Data Science Summer School

Course Outline

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 project.

The course provides training in information retrieval, data analysis and machine learning with Python and R and evolves along the thematic following units:

1. Programming with Python
   Keywords: data types, functions, objects
2. Data analysis with Python
   Keywords: pandas, database management, seaborn
3. Scraping data
   Keywords: scraping, API's, cloud storage
4. Supervised learning
   Keywords: sklearn, linear models, cross validation, regularisation, lasso, trees, ensembles, boosting
5. Unsupervised learning
   Keywords: clustering, factors, independent component analysis and matrix factorization
6. Crash course in R
   Keywords: packages, statistical analysis in R, data visualization

About the Instructors

Ioannis Arapakis is a Research Scientist at Telefónica Research, working on Information Retrieval, User Behaviour Modelling and Data Mining. Ioannis received his M.Sc. degree in Information Technology from the Royal Institute of Technology (KTH), Sweden. He then pursued a Ph.D. degree at the SoCS of the University of Glasgow, in the area of Information Retrieval. Between 2011-2015, he worked as a Researcher at Yahoo Labs and was a member of the "User Engagement", "Web Retrieval", and "Ad Processing and Retrieval" groups, where he addressed research problems related to pattern recognition, predictive modelling, multimedia mining and search, social media analysis, and machine learning. He has several publications in top-tier venues and journals, including a SIGIR best paper award, and he is a regular PC member of conference venues such as SIGIR, ECIR, CIKM, and WWW.

Omiros Papaspiliopoulos is ICREA Research Professor at UPF. He is the Scientific Director of the Barcelona GSE Master's Degree in Data Science. His research has appeared in the top journals in Statistics, including several articles in the Journal of the Royal Statistical Society Series B, Biometrika and the Annals of Statistics. He has been an Associate Editor for the first two journals and a Deputy Editor for Biometrika. He has delivered more than 80 invited talks, and has given courses at ENSAE in Paris, the Berlin Mathematical School, the Department of Mathematics at University of Copenhagen, and the Engineering Department at Osaka University. In 2010 he was awarded the Royal Statistical Society's Guy Medal in Bronze. His research interests include Monte Carlo Methods, Computational Methods, Bayesian Statistics, Stochastic Processes, Machine Learning.

Nandan Rao is a product of the Barcelona GSE Master's Degree in Data Science (Class of 2017) and is currently pursuing his long-time dream of becoming an economist, working towards a PhD at Universitat Pompeu Fabra. Previously, he was Head of Engineering at machine-learning startup Relink Labs in Copenhagen, taught Fullstack Javascript programming at Codeworks, and put data science in production with research projects at the IOM, the OECD, and The World Bank. Before all of that, he made movies. 

* Laptop required In order to participate, you must bring your own portable computer.

Applicants to all Summer School programs should meet the basic entry requirements. In addition, Data Science participants are expected to have a basic knowledge of linear algebra, basic computing skills, and familiarity with any kind of programming language (not necessarily R, Python).

For this course, previous knowledge of Bayesian statistics is not required, the first day of the course will provide an authoritative overview of all key ideas. However, participants are strongly encouraged to have studied Chapters 1-2 (minimally) and also Chapter 3 (ideally) of Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014, 3rd edition). Bayesian data analysis (Vol. 2). Boca Raton, FL: CRC press, or another textbook that covers this type of material. The course will be based on R code that will be provided to participants, hence prior knowledge of R is not compulsory either. Again, we strongly encourage participants completely unfamiliar with R to go through a tutorial such as this very short intro by Torfs and Brauer. Or those already familiar with basic concepts to look into a more advanced such as this one by Venables, Smith, and the R Core team.

