Courses Master Display 2022-2023
|Course Description||To PDF|
|Course title||Data Analytics for DBE|
For more information: firstname.lastname@example.org
|Language of instruction||English|
The course introduces conventional quantitative techniques for business decision making and obtaining hands-on experience in analysing business processes using available data and quantitative techniques.
After successfully finishing this course, students will be able to:
* List several data analytics methods.
* Analyse data by using data science concepts.
* Demonstrate understanding by developing and explaining how data can be used to provide new insights into business and create value for the business.
* Translate business problems into canonical data mining tasks and study business problems from a data perspective.
* Interpret and communicate application results from data science concepts in a business context.
PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.
This course introduces data analytics methods which are often used to support business decisions, particularly data-intensive decision problems. Data science topics such as predictive modelling, data mining, different types of modelling problems, model evaluation and model deployment are discussed. This course relates to several application areas where business problems are studied from a data perspective, business decision making is supported using systematic data analysis. Examples of applications are operations, manufacturing, supply-chain management, customer behaviour modelling, marketing campaign performance, workflow procedures, and so on. Many decision problems in these application areas are characterized by large uncertainty in data. Uncertainty modelling techniques, discussed in this course, are designed to support data driven analysis under these circumstances.
|Teaching methods (indicative; course manual is definitive)|
|Assessment methods (indicative; course manual is definitive)||Written Exam|
|Evaluation in previous academic year||For the complete evaluation of this course please click "here"|
|This course belongs to the following programmes / specialisations||