Statistics for Data Science


  • Jorge M. Mendes (; Office/Hours: Office 9, 2nd floor/under appointment
  • Bruno Damásio (; Office/Hours: Office 138, Colégio de Campolide /under appointment

General characterization





Responsible teacher

Jorge Morais Mendes


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Statistics and linear algebra (recommended)


  • Lock, Robin, H., Lock, P. F., Morgan, K.L., Lock, E.F.; Lock, D.F. (2017) Statistics: unlocking the power of data. Second edition, Wiley.
  • Wooldridge, J. M., Introductory econometrics: A modern approach, 6th Edition. South-Western, Cengage Learning, 2016;
  • Heiss, F., Using \textbf{R} for Introductory Econometrics, 1st Edition; CreateSpace (Independent publishing platform), 2016.
  • Greene, W. H., Econometric analysis, 7th edition, Pearson, 2012;
  • Stock, J. H and Watson, M. W, Introduction to Econometrics, 3rd. Edition, Pearson, Addison Wesley, 2011

Teaching method

The course is based on theoretical-practical and practical classes. Practical classes and problem-solving oriented.


Evaluation method

  • (50%) Final exam (1st and 2nd rounds)
  • (50%) Project (opcional)
  • Final grade: máximum (Final exam grade;0.5*(Final exam grade)+0.5*Project grade))

A minimum grade of 8.5 is required in the final exam to pass.

Subject matter

  1. Collecting data
  2. Statistical distributions
  3. Describing data
  4. Confidence intervals
  5. Hypothesis testing
  6. Inference
  7. The Nature of Econometrics. Correlation vs Causality
  8. The Multiple Linear Regression Model (MLRM)
  9. MLRM: Inference
  10. Heteroskedasticity
  11. Asymptotic properties of the OLS
  12. Quadratics and Interactions
  13. Functional Form Misspecification
  14. MLRM with qualitative information


Programs where the course is taught: