De – The Financial Times Guide to Data Transformation

Argomenti del programma: The content of the course is organized in five sections: 1. Analytics for Business Value: Covers data analytics value frameworks, enablers for business analytics, and creating organizational roadmaps for value creation. 2. Applied Machine Learning: Teaches descriptive and machine learning analytics, supervised and unsupervised learning methods, and advanced analytics topics. 3.

Argomenti del programma: - Data Analytics Frameworks and Tools: Introduction to data analytics frameworks, low-code tools like KNIME and PowerBI, and foundational machine learning concepts. - Supervised and Unsupervised Machine Learning: Techniques including regression, classification, clustering algorithms, and customer segmentation, tailored for marketing applications.

Argomenti del programma: - Data Analytics Frameworks and Tools: Introduction to data analytics frameworks, low-code tools (including Knime, Power BI, and Langflow), and foundational machine learning concepts. - Supervised and Unsupervised Machine Learning: Techniques including regression, classification, clustering algorithms, and customer segmentation, tailored for marketing applications.

Argomenti del programma: - Framework e strumenti di Data Analytics: introduzione ai framework di data analytics, agli strumenti low-code (tra cui KNIME, Power BI e Langflow) e ai concetti fondamentali di machine learning. - Machine Learning supervisionato e non supervisionato: tecniche di regressione, classificazione, algoritmi di clustering e segmentazione della clientela, adattate per applicazioni di marketing.