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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. To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

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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. JPMorgan Chase & Co.:

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. To do this at scale, you have to use AI/ML services for decision-making.

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A Guide to Data Analytics in the Travel Industry

Alation

To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations. What is big data in the travel and tourism industry? What are common data challenges for the travel industry?

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Introducing Cloudera DataFlow (CDF)

Cloudera

Process millions of real-time messages per second to feed into your data lake or for immediate streaming analytics. Streaming Analytics – Analyze millions of streams of data in real-time using advanced techniques such as aggregations, time-based windowing, content-filtering etc.,

IoT 73
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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

Do you recommend a consulting approach strategy rather than a CDO strategy? How do you think Technology Business Management plays into this strategy? Where does the Data Architect role fits in the Operational Model ? Assuming a data architect helps model and guide and assist D&A then they play a key role.