Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 474 for “"Data Science"”.
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The data science machine : emulating human intelligence in data science endeavors
Data scientists are responsible for many tasks in the data analysis process including formulating the question, generating features, building a model, and disseminating the results. The Data Science Machine is a automated system that emulates a human data scientist's ability to generate predictive …
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Responsible Spatial Data Science
The goal of responsible spatial data science is to encourage the design and development of spatial methods, processes, algorithms, and systems to discover spatial patterns (e.g., hotspots, colocations) that reduce adverse impacts on the communities that use them. Related work on fairness issues (F) …
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Theory-guided Data Science models
L'abstract è presente nell'allegato / the abstract is in the attachment
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Scaling collaborative open data science
Large-scale, collaborative, open data science projects have the potential to address important societal problems using the tools of predictive machine learning. However, no suitable framework exists to develop such projects collaboratively and openly, at scale. In this thesis, I discuss the …
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Data Science in Investment Management
In this thesis, titled "Data Science in Investment Management," we aim to explore the applications of data science and artificial intelligence across various dimensions of investment management, offering innovative solutions and insights to the industry. This thesis is composed of several parts, …
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Graph Analytics with Data Science Languages
In Big Data analytics, data exploded in the three Vs: Volume, Velocity, and Variety. The three Vs brought new challenges to data analysis systems, which require new approaches, tools, and algorithms to analyze data. The most complex exploration mechanism in Big Data Analytics is graphs, which are …
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Collaborative, open, and automated data science
Data science and machine learning have already revolutionized many industries and organizations and are increasingly being used in an open-source setting to address important societal problems. However, there remain many challenges to developing predictive machine learning models in practice, such …
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Causal Foundations for Pragmatic Data Science
… emphasizing the importance of causal models in science, i.e., models which describe the possible effects of actions upon a system. The work contained explores central topics in this domain, including causal discovery (learning causal models from data), causal representation learning (learning …
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Data-centric Approaches for Responsible Data Science
The abundance of data, coupled with recent advancements in computation, has revolutionized almost every aspect of human life. While the undeniable benefits of this evolution are evident and despite the promise to bring good to human life and society, data-driven technologies could instead become …
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The geographical data science of population flows
… Previous research based on interaction data for census administrative boundaries indicates urban – rural patterns in the propensity to travel which are exasperated in peripheral areas. These findings are considered to be an effect of the spatial structure of zonings and thus an …
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Data science strategies for real estate development
Big data and the increasing usage of data science is changing the way the real estate industry is functioning. From pricing estimates and valuation to marketing and leasing, the power of predictive analytics is improving the business processes and presenting new ways of operating. The field of …
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ICoN: Immersive Computational Notebook for Data Science
Computational notebooks are widely used in data science, offering an interface that integrates code, documentation, visualizations, and data within a single environment. However, as data analysis becomes increasingly complex, the traditional WIMP (Windows, Icons, Menus, Pointers) interface faces …
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Data Science for Mining Patterns in Spatial Events
There has been an explosive growth of spatial data over the last decades thanks to the popularity of location-based services (e.g., Google Maps), affordable devices (e.g., mobile phone with GPS receiver), and fast development of data transfer and storage technologies. This significant growth as …
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Large-scale optimization Methods for data-science applications
… optimization methods with the applications in data science and machine learning. In the first part, we present new computational methods and associated computational guarantees for solving convex optimization problems using first-order methods. We consider general convex optimization problem, …
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Multimedia Big Data Analytics and Fusion for Data Science
Big data is becoming increasingly prevalent in people's everyday lives due to the enormous quantity of data generated from social and economic activities worldwide. As a result, extensive research has been undertaken to support the big data revolution. However, as data grows in volume, traditional …
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