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Fundamentals of Data Mining

Data Science 101

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

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What is a data architect? Skills, salaries, and how to become a data framework master

CIO Business Intelligence

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. In some ways, the data architect is an advanced data engineer.

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity! Misleading statistics refers to the misuse of numerical data either intentionally or by error. 3) Data fishing. What Is A Misleading Statistic? But this didn’t come easy.

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GraphDB in Action: Smells Like Semantics Spirit

Ontotext

The question is how we can use data and cutting-edge technology to learn about the smell experiences and the perception of different smells in the past. The answer is through the wealth of object, text and image data, which serve as a reference to olfactory experiences. van Erp, M., Bembibre, C., and Leemans, I.

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Business Intelligence vs Data Science vs Data Analytics

FineReport

Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information. Typical tools for data science: SAS, Python, R.

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An Important Guide To Unsupervised Machine Learning

Smart Data Collective

k-means Clustering – Document clustering, Data mining. In data mining, k-means clustering is used to classify observations into groups of related observations with no predefined relationships. Hidden Markov Model – Pattern Recognition, Bioinformatics, Data Analytics. Source ].