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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Analytics Insights and Careers at the Speed of Data

Rocket-Powered Data Science

Focus on specific data types: e.g., time series, video, audio, images, streaming text (such as social media or online chat channels), network logs, supply chain tracking (e.g., RFID), inventory monitoring (SKU / UPC tracking).

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

Accompanying the massive growth in sensor data (from ubiquitous IoT devices, including location-based and time-based streaming data), there have emerged some special analytics products that are growing in significance, especially in the context of innovation and insights discovery from on-prem enterprise data sources.

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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.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

Such a masterpiece is probably also a saga (the story of a journey), containing intrigues, strategies, and plots that move ingeniously, methodically, and economically (in three acts or less) toward some climactic ending (thus representing pathfinding ). Context may include time, location, related events, nearby entities, and more.

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Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

As I progressed in my career into management roles for enterprise data systems, I gained a deeper understanding and appreciation of the synergies and interdependencies between system and user requirements. Analytics products represent the user-facing and client-facing derived value from an organization’s data stores.

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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.