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).

Language of instruction is English.

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

• a theoretical understanding of quantitative methods for analyzing data
• an understanding of the role of empirical evidence in evaluating economic, managerial, and business problems
• an understanding of the strengths and weaknesses of different statistical methods

Skills

The student can

• formulate empirical questions
• gather, obtain, and organize quantitative data
• conduct statistical analysis using software
• interpret statistical results

General competence

The student can

• think critically and understand the role of assumptions in arguments
• communicate effectively about economic, managerial, and business issues
• develop a well-organized argument that states assumptions and hypothesis, which are supported by evidence
• use and appropriately cite different data sources

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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 requirement must have been approved for the student to take the exam:

• Coursework 1: 5 individual 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. Expected time to complete each assignment is 5 hours. Each assignment must be completed and approved by the given deadline.

The purpose of the coursework requirement is for the students to apply and evaluate analysis methods taught in the module.

All required coursework must be completed and approved for the student to take the exam. If the coursework requirement is not approved, the student is given one opportunity to submit a new coursework requirement within a specified deadline.

The exam in the course is a course paper written in groups of max 5 students. The assignment has a scope of 10-15 pages, excluding the cover page and table of contents. Font and font size: Arial / Calibri / Verdana 12 points. Line spacing: 1.5.

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

Grade scale A - F

This course aims to provide students with an understanding of how data and statistical analysis can improve economic, managerial and business decision making. Students will learn how to develop empirical questions, collect and organize relevant quantitative data, apply appropriate statistical methods, and ultimately, make better business and policy decisions. The course will draw on a wide range of business and economic applications, such as finance, advertising, internet retailing, and human resources.

Language of instruction is English.

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