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2017/2018  KAN-CCMIU1000U  CEMS Business Analytics

English Title
CEMS Business Analytics

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 20
Max. participants 30
Study board
Study Board for BSc og MSc in Business, Language and Culture, MSc
Course coordinator
  • Evis Sinani - EGB
Main academic disciplines
  • Statistics and quantitative methods
Last updated on 07-09-2017

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: At the end of the course students will be able to:
  • use Stata in reading and manipulating the data;
  • calculate and interpret measures of correlation, partial correlation, and regression coefficients from different regression models and conduct tests based on cross section, time-series, and panel data;
  • test hypothesis and perform advanced statistical techniques to explore and analyse the relationship among variables;
  • critically discuss and recommend solutions to problems encountered in the analysis of a specific phenomenon;
  • critically interpret and present the results and make recommendations based upon the results of the analysis;
Course prerequisites

This is not an advanced course in statistics or econometrics. Issues such as dynamic panel data techniques are not going to be addressed in this course. The purpose of the course is to provide the business students with the basic principles, techniques, and applications of descriptive statistics as well as in depth data analysis, using STATA as the statistical software. Thus, this course is recommended to all master’s level students who have prior basic knowledge in statistics.
CEMS Business Analytics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Winter
Aids Limited aids, see the list below:
The student is allowed to bring
  • Non-programmable, financial calculators: HP10bll+ or Texas BA II Plus
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
At all written sit-in exams the student has access to the basic IT application package (Microsoft Office (minus Excel), digital pen and paper, 7-zip file manager, Adobe Acrobat, Texlive, VLC player, Windows Media Player). PLEASE NOTE: Students are not allowed to communicate with others during the exam : Read more about exam aids and IT application packages here
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Course content and structure


In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in decision-making, business planning and economic analysis. The purpose of the course is to provide master’s students with business analytical skills necessary to solve managerial problems in today’s competitive decision-making environment. It aims to familiarize business students with the basic principles, techniques, and applications of descriptive statistics as well as in depth data analysis.

Even though managers across the wide range of industries may not be necessarily involved in the applications of analytical skills, they should be able to understand the research design proposed and adequately judge the quality of research given its implications for decision making. Accordingly, this course is particularly useful to master students, in that it teaches them the state of the art tools and methods to analyze and process data rigorously, thus correctly addressing business related issues or hypotheses.

The statistical software we will use in this course is STATA, one of the most powerful statistical programs able to process small as well as large databases whether they are cross section, time series, panel data, or simply survey data. You will experience a “hands on software” approach and an interactive learning in analyzing these types of data. The simple and consistent command structure of this statistical program makes it rather easy to learn and to become proficient in its application.



The purpose of this course is to provide to master’s-level students with state of the art analytical skills for analyzing data rigorously. Thus, this course will address the quantitative skills with respect to data mining such as reading, summarizing and analysing the data; testing hypothesis and performing cross-section, time series and survey data estimation analysis. However, the course also considers problems related to data analysis and interpretation.


This course will help the students build upon their technical skills. For instance students will learn how to use STATA to perform data mining; prepare statistical analysis (table statistics, graphs for data exploration, testing hypothesis and performing statistical regressions/techniques to explore the relationship among variables); interpret and present the results in a comprehensive way.


The course consists of lectures that introduce the basic concepts and their applications to real business (and economic) data, such as cross-section, time series and survey data. The statistical program the students will get acquainted with and use throughout the course is Stata. Lectures will be interactive with ‘hands on software’ approach such that lecturing and learning are closely integrated. This approach will enable the students to learn how to use the software and it will facilitate the understanding of the course concepts and their applications on real data, as well as increase the class dynamics with lively in the class discussions.


At the end of the course the students will know how to use Stata in reading and analysing the data; testing hypothesis and applying cross-section, time series and survey data estimation techniques.

Indicative topics to be covered in this course:


  • Introduction to STATA, the software and the output
  • Introduction to linear regression analysis
  • Evaluation of linear regression analysis: regression diagnostics
  • The use of qualitative variables: Limited Dependent variable models
  • Multinomial logit and probit models
  • Model specification errors
  • Forecasting: Time series analysis



Teaching methods
Both the lectures and the in class applications will involve the students in class discussions which will be an occasion where everyone has a chance to be heard. Class discussion is intended to tap the creative re-sources of all class members, explore applications of theoretical course content, obtain feedback on degrees of understanding on a topic, help students to present and defend their ideas, and break up long lecture situations by providing additional value which may also act as a stimulant for further sessions. Other obvious but very important considerations are that students would gain experience and pleasure of working with others in class and provide each other with mutual aid and support.
Feedback during the teaching period
In this course the students will receive feedback in a continuous basis through lectures and exercises we perform in class, office hours, and in-class discussion. We encourage our students to make use of all these opportunities in order to enhance their learning and understanding of the notions and concepts discussed in the class. Also, feedback will be offered through regular participation in discussions and two-way communications in lectures as well as during lecture breaks.

Student workload
LECTURES 30 hours
Expected literature

The required textbook for this course is:

Damodar Gujarati. Econometrics by Example, Palgrave Macmillan (latest edition)

Last updated on 07-09-2017