Search Results - subject:"Data Science"

Herausforderungen des maschinellen Lernens in der praktischen Prüfarbeit

Bösiger, Sven - 2023

Persistent link: https://www.econbiz.de/10014286076

Data Science for Entrepreneurship : Principles and Methods for Data Engineering, Analytics, Entrepreneurship, and the Society

Liebregts, Werner (ed.); Heuvel, Willem-Jan van den (ed.); … - 2023

The Unlikely Wedlock Between Data Science and Entrepreneurship -- Data Engineering: Big Data Engineering -- Data … Marketing -- Data and Society: Data Protection Law and Responsible Data Science -- Perspectives from Intellectual Property Law … -- Liability and Contract Issues Regarding Data -- Data Ethics and Data Science -- Value Sensitive Software Design -- Data Science …

Persistent link: https://www.econbiz.de/10014239918

Using satellites and phones to evaluate and promote agricultural technology adoption : evidence from smallholder farms in India

Cole, Shawn; Killeen, Grady; Krishna, Aparna - 2025

Persistent link: https://www.econbiz.de/10015197085

Dynamic personalization with multiple customer signals : multi-response state representation in reinforcement learning

Ma, Liangzong; Huang, Ta-Wei; Ascarza, Eva; Israeli, Ayelet - 2025

Persistent link: https://www.econbiz.de/10015197090

Green Bond Returns and the Dynamics of Green and Conventional Financial Markets: An Analysis Using a Thick Pen

Gronwald, Marc; Wadud, Sania - 2025

This paper explores the relationship between green bond markets and both green and conventional financial markets, while also evaluating their effectiveness as a climate finance instrument. Using the Thick Pen Measure of Association — a visually interpretable tool for analysing co-movement...

Persistent link: https://www.econbiz.de/10015398790

Green bond returns and the dynamics of green and conventional financial markets : an analysis using a Thick Pen

Gronwald, Marc; Wadud, Sania - 2025

This paper explores the relationship between green bond markets and both green and conventional financial markets, while also evaluating their effectiveness as a climate finance instrument. Using the Thick Pen Measure of Association - a visually interpretable tool for analysing co-movement...

Persistent link: https://www.econbiz.de/10015339840

An uncertain elite: Professional differences and similarities between engineers and tech workers in times of digital transformation

Krzywdzinski, Martin; Pfeiffer, Sabine; Kuhlmann, Martin; … - In: Work Organisation, Labour & Globalisation 19 (2025) 1, pp. 84-104

The digital transformation of industries has given rise to new categories of tech workers, such as software engineers and UX/UI designers, who now work alongside traditional engineers. This study explores the evolving relationship between these groups, focusing on work processes, status...

Persistent link: https://www.econbiz.de/10015374116

An analysis of the challenges in the adoption of MLOps

Amrit, Chintan - In: Journal of innovation & knowledge : JIK 10 (2025) 1, pp. 1-16

The field of MLOps (Machine Learning Operations), which focuses on effectively managing and operationalizing ML workflows, has grown because of the advancements in machine learning (ML). The goal of this study is to examine and contrast the difficulties encountered in the implementation of MLOps...

Persistent link: https://www.econbiz.de/10015192330

The evolution of electric and hybrid vehicles and their influence on sustainable transport : a review and future research lines

Delso-Vicente, Alberto-Tomas; Camperos, Marisol-Carvajal; … - In: Sustainable technology and entrepreneurship 4 (2025) 2, pp. 1-23

This study presents a systematic review of the academic literature on electric and hybrid vehicles, their ecosystems, and supporting infrastructure over the period 2008-2024. Using the PRISMA methodology, the research evaluates related studies, exploring trends and developments in charging...

Persistent link: https://www.econbiz.de/10015409116

Business analytics : methods and cases for data-driven decisions

Huntsinger, Richard - 2025

. This is the book for upper division and graduate business students with interest in data science, for data science students …

Persistent link: https://www.econbiz.de/10015189466