Economics - Management

UNIVERSITY DEGREE: BIG DATA, DATA SCIENCE AND RISK ANALYSIS IN PYTHON

  • 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:

  • Ms Françoise SEYTE, Senior Lecturer
  • Mr Stéphane MUSSARD, Professor

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)

 

Read more

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)
Read more

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.

Read more

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
Read more

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
Read more

Admission

Target audience

Master 1 level

Read more

Recommended prerequisites

Econometrics Master I, Data analysis L3

Read more

And then

Professional integration

  • Bank risk analyst,
  • actuarial risks,
  • market risks,
  • Datascientist
Read more