# Wrap Up: Time Series Preparation[¶](https://knowledge.dataiku.com/latest/courses/timeseries-prep/course-complete.html#wrap-up-time-series-preparation "Permalink to this headline")

## Congratulations![¶](https://knowledge.dataiku.com/latest/courses/timeseries-prep/course-complete.html#congratulations "Permalink to this headline")

You’ve just completed the **Time Series Preparation** course, where you gained experience preparing time series data for analytics and modeling tasks using the Time Series Preparation plugin in Dataiku DSS. Here are a few of the main takeaways from this course:

* The **Resampling** recipe helps you to equally space, interpolate, extrapolate, and clip time series data using a number of different methods.

* The **Interval Extraction** recipe makes it possible to extract intervals of accepted values, while allowing specific deviations.

* The time series **Windowing** recipe enables you to build a wide variety of window frames, such as causal or non-causal, rectangular or weighted, and use them to create sliding aggregations.

* The **Extrema Extraction** recipe allows you to define window frames in the language of the time series Windowing recipe, but apply the aggregations only to the global extrema of a time series.

## Learn More[¶](https://knowledge.dataiku.com/latest/courses/timeseries-prep/course-complete.html#learn-more "Permalink to this headline")

Now that you can prepare time series data, you are ready to model and forecast. Be sure to check out other Academy courses and continue your ML Practitioner Learning journey with courses that cover topics like statistics, image classification, and NLP.
