Data Analysis - 2nd semester


The course Data Analysis aims to promote the numeracy of students of the Master, who, by their background, may present significant gaps in data analysis skills. Thus the course provides the principles for students to understand the fundaments of data analysis, extremely useful for any kind of activity related to information management. The course provides an understanding of the key concepts of descriptive data analysis, through theoretical presentations followed by practical hands­on exercises. It is thus intended that students develop skills in understanding and analyzing data that enables them to understand and develop autonomous descriptive analyzes.

General characterization





Responsible teacher

Elena Bucea (NOVA IMS), Frederico Jesus (NOVA IMS)


Weekly - 3 letivas + 1 tutorial

Total - Available soon

Teaching language





Newbold, P. Carlson, W.; Thorne, B. (2007) ­ Statistics for Business and Economics, 6th ed. Englewood Cliffs: Pearson International Edition. ISBN 0­
McClave, J.T; Benson, W; Sincich, T. (2008) ­ Statistics for Business & Economics, 11th Edition, Englewood Cliffs: Pearson International Edition, Prentice Hall.
Cleveland, W.S. (1993). Visualizing Data. New Jesey: Hobart Press. ISBN­13: 978­0963488404.
The Economist (2003) Numbers Guide – The Essential Of Business Numeracy, Fifth Edition, Profile Books LTD, London
Levine, D.M; Stephan, D.F.; Krehbiel C. & Berenson, M. (2003). Statistics for Managers Using Microsoft Excel, 3rd edition. Pearson Canada. ISBN

Teaching method

The teaching method is based on the following:
1) classes of oral presentation by the teacher ;
2) practical exercises solved by the students ;

Evaluation method

The evaluation is based on a group project (40%) and a written assignment one hour exam (60%).

Subject matter

1. The purpose and tasks of statistics and data analysis
2. Statistical lies, ambiguities and misuse of statistics
3. Statistical variables and types of data
4. The standard tools of descriptive data analysis for one variable
4.1. Distribution of frequencies and histograms
4.2. Central tendency indicators
4.3. Dispersion indicators
4.4. Location indicators
4.5. Other indicators
5. The standard tools of descriptive data analysis for two variables
5.1. Scatter diagrams
5.2. Covariance and correlation
5.3. The Spearman R
5.4. Concentration measures
6. Graphical representation of data
7. The index numbers
8. Basic references on sampling and survey methodology
9. Quality in statistics and data analysis


Programs where the course is taught: