The sheer amount of numerical data, textual documents, images, video, Web sites available today is overwhelming, and cannot satisfy, per se, the emerging knowledge society. It is indeed necessary to extract, from this wealth of information, the knowledge hidden inside. Only this ability could guarantee a better future to the individuals and the society, as well as a sustainable economical development and competitiveness.
To locate useful information, to transform it into actionable knowledge, and to manage its use for decision-making can be accomplished through the exploitation of methodologies and tools of Data Mining and Knowledge Management (DMKM). Notwithstanding the availability on the market and in academic environments of advanced solutions and systems, DMKM still calls for further research and developments to face new important challenges. In particular, some hot issues are still to be tackled, such as the following ones:
To face the exponential increase of data it is not sufficient to rely on larger storing devices and/or faster computers. New intelligent approaches are to be designed to tame the very size of data.
Data assume different modalities, such as numbers, texts, audio-video, sensor signals, and so on. Integrating into a unique system such complex data is still a challenge. Also, spatial distribution of data (for instance, on several Web sites or different data bases) is a source of difficulty for integration.
More importantly, there is still a "semantic gap" between the form in which the data are represented and used by a computer and their "meaning" for a human user. The new emerging techniques for the Semantic Web are trying to close this gap.
At the end of the programme, the successful students shall have acquired competences in computer science, applied mathematics and statistics, and advanced information processing methods that will allow them to:
Perform in depth analysis of information requirements for solving problems;
Manage large databases;
Make use of such databases with the goal of extracting from them hidden information and knowledge;
Deploy this knowledge in decision support systems or intelligent systems, both in academic and in industrial environments.
Around 50% of the programme is devoted to applications in the field, solving real data mining problems and the implementation of the standard techniques liable to be encountered by students in their future professional activities.
During their obligatory stage in Semester 4, students can integrate research laboratories in Data Mining and/or Knowledge Management for academic research work or join a company that develops solutions and/or applications in DMKM. On the one hand, they will be able to perfect their knowledge and, on the other, to contribute to the growth of this very active field of research. The skills in computer science and applied mathematics that are consolidated in this way by the software present on the market will also enable them to integrate applied research teams in a variety of fields, such as bio computing, information searches on the Internet, Customers Relationship Management (CRM), and so on. The skills acquired in each module are described in the syllabi and in the supplements of the diploma. Teamwork, multidisciplinary opportunities and taking into account the end user of the DMKM’s methods and tools will all be the focus of particular attention and active encouragement.
We should distinguish:
Common courses. These are composed of 10 courses: 6 in Semester 1, 2 in Semester 2 and 2 in Semester 3. They are devoted to providing the students with the theoretical and methodological background for the specialties. All common courses will be guaranteed to be multi-localized by the partner universities, through video-conferencing . One teacher or a team can give each course. The courses will be then available to all students independently from the country they live in. In this way, all students will share a common background (same pedagogical content, same teachers and same language with a virtual unity of place). To every course teachers available locally at each university will supply 15 additional hours of tutoring.
Specialty courses. Semesters 2 and 3 are dedicated to the acquisition by each student of two specialties provided by the partners under the mobility conditions stated below. Each partner gives a specific training related to its own specialties but following the global objective of the programme. In order to avoid course duplication, these semesters offer a total of six specialties including four courses each. The specialty courses are organized locally and not broadcasted using the video-conference technology:
Semester 2 is associated to the following specialties:
E-Science, by the University of Paris 6 (UPMC, France);
Data Mining & Complex system modelling, application in social science, by the University of Lyon 2 (ULY2 France);
Knowledge and Decision, by Polytech’Nantes (EPUN, France);
Semester 3 is associated to the following specialities:
Statistical Modelling and data mining, by the Polytechnic University of Catalonia (UPC, Spain)
To be eligible to apply for the Master programme DMKM, students must satisfy both the following conditions:
Be the holder of a Bachelor degree (a minimum of three years' study at a university and corresponding to the equivalent of 180 ECTS) in the fields of Computer Science, Mathematics or Statistics.
Mastering English at a level equivalent to 550 TOEFL.
In the following table the conversion among different English evaluation systems is reported. The minimum accepted for the European Master DMKM is “TOEFL Paper 550” or equivalent.
No work experience is required.
Fortunately enough I was able to find StudyPortals. Right from the start of the application to getting the confirmation of admission I was using StudyPortals.
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Lyon 2 Lumière University is one of the first universities to have integrated the European higher education scheme right from the start of the academic year 2004. The courses are organised within the scope of the LMD' system (Bachelor's degree - Master's degree - PhD).