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Course module: 320092-M-6
320092-M-6
Business Intelligence and Data Management
Course info
Course module320092-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Management; TiSEM: Management;
Is part of
M Marketing Analytics
M Communication and Information Sciences
M Information Management
Lecturer(s)
Lecturer
dr. E.A.M. Caron
Other course modules lecturer
Lecturer
E. Ioannou
Other course modules lecturer
Lecturer
N.M.E. Janse
Other course modules lecturer
Lecturer
P.K. Medappa
Other course modules lecturer
Academic year2019
Starting block
BLOK 3
Course mode
Full-time
Remarks-
Registration openfrom 07/01/2020 up to and including 21/08/2020
Aims
After completing this course, students will be able to:
  • apply the concepts of database management and use the Structured Query Language (SQL) to query and analyze business databases;
  • comment on the development and business implications of data transformation and data warehouses;
  • design and analyze an OLAP data cube for descriptive business analytics;
  • comment on the fundamental issues of knowledge discovery in databases, i.e. (the) data mining (process), such as learning algorithms for classification and prediction. Discuss the business relevance of data mining models;
  • interpret the substance of regression, fitting and supervised learning, including: interpreting parameters, designing the model, making choices;
  • comment on the Nearest Neighbor algorithm. Perform classification on data tables with Na├»ve Bayes;
  • design a decision tree on data;
  • analyze data sets using frequent item sets/association rules;
  • design clusters on data sets with different clustering techniques;
  • evaluate performance issues, complexity issues, business relevance and implementation;
  • work with (basic) data mining tools.
Instruction:
In computer labs you will work on practical exercises illustrating the theory. The exercises serve as a preparation for the exam.
 
 
 
Content
The main issues in this course concern the identification and extraction of new and useful knowledge from company databases. We will start with the fundamentals behind these databases, and introduce you to database management and database querying with SQL. The company databases are the sources for the development of the data-warehouse, which is a dedicated database for managerial decision-making. In addition, different types of knowledge can be derived from data-warehouses. OLAP data cubes for descriptive business analytics, rules characterizing potential customer classes, knowledge classifying groups with larger risks, and so on. Quite often causal relations are hidden in company databases and the goal of the data mining process is to induce these from the data and to represent them in meaningful ways to improve business processes. The emphasis will be on the methodological and practical aspects of data mining.

Specifics
Students may only participate in the examination after successful completion of several (lab) assignments (passed/not passed).
 
Type of instructions
Lectures, lab sessions, and self-study.
 
Type of exams
Written exam (100%) + See specifics.

Compulsory Reading
  1. Shmueli, Galit, Patel, Nitin R, and Peter C. Bruce, Data-Mining for Business Analytics, Wiley, 2016, ISBN 9781118729274.
  2. Scientific articles, made available to students on Blackboard in electronic form.

Recommended Reading
  1. Turban et al., Business Intelligence, A Managerial Approach, Pearson, 2011, ISBN 0-13-247882-X.
  2. Han, J. & M. Kamber, Data Mining, Morgan Kaufmann Publishers, 2011, ISBN 1-55860-901-6.
Course available for exchange students
Master level, conditions apply
Contact person
dr. E.A.M. Caron
Timetable information
Business Intelligence and Data Management
Written test opportunities
DescriptionTestBlockOpportunityDate
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Digitaal tentamen / Digital examEXAM_01BLOK 3116-06-2020
Digitaal tentamen / Digital examEXAM_01BLOK 3208-07-2020
Required materials
-
Recommended materials
-
Tests
Digital exam

Final grade

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Kies de Nederlandse taal