Discover how to handle seasonal data and explore the challenge of time alignment during data preparation.
- [Instructor] Now we're going to get into…a topic that can become very technical…and even somewhat confusing.…But gosh, it's an important one.…We have to talk about seasonality,…the notion of a cohort, and time alignment.…In the majority of projects, you're going to…end up looking at a year's worth of data.…Why?…If you look at less than a year,…there's all kinds of seasonal effects…that you might not detect.…For instance, if we were looking…at a bunch of cellphone monthly statements,…we have dads and grad specials in June,…and we have Black Friday specials around the holidays,…and we don't know how that's going to…impact people upgrading their phones…and changing their behaviors in certain ways.…
So looking at less than a year is risky.…If a year is better than a month,…why isn't 10 years even better than one year?…Technologies come and go, phone…plans get discontinued, going back 10 years…now, our data becomes very heterogeneous.…So one year is often a balance…between these two extremes.…Let's take a closer look.…
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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