Summer School: Foundations of Data Science
The Barcelona GSE Data Science Summer School introduces participants to some of the tools and methods of Data Science
21-25 June 2021 (online version)
5-9 July 2021 (in person version)
More info an enrollment here.
Computing for data science
This is an intensive, hands-on, 1-day course that will provide participants with the computing skills necessary for information retrieval, data management, data analysis and Machine Learning. It is an 8-hour course, with a number of little projects that take place during the course, that evolves around the following sub-themes:
- Programming with Python
Keywords: data types, functions, iterates, objects, classes
- Data analysis with Python
Keywords: pandas, database management, groupby, merge
- Scraping data
Keywords: scraping, API’s, cloud storage
- Data visualization in Python
Keywords: matplotlib, seaborn
Foundations of Data Science
This is an intensive 20-hour course based on a hands-on approach using Jupyter notebooks, all material is motivated by specific information retrieval and data analysis questions and each thematic unit concludes with a small project.
The course evolves along the following thematic units:
- Supervised learning (regression and classification)
Keywords: sklearn, linear models, cross validation, regularisation, lasso, trees, ensembles, boosting, nearest neighbour methods, class imbalance, multiclass predictive models, ordinal data
- Unsupervised learning
Keywords: factors, latent variables, independent component analysis, matrix factorization, embeddings, connections to neural networks (e.g. autoencoders), multidimensional scaling, clustering, K-means, spectral clustering and graph-based methods, latent semantic analysis and topic models
The course includes project sessions where the methods and algorithms developed in the hands-on sessions are employed within the context of concrete machine learning problems that the students with the guidance of the instructor and the help of junior data scientists are going to solve end to end.
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