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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics.

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ChatGPT: le nuove sfide della strategia sui dati nell’era dell’IA generativa

CIO Business Intelligence

Le aziende italiane investono in infrastrutture, software e servizi per la gestione e l’analisi dei dati (+18% nel 2023, pari a 2,85 miliardi di euro, secondo l’Osservatorio Big Data & Business Analytics della School of Management del Politecnico di Milano), ma quante sono giunte alla data maturity?

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Decoding Data Analyst Job Description: Skills, Tools, and Career Paths

FineReport

Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptive analytics: Assessing historical trends, such as sales and revenue.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Broadly, there are three types of analytics: descriptive , prescriptive , and predictive. The simplest type, descriptive analytics , describes something that has already happened and suggests its root causes. This data is gathered into either on-premises servers or increasingly into cloud data warehouses and data lakes.