Practical Data Science with Rafal Lukawiecki
How do you get started in data science? Richard chats with long time data scientist Rafal Lukawiecki about practical data science. Rafal starts out focusing on the most common data science scenarios - understanding your customer and their needs. This goal is more complicated than it appears, often the questions first asked are not the questions you'll actually need answers to. But asking them is important since it leads to information that will influence the next round of questions. The cloud has made it easier than ever to dive into data science, but the principals are still the same: This is a science, after all!
Episode 525 of the RunAs Radio podcast, hosted by Richard Campbell, titled "Practical Data Science with Rafal Lukawiecki" was published on March 29, 2017 and runs 32 minutes.
March 29, 2017 ·32m · RunAs Radio
Summary
How do you get started in data science? Richard chats with long time data scientist Rafal Lukawiecki about practical data science. Rafal starts out focusing on the most common data science scenarios - understanding your customer and their needs. This goal is more complicated than it appears, often the questions first asked are not the questions you'll actually need answers to. But asking them is important since it leads to information that will influence the next round of questions. The cloud has made it easier than ever to dive into data science, but the principals are still the same: This is a science, after all!
Episode Description
How do you get started in data science? Richard chats with long time data scientist Rafal Lukawiecki about practical data science. Rafal starts out focusing on the most common data science scenarios - understanding your customer and their needs. This goal is more complicated than it appears, often the questions first asked are not the questions you'll actually need answers to. But asking them is important since it leads to information that will influence the next round of questions. The cloud has made it easier than ever to dive into data science, but the principals are still the same: This is a science, after all!
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