Course Outline

In the course we develop the following main components of modern statistical methodology: 

1. high-dimensional regression and estimation of treatment effects in presence of very large number of instruments. We cover both modern optimization-based penalized likelihood approaches, such as the lasso, but also probabilistic inference approaches such as Bayesian variable selection and model averaging
2. Probabilistic models for high-dimensional data, such as latent variable models (factor models, independent component analysis, latent topic models), hierarchical models and graphical models. 
3. Advanced computational methods for statistical learning with models as in i and ii, such as Markov chain Monte Carlo and variational Bayes. 
4. We also develop one successful approach to learning content from text data, known as topic modelling, which is an instance of the high-dimensional Bayesian models we discuss in i, ii that computationally relies on the methods covered in iii. 

Alongside the presentation of these statistical tools, we will discuss economic applications that use them, for example treatment effect estimation and central bank communication. The course includes also lab sessions, delivered by the three instructors, that provide a hands-on experience on the material. 

About the Instructors

Stephen Hansen is Associate Professor in the Department of Economics at the University of Oxford and a Fellow of University College. He was previously Assistant then Associate Professor of Economics at Pompeu Fabra University after receiving his PhD in Economics from the London School of Economics in 2009. He serves as an academic consultant to the Bank of England, a Turing Fellow at the Alan Turing Institute, and is an Associate of the Centre for Economic Policy Research, CESifo, and the Oxford Man Institute. A main line of his research addresses the organization of monetary policymaking institutions, and his recent work has developed the use of machine learning algorithms for text data to answer questions in this area. His academic papers have been published in leading international journals, including the Quarterly Journal of Economics, Review of Economic Studies, Journal of Monetary Economics, and Journal of International Economics.

Omiros Papaspiliopoulos is ICREA Research Professor at UPF. He is the Scientific Director of the Barcelona GSE Master's Degree in Data Science. His research has appeared in the top journals in Statistics, including several articles in the Journal of the Royal Statistical Society Series B, Biometrika and the Annals of Statistics. He has been an Associate Editor for the first two journals and a Deputy Editor for Biometrika. He has delivered more than 80 invited talks, and has given courses at ENSAE in Paris, the Berlin Mathematical School, the Department of Mathematics at University of Copenhagen, and the Engineering Department at Osaka University. In 2010 he was awarded the Royal Statistical Society's Guy Medal in Bronze. His research interests include Monte Carlo Methods, Computational Methods, Bayesian Statistics, Stochastic Processes, Machine Learning. 

David Rossell is Ramón y Cajal Fellow at UPF. Previous appointment include the Biostatistics Dept. at MD Anderson Cancer Center (Houston, USA), IRB Barcelona (Spain) as head of Biostatistics & Biostatistics Unit and the Dept. of Statistics at the University of Warwick as Assistant/Associate professor. His work combines methodological research, such as theory, computation and the development of modelling techniques, and a substantial amount of inter-disciplinary work in biomedicine, chemistry and the social sciences. Specific research interests include high-dimensional inference, experimental design, dimensionality reduction and applied statistical modelling, with emphasis on the Bayesian approach. He authored over 35 peer-reviewed publications and has developed a number of R packages implementing statistical methodology. He has taught undergraduate and graduate courses at the University of Warwick, the Oxford-Warwick OxWaSP PhD program for Big Data and at UPF, shorter courses workshops at various universities, and >70 invited talks at international conferences and workshops.

* Laptop required In order to participate, you must bring your own portable computer.

About the Instructors

Alexandros Karatzoglou is the Scientific Director at Telefonica Research, his research focuses on Machine Learning. Alexandros received his PhD in Machine Learning from the Vienna University of Technology (TUWIEN). During his PhD he was a frequent visitor to the Statistical Machine Learning group at the ANU/NICTA in Canberra Australia. He has over 50 papers in the field and has won 3 best paper awards at the ACM RecSys and ECMLPKDD conferences. He is also the author of the core machine learning R package kernlab, and enjoys giving lectures on Machine Learning, Recommender Systems and Computational Statistics.

Ilias Leontiadis is currently a Research Associate at Telefonica Research. In the past he was a researcher at University of Cambridge and received his PhD from University College London (UCL). His research interests include mobile systems, pervasive computing, wireless networks, sensor networks, mobile phone privacy and mobility modeling.