Overall Analysis Process#

The data analytics process on the platform typically follows a well-defined iterative procedure. This includes the formulation of goals, planning implementations, executing pipelines, and interpreting results. The entire process can also be automated.

The standard steps are as follows:

  1. Goals and Subgoals: Define the overall objective of the analysis and break it down into manageable subgoals. See Goals and Subgoals.

  2. Implementation Plan: Each goal is associated with a set of implementation steps, automatically generated and editable. See Implementation Plans.

  3. Pipeline Generation: Based on the implementation plan, the system generates a Python analysis pipeline. See Pipelines.

  4. Pipeline Execution: The pipeline is executed, and results are captured and displayed. See Pipeline Execution and Results.

  5. Result Interpretation: Results are analyzed in the context of domain knowledge. See Result Interpretation.

Each goal may be executed through multiple iterations. Each iteration investigates a single goal but multiple iterations can be run for comparison and refinement.