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Data Preparation in R Cheatsheet

KDnuggets

Leverage the powerful data wrangling tools in R’s dplyr to clean and prepare your data.

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Migrate Amazon SageMaker Data Wrangler flows to Amazon SageMaker Canvas for faster data preparation

AWS Machine Learning Blog

Amazon SageMaker Data Wrangler provides a visual interface to streamline and accelerate data preparation for machine learning (ML), which is often the most time-consuming and tedious task in ML projects. Charles holds an MS in Supply Chain Management and a PhD in Data Science. Huong Nguyen is a Sr.

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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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Speed up Your ML Projects With Spark

Towards AI

As a Python user, I find the {pySpark} library super handy for leveraging Spark’s capacity to speed up data processing in machine learning projects. But here is a problem: While pySpark syntax is straightforward and very easy to follow, it can be readily confused with other common libraries for data wrangling.

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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning Blog

Choose Data Wrangler in the navigation pane. On the Import and prepare dropdown menu, choose Tabular. You can review the generated Data Quality and Insights Report to gain a deeper understanding of the data, including statistics, duplicates, anomalies, missing values, outliers, target leakage, data imbalance, and more.

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How do you make self-service data analysis work for your organization?

Alation

On August 25 at 11am PDT, Forrester’s VP and Research Director, Gene Leganza, Alation’s Head of Product, Aaron Kalb, and Trifacta’s Director of Product Marketing, Will Davis, will hold a webinar to discuss “Achieving Productivity with Self-Service Data Preparation.”

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Data Transformation and Feature Engineering: Exploring 6 Key MLOps Questions using AWS SageMaker

Towards AI

To prepare the data for models, a data scientist often needs to transform, clean, and enrich the dataset. Fortunately, SageMaker’s data-wrangling capabilities allow data scientists to quickly and efficiently transform and review the transformed data.

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