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2023/2024  KAN-CGMAO2003U  Business Analytics

English Title
Business Analytics

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for cand.merc. and GMA (GMA)
Course coordinator
  • Marin Jovanovic - Department of Operations Management (OM)
Main academic disciplines
  • Managerial economics
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 06-06-2023

Relevant links
Learning objectives
By the end of the course, students will be able to:
  • Create business analytic models to identify relationships relevant to specific business problems and generalize the application for business decision-making.
  • Explain important issues and apply analytical methods that are appropriate for the problem at hand.
  • Conduct business analytics by collecting, assessing, and applying business performance data.
  • Solve a business problem by using analytic methods to generate relevant insights.
  • Communicate the findings of the analysis and evaluate limitations and alternative methods.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 1
Compulsory home assignments
The student must obtain approval for 1 out of 2 possible group assignments in order to attend the ordinary exam. The assignment can be completed by groups of 4 or 5 students, and it can consist of up to 9 pages per group. Assessments will be conducted on a pass/fail basis.

Students will have no extra opportunities to get the required number of compulsory activities approved prior to the regular exam. If a student has not received approval for the required number of compulsory activities or has been ill, the student cannot participate in the ordinary exam.

If, prior to the retake exam, a student has still not been approved for the required number of compulsory activities but otherwise meets the preconditions set out in the program regulations, completing an extra assignment instead is possible. The extra assignment is a 10-page home assignment that covers the required number of compulsory activities. If the completed assignment is approved, the student will be able to attend the retake.
Examination
Business Analytics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Aids Limited aids, see the list below:
The student is allowed to bring
  • An approved calculator. Only the models HP10bll+ or Texas BA ll Plus are allowed (both models are non-programmable, financial calculators).
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Description of the exam procedure

The exam consists of a number of questions or exercises that need to be discussed and solved using the methods and approaches trained in the course. Responses should be well-grounded in the methods and approaches covered in exercises during the course. Exercises solved during the course are the primary means of preparing for the exam.
Course content, structure and pedagogical approach

Business analytics is a multidisciplinary field that uses data, statistical algorithms, and predictive models to identify and forecast business outcomes. It helps businesses make more informed decisions by providing insights based on data rather than on intuition or subjective interpretation.

 

Business analytics is important because it enables:

 

  • Improved decision-making. Business analytics provides a structured approach to decision-making, enabling businesses to make more informed decisions based on data.
  • Greater efficiency. By analyzing data, businesses can identify inefficiencies and optimize processes.
  • Enhanced competitiveness. Business analytics can help businesses gain a competitive edge by providing insights into market trends, customer behavior, and operational performance.
  • Increased profitability. By using business analytics to make more informed decisions, businesses can identify and exploit new opportunities, reduce costs, and improve efficiency.
  • Better risk management. Business analytics can help businesses to identify, assess, mitigate and avoid risks.

 

Business analytics helps businesses to make more informed and strategic decisions, improve efficiency, and stay competitive in a rapidly changing business environment.

 

This course focuses on how applying quantitative analytical models can develop insights that are derived from data and that inform decision-making. The aim is to provide students with the competencies necessary to develop solutions to business problems that general managers and business analysts frequently encounter and to discuss the implications of these solutions.

 

This course will focus on real-world applications of business analytics and examine how its methods have transformed firms and industries. We will discuss Moneyball, the Framingham Heart Study, Google, Twitter, IBM Watson, and Netflix, and many other examples.

 

The course focuses on skills relevant to business decision-making. It introduces students to methods and approaches applied in business analytics. This includes practical applications of topics such as data manipulation and visualization, descriptive statistics and hypothesis testing, optimization (linear and nonlinear), regression analysis, time series analysis, and advanced topics in business analytics (eg, geospatial analysis and network analysis). Students are trained to collect, structure, analyze and visualize data. The course explores general management problems through a number of cases and exercises.
Description of the teaching methods
Business problems are the starting point for business analytics, and the course adopts a decision-driven approach consisting of four steps: Framing the question, Assembling the data, Calculating the results by implementing the analytical model, and Telling others the results and discussing the implications (the FACT framework). For each topic we cover, we discuss several examples. Teaching is practically oriented.

Although the lectures frame business problems, the exercises focus on assembling the data, calculating the results, and telling the results. Exercises allow students to acquire a better understanding of the type of data and analytical methods needed to reach a solution for the business problem being considered. The implications of data quality and data neutrality and how these affect the solution of the problem are also thoroughly explored using in-class exercises and assignments. The teaching method ensures the participation of students.
Feedback during the teaching period
Feedback will be offered in three ways. First, students will receive feedback during lectures in the form of interaction with the teacher if any question arises. Second, students will receive one-on-one feedback during workshops in response to their inquiries. Students can also ask instructors to clarify the course content in relation to exercises, workshops, and the overall curriculum. Third, students will receive summative feedback during the workshops that addresses the challenges perceived during the one-on-one interaction. Students are expected to actively participate in lectures, workshops, and exercises. Additional individual feedback can be obtained during office hours.
Student workload
Lectures 33 hours
Preparations before lectures including a group assignment 83 hours
Exam preparations 90 hours
Expected literature

A full list of relevant literature will be provided in class. The following works are indicative:

  • Bertsimas, D., O'Hair, A., & Pulleyblank, W. (2016). The analytics edge. Dynamic Ideas LLC.
  • Davenport, TH (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80.
  • Grolemund, G. & Wickham, H. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Available at  https://r4ds.had.co.nz/
  • Jovanovic, M., Kostić, N., Sebastian, IM, & Sedej, T. (2022). Managing a blockchain-based platform ecosystem for industry-wide adoption: The case of TradeLens. Technological Forecasting and Social Change, 184, 121981.
  • Pimpler, E. (2017). Data visualization and exploration with R: A practical guide to using R, RStudio, and Tidyverse for data visualization, exploration, and data science applications. CreateSpace Independent Publishing Platform.
Last updated on 06-06-2023