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Business Intelligence and Analyzing Big Data

  • Enseignant(s):   T.Niemi  
  • Titre en français: Business intelligence et analyse du Big Data
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre de printemps 2019-2020, 4.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formations concernées:
    Maîtrise universitaire ès Sciences en management, Orientation stratégie, organisation et leadership

    Maîtrise universitaire ès Sciences en management, Orientation comportement, économie et évolution

    Maîtrise universitaire ès Sciences en management, Orientation marketing

    Maîtrise universitaire ès Sciences en management, Orientation business analytics

 

Objectifs

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.

Contenus

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).

Références

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.

Evaluation

1ère tentative

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

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

Rattrapage

Examen:
Sans examen (cf. modalités)  
Evaluation:

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



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