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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? It’s also necessary to understand data cleaning and processing techniques.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts.

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Top 10 Analytics And Business Intelligence Buzzwords For 2020

datapine

Predictive & Prescriptive Analytics. Predictive Analytics: What could happen? We mentioned predictive analytics in our business intelligence trends article and we will stress it here as well since we find it extremely important for 2020. Prescriptive Analytics: What should we do?

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What Is Embedded Analytics?

Jet Global

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Bottom line is that analytics has migrated from a trendy feature to a got-to-have. Their dashboards were visually stunning.