Foundations of Data Science

An introduction to basic data science techniques using R or Python

An introduction to basic data science techniques using R or Python

Expected duration

15 to 30 h


Introduction to intermediate programming and data analysis with Python

Scraping data

Introduction to supervised learning:

  • High-d regression and classification (e.g. lasso)
  • Model selection (predictive, cross validation, information criteria, etc)
  • Tree-based models, Ensemble methods (e.g. random forests)
  • Additive models and algorithms (e.g. boosting)

Introduction to unsupervised learning

  • Singular value decomposition
  • PCA, factor models, latent semantic analysis
  • Graph-based methods: spectral clustering, diffusion maps

Data Visualization

  • Classical data viz methods: correspondence analysis, biplots, multidimensional scaling
  • Visualization in Python, visualization in R. Generation of reports/ dashboards.


  • some/moderate experience with data analysis
  • some/good background in Statistics/Econometrics
  • some experience with computing and programming: we provide an online datacamp tutorial with exercises that have to be completed before course


The material forms core of Winter School, Summer School Week I and has been taught in various institutions that collaborate with the Barcelona GSE Data Science Center, e.g. CEMFI (Madrid), Universidad de Piura, CAIXABANK, etc

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Data Sciencce Center Barcelona Graduate School of Economics Ramón Trías Fargas, 25-27 08005 Barcelona, Spain.

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