www.teradata.com/vantage

Are you a Data Scientist who love using Python and Jupyter Notebook but having a difficulty building performant analytics and machine-learning at scale? Don’t despair - Teradata Vantage and Teradata Package for Python (teradataml) are to the rescue. They enable performant execution of complex analytics on large datasets, while using your favorite data science tool and language.

In this second episode in Using Python with Vantage TechBytes series, Alexander Kolovos demonstrates how to use Vantage and teradataml for data exploration and transformations as well as to build an analytic data set (ADS).

Get a full understanding of the latest features offered by teradataml and how Vantage coupled with teradataml can drive faster time to value through this TechBytes series:
• Part 1. Introduction and Connections [[ Ссылка ]]
• Part 2. Data Exploration and Transformations | Building an Analytic Data Set (ADS) [[ Ссылка ]]
• Part 3. Analytic Functions Modeling and Model Cataloging [[ Ссылка ]]
• Part 4a. In-Database scripting with SCRIPT Table Operator - Scoring with External Model [[ Ссылка ]]
• Part 4b. In-Database scripting with SCRIPT Table Operator - Micromodeling (multiple model training and scoring) and Map Functions [[ Ссылка ]]

Download Jupyter notebook used in the demonstration from a Teradata GitHub site: [ Ссылка ]

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