• Explain the key concepts of business intelligence
• Identify types of analytics used in business
• Explain how data-driven decision making impacts business
• Identify types and formats of data
• Effectively use Microsoft Excel and Microsoft Azure Machine Learning Studio to process, summarize and visualize business data
• Display a comprehensive understanding of a wide range of quantitative methods and machine learning techniques
• Appropriately choose and appraise methods and technology for specific business problems

Business intelligence refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business data in order to support business decision making. Essentially, it is a collection of data-driven decision support models. This course teaches students analytical skills on data to support decision making and evaluation in business. It emphasizes the practical applications and makes extensive use of Microsoft Excel and Microsoft Azure Machine Learning Studio for intelligent business analytics. Each class is combined with lecture (theory) and workshop (practice). Students need to have their computers with Internet access. 

Preliminary assignment: A small assignment (with several questions) 

Class 1: Introduction to business intelligence and Microsoft Azure Machine Learning Studio

Class 2: Understanding business data types and structure 

Class 3: Summarizing and presenting business data 

Class 4: Forecasting sales using linear regression

Class 5: Identifying fraudulent card transactions using logistic regression

Class 6: Data preprocessing, model training and evaluation

Feedback activity: A small assignment (with several questions) 

Class 7: Segmenting consumers and forecasting sales using artificial neural networks

Class 8: Predicting customers’ feedback ratings using tree-based models

Class 9: Recommender systems

Class 10: Segmenting consumers using cluster analysis

Class 11: Course review and Q&A for the assignment

Preliminary assignment: To help students get maximum value from ISUP courses, instructors provide a reading or a small number of readings or video clips to be read or viewed before the start of classes with a related task scheduled for class 1 in order to 'jump-start' the learning process.

Course timetable is/will be available on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams

We reserve the right to cancel the course if we do not get enough applications. This will be communicated on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams in in March 2021.

Mandatory readings:

• Anil Maheshwari. Business Intelligence and Data Mining. Business Expert Press, 2015, Chapters 1-8
• Jeff Barnes. Azure Machine Learning Microsoft Azure Essentials, Microsoft Press, 2015
• Hsinchun Chen, Roger Chiang and Veda Storey. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, Vol. 36, No. 4, 2012, pp. 1165-1188
• Maxime Cohen. Big Data and Service Operations. Production and Operations Management, Vol. 27, No. 9, 2018, pp. 1709-1723

Additional relevant readings:

• Roger Barga, Valentine Fontama, Wee Hyong Tok. Predictive Analytics with Microsoft Azure Machine Learning, Apress, 2015