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

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. ” “Data science” was first used as an independent discipline in 2001. Both data science and machine learning are used by data engineers and in almost every industry.

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Common Problems With CPM Software

Jet Global

Rather, it represents the management framework put in place by corporate leadership to monitor and respond to important metrics. Once isolated within the finance department, CPM is now broadly employed in the form of reporting departmental metrics measured against targets. Budgeting, planning, and forecasting in finance.

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Reclaiming the stories that algorithms tell

O'Reilly on Data

In 2001, just as the Lexile system was rolling out state-wide, a professor of education named Stephen Krashen took to the pages of the California School Library Journal to raise an alarm. His system was needed because “beginning teachers and librarians” were less expert at “forecasting comprehension rates” than the algorithm was.

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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.

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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) from sklearn.neighbors import NearestNeighbors from random import randrange. return synthetic.