Syllabus

Title
0895 Data Management and Analysis in IB
Instructors
Univ.Prof. Dr. Phillip C. Nell
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/05/23 to 09/15/23
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Tuesday 10/17/23 01:00 PM - 04:00 PM TC.0.01 ERSTE
Tuesday 10/24/23 01:30 PM - 04:30 PM TC.1.02
Tuesday 11/07/23 01:00 PM - 04:00 PM TC.4.16
Tuesday 11/21/23 01:30 PM - 05:00 PM TC.3.11
Tuesday 11/28/23 02:00 PM - 06:00 PM TC.4.14
Tuesday 12/05/23 02:00 PM - 04:00 PM D3.0.222
Tuesday 12/12/23 01:00 PM - 04:00 PM TC.5.28
Tuesday 12/19/23 02:30 PM - 06:30 PM D2.0.330
Contents

1. COURSE DESCRIPTION

The increasing complexity of industry and commerce, along with global trends such as big data, the analytics revolution, or digitization, means that companies must take a more sophisticated approach to their decision-making. The quality of decisions is often based on “hard data” to select the best course of action. Moreover, management consultants are often required to strongly quantify their recommendations wherever possible.

However, managers often fail to understand some of the underlying (statistical) principles and rely too heavily on intuition. In consulting, especially junior consultants – the level which is usually tasked with extensive analytical tasks at top management consultancies – frequently face a range of challenges when assigned analytical jobs.

This course is designed to address this knowledge gap by integrating quantitative skills and Microsoft Excel-based techniques with basic statistical analysis skills. However, it is important to note that while we will be using Excel as a tool for statistical analysis, this is not an Excel training course. Our focus is primarily on the understanding and application of statistical principles, with Excel serving as a means to implement and illustrate these concepts.

We will cover various types of research and analysis methods. Throughout the course, key ideas, concepts, and techniques will be discussed extensively in class. The lectures will provide both basic concepts and informative material, along with a critique of the main ideas. We will use many examples that relate to international business.

Please Note: Students should be aware that this course is not suited for those who already have a strong background in Excel and in statistical analysis.

2. AIMS

Main aim of the course is to deliver a set of key quantitative skills that the students can use in the future. This not only covers how to conduct a specific quantitative analysis but also to understand its value and importance in today’s business life, as well as its associated problems when it comes to preparing, managing, analyzing, and communicating data and results of analyses.

Learning outcomes

Objectives
By the end of the course students will have learned:

  • the need for a holistic approach to the analysis of data to generate valuable information
  • several analytical approaches and techniques in MS Excel.

Learning outcomes

Knowledge and Understanding:

After completing the course, the student will be able to:

• examine how data assists management in decision-making

• understand the different types of data available to businesses, the ways data is collected, and what makes data usable to create knowledge.

• understand and apply specific techniques for data analysis, including techniques using Excel.

 

Cognitive and Subject Specific Skills:

After completing the course, the student will have acquired skills as follows:

• know how to conduct basic analyses in Excel

• demonstrate that they can move beyond a simple description of data to the analysis and evaluation of data and to the transformation into information

• be able to code and analyze primary data by using appropriate (statistical) software packages

• be able to present and interpret data analysis results in a professional manner

 

Key Skills:

Upon completion of the course, students will have the following skills:

• analytical skills – beyond simple description

• discussion skills of data analysis and interpretation issues through class discussion

• argumentation skills – to debate and defend considered arguments in class during discussions

Attendance requirements

This course is organized as a face-to-face module. There is no online or hybrid setup.

Students failed the course if more than 20 % of total class time is missed. In any case, missed class time is taken into account in the participation grade.

 

Teaching/learning method(s)

The course is taught using a combination of lectures, case analyses and class discussion. The readings will give you a broad picture of the importance of data management and analytics.

Assessment

Grading:

  • Final exam 50%
  • Group case study 30%
  • In-class participation 20%

The final exam:

  •  The final exam will take place in the final session on 5th, December, 2023

Group case study:

  • Analysis of a case study in Excel
  • The case study will be assigned and discussed at the end of the course

In-class participation:

  • Active class participation is expected. Both quality and frequency of contributions will be evaluated
  • Note: Coming late to sessions and missing sessions will contribute negatively to the final participation grade

The final grade will be based on the following total points out of 100:

≥ 90...1

≥ 80...2

≥ 70...3

≥ 60...4

< 60...5

Readings

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Last edited: 2023-07-03



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