Differences

This shows you the differences between two versions of the page.

--- other:inspect3d:documentation:knowledge_discovery:knowledge_discovery_in_inspect3d [2024/07/17 15:44] – created sgranger
+++ other:inspect3d:documentation:knowledge_discovery:knowledge_discovery_in_inspect3d [2026/04/13 20:38] (current) – ↷ Links adapted because of a move operation 43.156.247.77
@@ Line 21: / Line 21: @@
 Complete the [[Other:Inspect3D:Tutorials:Clean_your_Data|tutorial]] for cleaning your data to learn more about these processes.
-\\
@@ Line 35: / Line 32: @@
 Read about how to query [[Other:Inspect3D:Documentation:Definitions:Subject#Querying_based_on_Subject|multisubject]] data.
-\\
 ===== Performing analysis =====
-Having queried your clean dataset to get exactly the traces and metrics that you wanted, now you're finally ready to start analysing. We learn a lot about analytical techniques in our courses and throughout our formal training, but it's only one of the five steps here. Even though this is what we often think of as the difficult work of research, you've already put in a lot of effort to get through the first three steps and get to this point! The type of analysis you perform is obviously going to depend on the question you're trying to answer and the dataset that you have. Inspect3D implements a range of common data analysis techniques such as summary statistics calculation, [[Sift:Principal_Component_Analysis:Principal_Component_Analysis|principal component analysis (PCA)]], and clustering algorithms, with new techniques being added. Sometimes your analysis will prompt new questions or new ways of looking at your data. Don't be afraid to go back to collecting or shaping your data to see what else you might find.
+Having queried your clean dataset to get exactly the traces and metrics that you wanted, now you're finally ready to start analysing. We learn a lot about analytical techniques in our courses and throughout our formal training, but it's only one of the five steps here. Even though this is what we often think of as the difficult work of research, you've already put in a lot of effort to get through the first three steps and get to this point! The type of analysis you perform is obviously going to depend on the question you're trying to answer and the dataset that you have. Inspect3D implements a range of common data analysis techniques such as summary statistics calculation, [[sift:principal_component_analysis|principal component analysis (PCA)]], and clustering algorithms, with new techniques being added. Sometimes your analysis will prompt new questions or new ways of looking at your data. Don't be afraid to go back to collecting or shaping your data to see what else you might find.
 Complete the [[Other:Inspect3D:Tutorials:Perform_Principal_Component_Analysis|tutorial]] for performing PCA to see some of the different ways you can analyse your data.