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Business Intelligence and Analyzing Big Data

  • Enseignant(s):  
  • Titre en français: Business intelligence et analyse du Big Data
  • Cours donné en: anglais
  • Crédits ECTS:
  • Horaire: Semestre de printemps 2019-2020, 4.0h. de cours (moyenne hebdomadaire)
      WARNING :   this is an old version of the syllabus, old versions contain   OBSOLETE   data.
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At the of the course the students will be able to:

  • Explain key concepts and methods of business intelligence and big data.
  • Use standard tools for data collection, integration of different data sources, and processing large data sets.
  • Use SQL and OLAP methods in BI.
  • Analyse business benefits, complexity, cost, and challenges of business intelligence and big data projects.


The course will detail the steps in a successful BI project from identifying the data sources to creating visual reports. The methods and tools detailed in the course contain, for example:

  • Internal and external data sources (open and public data, social media data, data quality, confidentiality and privacy issues)
  • Database systems (relational, XML, NoSQL)
  • ETL process in internal and external data (integration, harmonisation, correctness of aggregations, missing data, etc.)
  • Data warehouse design (models, OLAP cube design, design challenges)
  • Analysis methods and tools (OLAP, R, SQL, BI and big data tools)

Coursework: Group assignment on implementing, reporting and presenting a small business intelligence project (50% of the final grade).


No specific textbook. All relevant material will be made available on the course website. For those interested the following books contain relevant material for the course:

  • Krishnan, Krish. Data warehousing in the age of big data. Morgan Kaufmann Publishers Inc., 2013.
  • Kimball, Ralph, and Margy Ross. The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons, 2013.
  • Danneman, Nathan, and Heimann, Richard. Social media mining with R. Packt Publishing Ltd, 2014.


1ère tentative

Sans examen (cf. modalités)  
SUMMER 2020, due to coronavirus

Grading: Take-home exam (50%) and coursework (50%) of the final grade.


Sans examen (cf. modalités)  

Grading: Take-home exam (50%) and coursework (50%) of the final grade.

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