Topic area: Misc
The entire data science process can be organized into multiple steps/phases, and it is helpful to establish a standardized workflow for team members to collaborate effectively and generate valuable results. In this presentation, we will provide a detailed walk-through of seven phases of the data science process.
The following seven phases of the data science process will be discussed: (1) Business Understanding, (2) Data Preparation, (3) Data Munging, (4) Model Building, (5) Model Evaluation, (6) Model Deployment, and (7) Model tracking. We will emphasize certain parts of the process that are specifically relevant and interesting to machine learning practitioners. We will not be discussing any specific machine learning techniques or the hottest new tool in market, but we will explore the data science process from a bird’s eye view. We will use some examples, take occasional detours, and dig deeper into some interesting areas to better understand how the different pieces of the data science puzzle fit together. The objective of this presentation is to introduce various steps/phases of the data science process that will help think about data science more systematically.