb).
- Obtain an introduction to object programming for programming discriminative neural networks needed for risk analysis.
- Acquire training (theoretical and practical) in analytics, fraud detection, and big data in Python, including legal analysis of the use of massive databases.
- Acquire the basics of web scraping in order to extract information (database enrichment) and analyse it using textmining and machine learning techniques.
Know-how and skills
Master the python software to :
- massive data (spreadsheet, pandas, NoSQL),
- econometrics (time series, data analysis, etc.),
- textmining (extracting knowledge from textual data),
- customer risk analysis (neural networks),
- risk of customer anomalies,
- creation of micro-services and analytics,
- python for insurance.
- R for actuarial purposes
Organization
Programme
|
Lessons learned : |
Number of hours : |
|
Introduction to Python |
16 hours |
|
Web scraping |
12 hours |
|
Textmining |
16 hours |
|
NoSQL |
08 hours |
|
Fraud detection |
08 hours |
|
Analytics |
16 hours |
|
Neural networks |
20 hours |
|
Big Data Assurance |
16 hours |
|
Econometrics |
16 hours |
|
Machine Learning |
7 p.m. |
|
Tutored project |
70 hours |
|
Right |
06 hours |
- The training is a hybrid "face-to-face + distance zoom" type
And then
Professional integration
- Bank risk analyst,
- actuarial risks,
- market risks,
- Datascientist