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

IBM Big Data Hub

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. ” “Data science” was first used as an independent discipline in 2001.

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11 Digital Marketing “Crimes Against Humanity”

Occam's Razor

Making lame metrics the measures of success: Impressions, Click-throughs, Page Views. Use metrics that matter: Loyalty, Recency , Net Profit, Conversation Rate, Message Amplification , Brand Evangelist Index , Customer Lifetime Value and so on and so forth. Making website iterations based on executive opinions, but not site testing.

Marketing 126
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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities. These metrics help agents improve their call handle time and also reallocate agents across organizations to handle pending calls in the queue.

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Reclaiming the stories that algorithms tell

O'Reilly on Data

Under school district policy, each of Audrey’s eleven- and twelve-year old students is tested at least three times a year to determine his or her Lexile, a number between 200 and 1,700 that reflects how well the student can read. They test each student’s grasp of a particular sentence or paragraph—but not of a whole story.

Risk 355
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. return synthetic. Chawla et al.,