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Business Intelligence and Analyzing Big Data

  • Teacher(s):  
  • Course given in: English
  • ECTS Credits:
  • Schedule: Spring Semester 2018-2019, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Management, Orientation Marketing

    Master of Science (MSc) in Management, Orientation Business Analytics

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

 

Objectives

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 and integration different data sources.
  • Use SQL and OLAP methods in BI.
  • Analyse business benefits, complexity, cost, and challenges of business intelligence and big data projects.

Contents

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 implementing and reporting on a small business intelligence project (40% of the final grade).

References

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

First attempt

Exam:
Written 2h00 hours
Documentation:
Not allowed
Calculator:
Allowed with restrictions
Evaluation:

Grading: Exam (60%) and coursework (40%) of the final grade.

Retake

Exam:
Written 2h00 hours
Documentation:
Not allowed
Calculator:
Allowed with restrictions
Evaluation:

Retake exam 100% of the final grade.



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