Data Analysis Data Preparation Webinar 
Alation
FEBRUARY 20, 2020
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.” Get the latest data cataloging news and trends in your inbox.
Tableau
JULY 6, 2022
release includes features that speed up and streamline your data preparation and analysis. Automate dashboard insights with Data Stories. If you've ever written an executive summary of a dashboard, you know it’s time consuming to distill the “so what” of the data. Product Marketing Associate, Tableau.
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Tableau
JULY 6, 2022
release includes features that speed up and streamline your data preparation and analysis. Automate dashboard insights with Data Stories. If you've ever written an executive summary of a dashboard, you know it’s time consuming to distill the “so what” of the data. Product Marketing Associate, Tableau.
phData
JUNE 2, 2023
The Core Concepts exam covers the basic concepts of Alteryx Designer, including data input/output, basic data preparation, and data analysis. The Advanced Concepts exam covers more advanced topics such as reporting , data investigation , and spatial tools.
ODSC - Open Data Science
APRIL 13, 2023
Hands-on Data-Centric AI: Data Preparation Tuning — Why and How? Going into developing machine learning models with a hands-on, data-centric AI approach has its benefits and requires a few extra steps to achieve. Here are a few examples, including health record analysis and protein discovery. Learn more here.
Pickl AI
SEPTEMBER 5, 2024
Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. The Microsoft Certified: Azure Data Scientist Associate certification is highly recommended, as it focuses on the specific tools and techniques used within Azure.
Pickl AI
NOVEMBER 28, 2024
Data Transformation Transforming data prepares it for Machine Learning models. Encoding categorical variables converts non-numeric data into a usable format for ML models, often using techniques like one-hot encoding. Outlier detection identifies extreme values that may skew results and can be removed or adjusted.
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