Syllabus

Titel
1873 Managing and Analyzing Data for Business Decisions
LV-Leiter/innen
Ass.Prof. Alexandra Gregoric
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
23.09.2019 bis 15.11.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Montag 18.11.2019 11:30 - 16:00 TC.3.02
Dienstag 19.11.2019 11:30 - 16:00 TC.3.02
Mittwoch 20.11.2019 11:30 - 16:00 LC.-1.038
Donnerstag 21.11.2019 11:30 - 16:30 TC.3.02
Freitag 22.11.2019 12:00 - 16:30 LC.2.064 Raiffeisen Kurslabor

Inhalte der LV

1. Course description

The increasing complexity of business, 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 increased when managers rely on “hard data” to select the best course of action. Management consultants as well are often required to focus strongly on quantifying their recommendations wherever they can. However, managers (and other users) often fail to understand some of the underlying (statistical) principles or simply too often rely on their guts to make decisions. In consulting, especially junior consultants – the level that is usually charged with extensive analytical tasks at top management consultancies – frequently face a number of challenges when being assigned analytical jobs.

The course is an introductory business statistics course. The course focuses on descriptive statistics, charts, hypotheses testing, correlations and basic introduction to linear regression analysis. The course consists of lectures that introduce the basic concepts and their applications to real economic problems using Excel (or alternatively, Stata) and various types of data (exercises). The lectures aim provide basic concepts and methods but also points to various issues in applying these methods in practice.

Please Note: Students should be aware that the course is not suited to students who already have a strong background in statistical analysis. During the course, we will primarily use EXCEL.

 

2. Aim of the course

The course is taught using a combination of lectures, case analyses, class exercises and discussions. The main aim of the course is to deliver a set of basic quantitative skills that the students can use in the future. This not only covers the knowledge on how to conduct a specific quantitative analysis but also the ability to understand its value and importance in today’s business life, as well as its associated problems when it comes to preparing, managing, analyzing, interpreting and communicating data and results of analyses.

Please Note: Students should be aware that the course is not suited to students who already have a strong background in statistical analysis. During the course, we will primarily use EXCEL.

Lernergebnisse (Learning Outcomes)

Knowledge and Understanding:

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

» examine how data assists managers in the decision-making, understand the different types of data available to businesses, the ways data is collected, and what makes data usable to create knowledge

» strengthen their ability to frame and formulate managerial problems and their ability to provide inputs based on quantitative analysis

» understand and apply specific techniques for data analysis, such as Excel

» understand the different issues in making inferences based on sample data and, consequently,  understand how to interpret and represent the results properly

» cultivate a sound judgment when making the link between the empirical results and managerial (policy) implications

 

Cognitive and Subject Specific Skills:

After completing the course, the student will develop the following skills:

» know how to gather and search for information that can assist the managers in their decisions

» demonstrate that they can move beyond a simple description of data to the analysis and evaluation of data and to transfer of data and results into information that can sustain decision making

» be able to code and analyze primary data by using Excel

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

 

Key Skills:

Students upon completion of the course 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

 

Regelung zur Anwesenheit

If you miss more than 20% of total class hours or more, you will fail the course!

Lehr-/Lerndesign

The course is taught using a combination of lectures, case analyses, class exercises and discussions. The main aim of the course is to deliver a set of basic quantitative skills that the students can use in the future.

Leistung(en) für eine Beurteilung

The course grade is based on the following components:

» In-class participation (10% - Individual)

» Individual paper (25% - Individual; more information follows)

» Final exam (65% - Individual)

Attention: If you miss more than 20% of total class hours or more, you will fail the course!

The final exam will take 120 minutes, it is hand-written, closed book, and no statistics software is necessary (i.e. no computer required). There will be open and closed questions based on all the readings, cases, and exercises that were required for and/or discussed during this course.

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Before day 1, please buy and read the following case:

Campbell, D.; Martinez-Jerez, F.; Epstein, M. (2006): “Slots, tables, and all that jazz: Managing customer profitability at the MGM Grand Hotel”, Harvard Business School Case: 9-106-029.

 

Please, read also:

Davenport, T. H. (2006). “Competing on analytics”, Harvard Business Review (will be uploaded)

Erreichbarkeit des/der Vortragenden

Course lecturer: 

Aleksandra Gregorič

Associate Professor, Department of Strategy and Innovation, Copenhagen Business School

ag.si@cbs.dk

(https://www.cbs.dk/en/research/departments-and-centres/department-of-strategy-and-innovation/staff/agrsi-0)

Sonstiges

Detailinformationen zu einzelnen Lehrveranstaltungseinheiten

Einheit Datum Inhalte
1 Day 1

Topics

  • ­ Introduction to data analytics, decision making and data exploration
  • ­ Describing data, making changes to variables
  • ­ Introduction to Excel
  • ­ Case discussion

Readings

  • ­ Campbell, D.; Martinez-Jerez, F.; Epstein, M. (2006): Slots, tables, and all that jazz: Managing customer profitability at the MGM Grand Hotel, Harvard Business School Case: 9-106-029.
  • ­ Davenport, T. H. (2006). “Competing on analytics”, Harvard Business Review
2 Day 2

Topics

  • The logics behind statistical inference
  • ­Sampling and Sampling distributions
  • ­Principles of hypothesis testing: testing for differences and for associations
  • ­Exercises

 

Readings        

  • ­Levine, Stephan, Krehbiel, Berenson: Statistics for Managers Using Microsoft Excel, Chapters 8-9
3 Day 3

Topics

  • Simple bivariate and multivariate regression
  • Interpretation of regression coefficients
  • Exercises

 

Readings        

  • Levine, Stephan, Krehbiel, Berenson: Statistics for Managers Using Microsoft Excel, Chapters 10-11
4 Day 4

Topics

  • Causation and correlations
  • What if our dependent variable is a dummy?
  • Case

 

Readings        

  • Causation and Correlation, HBR Chapter
  • CASE/Exercise (TBA)
5 Day 5

Topics

  • Interaction/moderation effects using regression
  • Visualization of results and managerial implications
  • Final exercise and review

 

Readings        

  • Lecture Slides
Zuletzt bearbeitet: 25.10.2019



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