Summary
In this chapter, we learned how to perform advanced analysis on our dataset. We took the skills learned in Chapter 7, Extracting Value, and extended our analysis to acquire a more in-depth understanding of the data. We started with spatial analysis and learned how to create spatial objects before finding the relationships between the geographic information that appear in your data.
Next, we learned the different methods for creating ML models with Alteryx. We found how to develop black-box and guided models for quickly beginning a data science project. We then saw the different methods for gaining control over the data science process using the R-based tools and taking complete control with the R or Python tools.
This chapter also concludes Part 2, Functional Steps in DataOps. First, we learned how to build a data pipeline and apply the DataOps method to create workflows. Then, we learned how to access raw datasets and process them into valuable final datasets. We have...
