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The Data Visualization Design Process: A Step-by-Step Guide for Beginners

Depict Data Studio

Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations. But it can be overwhelming to get started with data visualization. If so, this step-by-step data visualization guide is for you!

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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. In their 2002 paper Chawla et al.

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

Her talk addressed career paths for people in data science going into specialized roles, such as data visualization engineers, algorithm engineers, and so on. Then calculate the variance divided by the mean to construct a metric for noise in decision-making. For kicks, try calculating this kind of metric within your own organization.