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Part One - Making Sense of Data: Auditing, Discovery, and Acquisition

It’s easy to get caught up in a rush to start building your machine learning models. But, before you actually build them, you need to understand your goals and how to achieve them. That process begins with data.

The question is, do you have enough data to get the insights you need? And if not, how can you fill the gaps?

In part one of our new whitepaper series, Explorium Explains: Data for Machine Learning, you’ll learn:

  • Auditing: How to ensure your data is stable and relevant (page 4)

  • Discovery: Understand the data you're missing and how to find it quickly (page 11)

  • Acquisition: Fill in the gaps using a step-by-step data acquisition process (page 13)

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Next Flipbook
Part Two - Making Sense of Data Prep: ETL, Wrangling, and Data Enrichment
Part Two - Making Sense of Data Prep: ETL, Wrangling, and Data Enrichment

In part two of our series, we cover ETL, data wrangling, and data enrichment so you can ensure your data is...