0988 Data Management and Analysis in IB
Katarina Nossakova, BA, MA
Contact details
Weekly hours
Language of instruction
09/07/22 to 09/13/22
Registration via LPIS
Notes to the course
Day Date Time Room
Monday 10/10/22 10:00 AM - 12:30 PM D2.0.342 Teacher Training Raum
Tuesday 10/11/22 10:00 AM - 12:00 PM TC.3.11
Tuesday 10/11/22 02:00 PM - 05:00 PM TC.4.03
Wednesday 10/12/22 11:00 AM - 01:00 PM TC.3.11
Wednesday 10/12/22 03:00 PM - 06:00 PM D5.1.004
Thursday 10/13/22 11:00 AM - 01:00 PM TC.4.18
Thursday 10/13/22 03:00 PM - 06:00 PM TC.3.08
Friday 10/14/22 10:00 AM - 12:00 PM TC.3.11
Friday 10/14/22 02:00 PM - 05:00 PM TC.4.14
Wednesday 11/02/22 05:00 PM - 07:00 PM TC.4.16
Monday 11/07/22 05:00 PM - 07:00 PM D1.3.088


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

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

The course addresses some quantitative skills and merges techniques in Microsoft Excel with basic statistical analysis skills. The course considers various types of research and analysis methods. Throughout the course, key ideas, concepts, and techniques will also be discussed extensively in class. The lectures provide both basic concepts and informative material along with some critique of the main ideas. We will use many examples that relate to international business.

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


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

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.



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

Content of the final exam:

  • A closed-book exam. The exam will include theory questions and exercises in Excel. The material relevant for the exam is the entire course material and all readings. The exam will take place approximately 2 weeks after the final session

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:

  • 90-100:           1
  • 80-89:             2
  • 70-79:             3                     
  • 50-69:             4
  • <50:                5
Last edited: 2022-08-16