Master of Science in Informatics at Paris 13 University

Speciality: Data Mining, Analytics, and Knowledge Discovery

The specialty EID2 (Exploration Informatique des Données et Décisionnel - Data Mining, Analytics, and Knowledge Discovery) MSc focuses on data mining, business analytics, and knowledge discovery. The program is particularly suited for students who have completed a Bachelor’s degree (or equivalent) in one of the fields of computer science, mathematics or statistics, and wish to pursue a career in data mining and analytics.

 
The EID2 MSc is designed to produce graduates with the knowledge and skills to:
    • Select, apply and evaluate business analytics and data mining techniques which are focused on discovering knowledge that can be acted on to add value to a company.
    • Bring both an in-depth theoretical understanding, and the practical hands-on experience, to a data exploration and mining project including implementing novel and emerging techniques.
    • Keep abreast of current research and business analytics related topics.
 
The curriculum for the EID2 MSc is built on a foundation of core and elective courses. This curriculum joins courses with a Computer Science main theme, those with a Statistical data analysis, Advanced Databases, Data Mining, Business Analytics, and Data Warehousing main theme, and those with cultural courses. These may be grouped, as follows:
 
    • Fundamental courses
        • Programming Languages and Integrated Development Environment (4 ECTS)
        • Knowledge Representation (4 ECTS)
        • Numerical Methods and Data Analysis (4 ECTS)
 
·       Speciality courses
        • Statistical data analysis (3 ECTS)
        • Advanced Databases (3 ECTS)
        • Data Mining and Business Analytics (3 ECTS)
        • Data Warehousing (3 ECTS)
 
·       Cultural courses
        • English (2 ECTS)
        • Intellectual property (2 ECTS)
        • Jobs in computer science (2 ECTS)
 

The electives courses may be chosen, in consultation with the student's advisor,to meet the interdisciplinary  and the speciality distribution requirements. The full list of available courses may be grouped, as follows:

 
    • Deepening courses (1 or 2 choice among the list)
        • Decision-making support (4 ECTS)
        • Neural Networks learning (4 ECTS)
        • Statistical learning (4 ECTS)
        • Machine learning (4 ECTS)
 
    • Complementary  courses (1 or 2 choice among the list)
        • Visual data mining (4 ECTS)
        • Speech analytics (4 ECTS)
        • Text mining (4 ECTS)
        • Time series analysis (4 ECTS)
        • Knowledge management (4 ECTS)
        • Human-machine interaction (4 ECTS)
        • Social networks (4 ECTS)
        • Bioinformatics (4 ECTS)
 
The fourth semester is targeted to the writing of a dissertation during an internship in either a laboratory or a company.
    • Internship  (company/laboratory) (18 ECTS)