Permalink
2018/2019 KAN-CCMVI2071U Business Intelligence
| English Title | |
| Business Intelligence |
Course information |
|
| Language | English |
| Course ECTS | 7.5 ECTS |
| Type | Elective |
| Level | Full Degree Master |
| Duration | Summer |
| Start time of the course | Summer |
| Timetable | Course schedule will be posted at calendar.cbs.dk |
| Max. participants | 60 |
| Study board |
Study Board for MSc in Economics and Business
Administration
|
| Course coordinator | |
|
|
| For academic
questions related to the course, please contact the course
instructor.
Other academic questions: contact academic director Sven Bislev at sb.msc@cbs.dk |
|
| Main academic disciplines | |
|
|
| Teaching methods | |
|
|
| Last updated on 29-05-2019 | |
Relevant links |
| Learning objectives | ||||||||||||||||||||||
To achieve the grade 12, students should meet the
following learning objectives with no or only minor mistakes or
errors:
|
||||||||||||||||||||||
| Course prerequisites | ||||||||||||||||||||||
| This is an introductory course for MSc students in business studies. It is self-contained and fundamental mathematics will be reviewed. Students are expected to have basic mathematics knowledge such as calculus and linear algebra. No programming skills is needed. | ||||||||||||||||||||||
| Examination | ||||||||||||||||||||||
|
||||||||||||||||||||||
| Course content and structure | ||||||||||||||||||||||
|
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 empirical data to support decision making and evaluation in business. It uses a combination of lectures and workshops. The course emphasizes the practical applications and makes extensive use Excel and Microsoft Azure Machine Learning Studio for intelligent business analytics.
Each class will be held in the classroom. It is combined with
lecture (theory) and workshop (practice). Students will need to
bring their own laptops to the classroom (connected to Wifi) or the
teaching can be delivered in the computer lab.
Class 1: Introduction to Business Intelligence Class 2: Understanding Business Data Types and Structure Class 3: Summarizing and Presenting Business Data Class 4: Forecasting Sales Using Linear Regression Class 5: Forecasting Demand Using Artificial Neural Networks Class 6: Identifying Fraudulent Card Transactions Using Logistic Regression Feedback Activity: a small assignment (with several questions) Class 7: Predicting Customers’ Feedback Ratings Using Tree-Based Models Class 8: Segmenting Consumers Using Naive Bayes and K-Nearest Neighborhood Methods Class 9: Recommending Products to Customers Using Collaborative Filtering Techniques Class 10: Segmenting Consumers Using Cluster Analysis Class 11: Review Session and Q&A for the Assignment |
||||||||||||||||||||||
| Description of the teaching methods | ||||||||||||||||||||||
| Each class will be held in the classroom. It is combined with lecture (theory) and workshop (practice). Students will need to bring their own laptops to the classroom (connected to Wifi) or the teaching can be delivered in the computer lab. | ||||||||||||||||||||||
| Feedback during the teaching period | ||||||||||||||||||||||
| Students will get a mini/small assignment based
on the first 6 classes, helping them to understand how they are
doing academically, and what to expect from the final assignment.
All Home Project Assignments/mini projects are based upon a research question (problem formulation) formulated by the students individually, and must be handed in to the course instructor for his/her approval no later than 11 July 2019. The instructor must approve the research question (problem formulation) no later than 16 July 2019. The approval is a feedback to the student about the instructor's assessment of the problem's relevance and the possibilities of producing a good report. |
||||||||||||||||||||||
| Student workload | ||||||||||||||||||||||
|
||||||||||||||||||||||
| Further Information | ||||||||||||||||||||||
|
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 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 end February 2019 at the latest.
|
||||||||||||||||||||||
| Expected literature | ||||||||||||||||||||||
|
Mandatory readings:
Anil Maheshwari. Business Intelligence and Data Mining. Business Expert Press, 2015, Chapters 1-8
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
Additional relevant readings:
Maxime Cohen. Big Data and Service Operations. Production and
Operations Management, Vol. 27, No. 9, 2018, pp. 1709-1723 Roger
Barga, Valentine Fontama, Wee Hyong Tok. Predictive Analytics with
Microsoft Azure Machine Learning, Apress, 2015
|
||||||||||||||||||||||