ØABED2200 Business Analytics Course description

The course provides a rigorous introduction to Business Analytics. Business Analytics refer to statistical and computational methods used to analyze historical data to gain new insight and improve strategic decision-making. A central theme in the course is dealing with uncertainty in decision-making situations. We will constantly emphasize the interpretation of analysis results, as well as their implications for financial management and planning.

Examples of types of decision problem:

* How to set up an effective staffing plan when the need for labor varies over time?

* How to design an optimal transport plan for a supply chain?

* How to choose the location of production and warehouse in a supply chain?

* How to set up an investment plan with requirements for expected return and diversification?

* How to make demand forecasts based on historical data?

* How do we seasonally adjust a house price index?

* How can we use simulation to better understand the variation in a project's cash flow over different possible scenarios?

* How to use statistics tools to identify patterns in large data sets (Data Mining and Big Data).

The tuition is in English.

Mathematics, Statistics, Financial Accounting, Managerial Accounting.

None.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student has

• knowledge of how to apply the methods Decision Theory, Linear Programming, Simulation, Prediction Models, and Data Mining to business decision problems.
• knowledge of how quantitative methods and optimization can be used to solve business decision problems.

Skills

The student can

• perform analysis of business decision problems and make decisions based on maximin, minimax, minimax and opportunity cost
• draw decision trees and make decisions based on them
• perform simple and multiple regression analysis using relevant software and interpret results
• prepare forecasts using e.g. moving average, exponential smoothing and regression analysis
• formulate problems that can be solved using linear programming, as well as assess shadow prices and the value of increased capacity
• formulate and solve transport problems
• use relevant computer tools to solve business decision making problems
• implement simple simulation models in relevant computer tools
• carry out data mining in relevant data tools

General competence

The student

• has increased numerical and analytical competence
• can reflect on ethical issues related to business decision-making
• can solve problems in groups

Lectures and workshops solving cases in groups. Emphasis is placed on the use of computer tools in teaching.

The following coursework requirements must have been approved in order for the student to take the exam:

The module has 5 individual compulsory assignments. The student must pass at least 4 out of 5 assignments to take the exam. Each assignment consists of answering questions from a business case, and may also include quiz and contribution to discussion on the course website.

All required coursework must be completed and approved by the given deadline in order for the student to take the exam.

The exam in the course is:

Assignment in groups of 3 or 4 students. The assignment has a scope of a maximum of 15 pages (Arial 12 points and 1.5 line spacing).

All aids are permitted, as long as the rules for source referencing are complied with.

Grade scale A - F.

The exam papers are assessed by one internal and one external examiner.

At least 25% of the exam papers will be assessed by two examiners. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.

Knut Nygaard