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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.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. Choosing the tuning parameters for data-adaptive methods such as regression trees and MARS is the subject of a large number of research articles and books.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Their tests are performed using C4.5-generated return synthetic. Chawla et al.,

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Agile Reporting for the Manufacturing Industry: 5 Tips for Success

Jet Global

In 2001, a group of software developers got together at a ski resort in the Wasatch mountains of Utah and drew up a document they called the “Agile Manifesto.” They rejected the classic waterfall model of software development in favor of an iterative approach in which initial prototypes are delivered and tested early in the process.