Explore some examples of assessing data. It's similar to counting apples and oranges.
- [Instructor] Now what I would like to do is…kind of invite you to watch my thought process…as I assess a data set from the modeler's point of view.…What this is going to do is give you an idea…of why the modeler has to have access…to such a huge amount of data…in the early part of the process,…while they're trying to figure out…what the final form of the data will be.…The first thing we're going to do…is focus in on the number of transactional rows per case,…so let's take a quick look at some transactional data.…
Now, I may even do this visually at first,…like I might look to see that this customer…has just a few transactions,…but obviously I'm combining this with queries;…I can see this customer has many more.…Here are some of the questions…that I'm asking at this point:…What is the maximum number of rows…that I'm finding for any particular customer ID?…And I might have millions or tens of millions…of transactions that I'm looking at,…or even more.…I'm also curious if there are any case IDs…that appear in my transactional data…
Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Seasonality and time alignment
- Data preparation challenges
- Data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
Skill Level Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
Defining terms1m 48s
1. The Phases of a Machine Learning Project
2. Designing a Machine Learning Dataset
3. Data Prep Challenges
4. Modeling Challenges
7. Monitoring and Maintenance
Next steps1m 1s
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