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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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Breaking down Business Intelligence

BizAcuity

The more effectively a company uses data, the better it performs. Cutting down latency or delay is now one of the most crucial elements of business intelligence strategy in present times. For business intelligence to work out for your business – Define your data strategy roadmap. Data mining.

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What is a data engineer? An analytics role in high demand

CIO Business Intelligence

Data engineers also need communication skills to work across departments and to understand what business leaders want to gain from the company’s large datasets. Data engineers must also know how to optimize data retrieval and how to develop dashboards, reports, and other visualizations for stakeholders.

Analytics 135
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What is a data engineer? An analytics role in high demand

CIO Business Intelligence

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 128
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Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

datapine

This genie (who we’ll call Data Dan) embodies the idea of a perfect data analytics platform through his magic powers. Now, with Data Dan, you only get to ask him three questions. The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality.

IT 317
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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. These two functions are nearly inseparable as we move further into a world of analytics that blends sources of varying volume, variety, veracity, and velocity. Building the right data model is an important part of your data strategy.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Big Data Hub

. With Db2 Warehouse’s fully managed cloud deployment on AWS, enjoy no overhead, indexing, or tuning and automated maintenance.  Netezza incorporates in-database analytics and machine learning (ML), governance, security and patented massively parallel processing. .