2024/2025 KAN-CACAO2406U Data & Analytics in Management Accounting
English Title | |
Data & Analytics in Management Accounting |
Course information |
|
Language | English |
Course ECTS | 7.5 ECTS |
Type | Mandatory |
Level | Full Degree Master |
Duration | One Quarter |
Start time of the course | Second Quarter |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Study board |
Study Board for cand.merc. and ASC
|
Course coordinator | |
|
|
Main academic disciplines | |
|
|
Teaching methods | |
|
|
Last updated on 20-06-2024 |
Relevant links |
Learning objectives | ||||||||||||||||||||||
|
||||||||||||||||||||||
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
One out of two has to be approved. 1. Weekly quiz: Every week a mandatory multiple-choice test is administered. The purpose of the test is to provide students with an overview of which topics they master and which not. More specifically, the multiple-choice test examines students’ capabilities with respect to (i) knowledge, (ii) comprehension, (iii) application, and (iv) problem solving for the topics covered in the course. This helps students to better prepare for the final exam. 2. Case analyses and presentation: The students are required to work in teams to solve a case, deliver a case solution as well as produce a video recording where they present and discuss the solution. Each team will get written feedback. Students will not have extra opportunities to get the required number of compulsory activities approved prior to the ordinary exam. If a student has not received approval of the required number of compulsory activities or has been ill, the student cannot participate in the ordinary exam. If a student prior to the retake is still missing approval for the required number of compulsory activities and meets the pre-conditions set out in the program regulations, an extra assignment is possible. The extra assignment is a 10 page home assignment that will cover the required number of compulsory activities. If approved, the student will be able to attend retake |
||||||||||||||||||||||
Examination | ||||||||||||||||||||||
|
||||||||||||||||||||||
Course content, structure and pedagogical approach | ||||||||||||||||||||||
In today's data-driven business environment, the role of accountants has evolved from mere financial record-keepers to strategic advisors who leverage data and analytics to drive decision-making and enhance organizational performance. The "Data and Analytics in Management Accounting" course is designed to equip students with the essential skills and knowledge to harness the power of data analytics in the realm of accounting. By the end of this course, students will have the skills and knowledge necessary to leverage business analytics techniques to enhance accounting practices, support decision-making, and add significant value to organizations in a data-driven world. Contents: The rise of analytics as a discipline. Data driven decision making including common decision biases The role of analytics in accounting. The role of accountants in data-driven decision-making processes. How analytics can add value to financial reporting, auditing, and management accounting. Data Acquisition and Preparation including: a. Acquire data from various sources, including financial systems, databases, and external data providers. b. Clean, transform, and prepare data for analysis to ensure accuracy and relevance. Working with descriptive analytics including: a. The basics of descriptive statistics to summarize and visualize financial data. b. Data visualization tools to communicate financial insights effectively. Working with predictive analytics in accounting: a. Predictive modelling techniques such as regression analysis and time series forecasting. b. Predictive analytics to financial forecasting, risk assessment, and budgeting.
|
||||||||||||||||||||||
Description of the teaching methods | ||||||||||||||||||||||
Face to face Lectures
Group work Case studies/Exercises Lectures: Lectures are a fundamental teaching method for a course in business analytics for accounting. In these sessions, the instructor introduces key concepts, theories, and frameworks that form the foundation of business analytics in the accounting context. Case Studies and exercises: Case studies and exercises bridge the gap between theory and practical application. In a business analytics for accounting course, students analyze real-world accounting scenarios, apply analytical techniques, and make data-driven decisions. Case studies also encourage critical thinking and problem-solving skills as students work through complex accounting problems. The chosen textbook offers a veriety of cases and exercises including access to data sets that the course will utilize. Other cases and data sets will also be added to these. To solve cases and train analytical skills Power BI will be used in this course. Guest Lecturers: Inviting guest speakers from the accounting and analytics industry can offer students valuable insights into how analytics is applied in real-world accounting settings. These experts can share their experiences, best practices, and the latest trends in business analytics, providing students with a broader perspective on the field. Group Discussions and Debates: Group discussions and debates can promote active learning and critical thinking. Students can analyze accounting scenarios, debate different analytical approaches, and defend their viewpoints. These discussions encourage collaborative problem-solving and enhance students' ability to articulate their ideas |
||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||
The students will solve cases and exercises in groupwork during the course that will be commented on and discussed by the lecturer as well as other students. Students will receive written feedback on compulsory activies. | ||||||||||||||||||||||
Student workload | ||||||||||||||||||||||
|
||||||||||||||||||||||
Expected literature | ||||||||||||||||||||||
Main textbook: Ann C. Dzuranin, Guido Geerts, Margarita Lenk
(2022). Data and Analytics in Accounting: An Integrated Approach.
Wiley. ISBN: 978-1-119-72315-8
|