Learn about the importance of aggregating and restructuring data to the creation of predictor variables.
- [Narrator] In my experience,…some of the most important variables get generated…when you convert a very tall transactional data set…into a case level data set,…but you lose a lot of information when you go…from a lot of rows to fewer rows…so what kind of information do you want to keep?…A lot of this will seem obvious…like you might do total purchases or median…or mean purchases.…What might be less obvious at first is it won't be clear…to the data scientist which of these variables…is going to work best until they're in there assessing…and exploring the data.…
So for instance, somewhat famously in statistics…means are sensitive to outliers.…The presence of just a few outliers can change the mean,…but it doesn't change the median as much.…Now an experienced data scientist will just grab a few…of these but there are some that they won't know about…until they look.…For instance, looking for something…like the number of transactions under five dollars…is not something that a data scientist chooses to do…because of statistical theory,…
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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