Q:  What is Data-Ed Online?
A:  Free Live Data Education Webinars

Data Ed Online is the the longest running (11 years and counting) free, live webinar dedicated to data management topics

At least it is free?   Seriously we have been doing this since 2012 and are ever perfecting the format based around a 60-minute lecture, followed by a 30 minute question & answer session.

Data Ed Online is my monthly data management webinar that focuses on how to unlock business value through foundational data management practices and techniques

All webinars consist of a 60-minute presentation, followed by a 30-minute interactive Q&A session - come join the discussion!

Produced in conjunction with our long time partners at Dataversity who have the finest collection of data management educational resources on the web.

Yes - all registrants will receive a link to the recording a couple of days after each event

Dataversity sends a link to the slides within two business days after the webinar. You are always welcome to repurpose my slides with attribution! Many of them originated with you in the first place. Please let me know if you want the original source files?

Data Preparation Fundamentals

14 June 2022 19:00 UTC (2:pm NYC)

Whether you call it data munging, data cleansing, or data wrangling, everyone agrees that data preparation activities account for 80% of analysts time, leaving only 20% for analysis. Shifting this work to more specialized talent represents a major source of data analysis productivity improvements. This program ‘walks’ through the major preparation categories including collection, evaluation, evolution, access design and storage requirements. Understanding each in context also provides opportunities to develop complimentary data governance/ethics frameworks. A generalized approach is presented. 

Learning objectives:
–  Appreciate the savings that can accrue
    from transforming data preparation
    from one-off to an improvable process
-  Recognize what data preparation
    knowledge/skills your organization
    has and/or needs
-  Better know the transformations that
    data can survive as it is prepared
    to be analyzed 

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