Exploring the Data Science Process
Building accurate and timely machine learning models is not enough. It is important to ensure that analysis is aligned with the business objective to make sure that the results are useful. 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. Over the years, there have been multiple attempts to create such a standard process for data science, e.g., CRISP-DM (Cross Industry Standard Process for Data Mining) and TDSP (Team Data Science Project). In general, they all start with a business question and end with the implementation of the model.
In this presentation, I will provide a detailed walk-through of six phases of the data science process. I will emphasize certain parts of the process that are specifically relevant and interesting to the members of the Data Hackers community. 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. I 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.
Vishal Patel is a data science consultant with over fifteen years of extensive experience in data mining and advanced analytics. He has worked as a lead data scientist across several Fortune 500 clients like McDonald’s, IBM, Sprint, Pizza Hut, Jaguar, Biogen Idec, AARP, Humana, Anheuser-Busch, Green Mountain, and Sears. Vishal holds two Master’s degrees: MS in Computer Science, and MS in Decision Sciences (emphasis on Statistics). Vishal is currently running his start-up, Derive, which focuses on automated advanced analytics products and data science services.
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RVA Data Hackers meets monthly to hear, present and discuss topics in machine learning & big data. Come on down and help us build Richmond's Data Hacking community.
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