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Business intelligence data mining News
April 01, 2019
Machine learning platforms can bring the lone data scientist into the overall workflow. Updates to tooling also let that data scientist use familiar development interfaces.
March 23, 2018
Ethical data collection practices are becoming even more important, as cases like Cambridge Analytica's misuse of Facebook data challenge consumer trust in enterprise analytics.
November 21, 2017
Microsoft Connect(); 2017 saw the addition of MariaDB and Cassandra to the Azure cloud database lineup. Also discussed: A set of Spark-based analytics services called Azure Databricks.
November 09, 2017
Python and R are among the tools in the SQL Server machine learning toolkit. Native T-SQL scoring is also on the agenda, as uncovered at PASS Summit 2017.
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As data analytics have increasingly become a core component of organizations' strategies, concerns have arisen around how data is mined. Experts offer tips. Continue Reading
Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. Continue Reading
Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Continue Reading
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The rise of augmented analytics empowers businesses to find correlations in data that might very well be the critical difference in operational efficiency and cost savings. Continue Reading
Data science and business analyst roles differ in that data scientists must deep dive into data and come up with unique business solutions -- but the distinctions don't end there. Continue Reading
Learn about the features and components of the Microsoft R family of predictive analytics tools, which includes Microsoft R Open, Microsoft R Client and Microsoft R Server. Continue Reading
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How can organizations leverage social media analytics data? Analytics professionals offer tips, starting with picking metrics that track to your goals. Continue Reading
Customer data analytics are easy to gather in the social media era -- but they can be misleading if based on sentiment analysis culled from automated social media monitoring. Continue Reading
Democratizing data analytics gives everyone access to tools and information, but data literacy is still required for analyzing data and delivering successful outcomes. Continue Reading
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Having data scientists build analytical models doesn't do much for an organization if the models don't help generate business benefits. Here are some ways to make sure they do. Continue Reading
Big data environments based on technologies such as Hadoop and Spark are being deployed more widely -- and the same goes for advanced analytics tools that can help organizations make effective use of the data flooding into those systems. In fact, ... Continue Reading
Many modern consumers and workers are always tuned-in to the digital world. They have laptops, tablets and smartphones that chirp and beep away with incoming messages. Those devices are also sending out information -- tweets and texts, yes -- but ... Continue Reading