Aller à : contenu haut bas recherche
 
 
EN     FR
Vous êtes ici:   UNIL > HEC Inst. > HEC App. > SYLLABUS
 
 

Business Intelligence and Analyzing Big Data

  • Teacher(s):   T.Niemi  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2019-2020, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Management, Orientation Business Analytics

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

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

    Master of Science (MSc) in Management, Orientation Marketing

 

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, 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.

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 on implementing, reporting and presenting a small business intelligence project (50% 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:
Without exam (cf. terms)  
Evaluation:
SUMMER 2020, due to coronavirus

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

Retake

Exam:
Without exam (cf. terms)  
Evaluation:

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



[» go back]           [» courses list]
 
Search


Internef - CH-1015 Lausanne - Suisse  -   Tél. +41 21 692 33 00  -   Fax +41 21 692 33 05
Swiss University