Needs of data teams are mostly around data access and sharing; Columnar databases are often more efficient for analytics; MS Excel is useful at many scales; Stakeholder communication is important to make your work more relevant; Use metrics to get to know how data products are being used.
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Do you often get confused with terms like maptlotlib, pyplot, pylab, figures, axes, gcf, gca, etc and wonder what they mean? Matplotlib is the toolkit, PyPlot is an interactive way to use Matplotlib and PyLab is the same thing as PyPlot but with some extra shortcuts.
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Some highlights of the Podcast Episode with Dr. Joseph Konstan on interesting topics related to Recommender Systems. Discussed topics include serendipity, serpentining, diversity and temporal effects.
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Data Provenance (also called Data Lineage) is version control for data. It refers to keeping track of modifications to datasets you use and train models on. This is crucial in data science projects if you need to ensure data quality and reproducibility.
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Netflix never really used the #1 winning solution to the Netflix Challenge. Some of the reasons were that just wasn't cost-effective to implement the full thing and another was that requirements had changed.
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The Instacart "Market Basket Analysis" competition focused on predicting repeated orders based upon past behaviour. Among the best-ranking solutings, there were many approaches based on gradient boosting and feature engineering and one approach based on end-to-end neural networks.
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Ensemble learning refers to mixing the outputs of several classifiers in various ways, so as to get a better result than each classifier individually.
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There are multiple ways to measure your model's performance in machine learning, depending upon what objectives you have in mind. Some of the most important are Accuracy, Precision, Recall, F1 and AUC.
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