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Unlocking the Power of Better Data Science Workflows

Smart Data Collective

It doesn’t matter what the project or desired outcome is, better data science workflows produce superior results. 5 Tips for Better Data Science Workflows. Data science is a complex field that requires experience, skill, patience, and systematic decision-making in order to be successful. Adding it All Up.

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How Do Super Rookies Start Learning Data Analysis?

FineReport

For super rookies, the first task is to understand what data analysis is. Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. One is how to gain insights from the data. Data is cold and can’t speak. 6 Key Skills That Data Analysts Need to Master.

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Accelerating model velocity through Snowflake Java UDF integration

Domino Data Lab

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Companies like Google [2], Amazon [3], and Microsoft [4] have all published scholarly articles on this topic. In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a.

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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. Data mining for direct marketing: Problems and solutions. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. References.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

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