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Master's Degree in Data Science
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Visualitzant Master's Degree in Data Science per títol

Visualitzant Master's Degree in Data Science per títol

Ara mostrant els elements 3-18 de 18

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Corpus construction and social media analysis about immigration in Chile

Couble, Andrés; Schindler, Mathias; Stassinos, Kalliope (2022-06)

This thesis presents a general-purpose corpus construction methodology with Twitter data for a given political topic in a given country. It applies the methodology to immigration in Chile from November 2021 to April 2022, ...
Deep reinforcement learning for the optimization of traffic light control with real-time data

Matyja, Monika; Morera, Jordi; Wolf, Sebastian (2019)

In this thesis we develop a traffic light control agent that can manage traffic lights with the objective to reduce traffic jams, trip time and other traffic metrics in a given network using reinforcement learning. To this ...
Deep vector autoregression for macroeconomic data

Agustí, Marc; Altmeyer, Patrick; Vidal-Quadras, Ignacio (2021-07-20)

Vector autoregression (VAR) models are a popular choice for forecasting of macroeconomic time series data. Due to their simplicity and success at modelling the monetary economic indicators VARs have become a standard tool ...
Exploring user retention in enhance VR: a comprehensive analysis using predictive models and clustering

Odizzio, Catalina; Pissinis, Agostina (2023-07)

This study delves into understanding and predicting user engagement in Enhance VR, a virtual reality cognitive training application, through data-driven approaches. The dataset encompasses de-identified user data including ...
Forecasting global refugee flows: a machine learning approach using non-conventional data

De los Santos, Daniela; Frey, Eric; Vassallo, Renato (2023-08-18)

This study presents a novel forecasting framework for global refugee flows, incorporating non-conventional data sources such as Google Trends, the GDELT project event dataset, conflict forecasts, among others. Our main ...
Harnessing big data news media for conflict prediction and anticipatory decision-making

Chaves, Giovanna; Philipp, Margherita; Quiñones, Luis (2023-07)

Advances in data and computing techniques have opened possibilities for real-time and cost-efficient conflict prediction and early warning capabilities, with news-based data being utilized to generate relevant forecasts. ...
Investigation of sentiment importance on intraday stock returns

Costa, Michele; Marschall, Laurits; Mirsadeghi, Seyed Hamed; Sanctis, Alessandro de (2018)

The main goal of our Master Project is to predict intraday stock market movements using two different kinds of input features: financial indicators and sentiments from news and tweets. While the former are part of the ...
Leveraging satellite imagery to assess road quality in the Democratic Republic of the Congo

Conner Bonmatí, Miguel; Talvi Robledo, Ramón; Wielath, Dominik Johannes (2023-06)

We attempt to build a road quality classifier to detect bad roads using satellite imagery in the province of Sud-Kivu in the Democratic Republic of the Congo (DRC). Using 60 cm/pixel resolution from Google Earth, paired ...
Particle filtering for nonlinear state space models

Höllwirth, Hans-Peter (2017)

This paper studies a novel particle ﬁlter method proposed by Brownlees and Kristensen (2017) for parameter estimation of nonlinear state space models. The particle ﬁlter, named Importance Sampling Particle Filter, is tested ...
Randomized numerical linear algebra for generalized linear models with big datasets

Lange, Robert Tjarko (2017)

Data scientific questions face the fundamental trade-off between complexity, generalizability and computational feasibility. The need for quick estimation and evaluation of a vast amount of statistical models has given ...
Risk detection in cryptocurrency markets: meeting the needs of traditional finance

Aguilar, Iván; Jones, Rebecca; Lovicu, Gian-Piero (2022-05)

Financial institutions are beginning to integrate cryptocurrencies into their payment systems but must ensure to comply with anti-money laundering regulations to avoid facilitating transactions linked to criminal activities. ...
Scalable inference for crossed random effects models

Müller, Maximilian (2020-08-17)

In order to apply statistical learning in the framework of crossed random effects models it is necessary to efficiently compute the Cholesky factor L of the models precision matrix. In this paper we show that for the case ...
Scalable inference for Gaussian hierarchical models

Hao, Kwa Jie (2018)

Hierarchical modeling is a practical approach with proven results in modeling real world data. This paper studies Gaussian hierarchical models and methods which exploit the sparse conditional independence structure of such ...
Structure and power dynamics in economic networks : a quantitative analysis of labour flow and company control networks in the UK

Pap, Aron (2020-06-25)

In this thesis project I analyse labour flow networks and company control networks in the UK. I observe that these networks exhibit characteristics that are typical of empirical networks, such as heavy-tailed degree ...
Tracking the economy using FOMC speech transcripts

Battaglia, Laura; Salunina, Maria (2020-07-20)

In this study, we propose an approach for the extraction of a low-dimensional signal from a collection of text documents ordered over time. The proposed framework foresees the application of Latent Dirichlet Allocation ...
Understanding latent vector arithmetic for attribute manipulation in normalizing flows

Gimenez Funes, Eduard (2021)

Normalizing flows are an elegant approximation to generative modelling. It can be shown that learning a probability distribution of a continuous variable X is equivalent to learning a mapping f from the domain where X is ...