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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

Every presentation I do is customized for the audience in the room. I'm going to present a cluster of what I think are digital "crimes against humanity." Making lame metrics the measures of success: Impressions, Click-throughs, Page Views. But maybe the issue is that you (and the Marketers and Leaders.

Marketing 126
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Reclaiming the stories that algorithms tell

O'Reilly on Data

Each of the classroom’s library books has a color coded sticker on its spine reflecting its Lexile score—a visual announcement of its official complexity level, and thus of which students might be officially ready to read it. This whole scoring system also changes the story about who librarians and teachers are.

Risk 356
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Data Science, Past & Future

Domino Data Lab

Paco Nathan presented, “Data Science, Past & Future” , at Rev. I am honored to be able to present here and thrilled to have been involved in Rev. He also really informed a lot of the early thinking about data visualization. This blog post provides a concise session summary, a video, and a written transcript.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

We present the inner workings of the SMOTE algorithm and show a simple “from scratch” implementation of SMOTE. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case.