Emmanuel Ameisen on the TDS podcast
Data science is about much more than jupyter notebooks, because data science problems are about more than machine learning. What data should I collect? How good does my model need to be to be “good enough” to solve my problem? What form should my project take for it to be useful? Should it be a dashboard, a live app, or something else entirely? How do I deploy it? How do I make sure something awful and unexpected doesn’t happen when it’s deployed in production? Data exploration is a critical step in the data science lifecycle, but its value is really hard to quantify. How would you know if someone failed to find interesting insights in a dataset because there weren’t any insights to be found, or because they’re not skilled enough for the job? Companies tend to bias towards assessing employees based on aspects of job performance that are easy to measure, and that bias means that data exploration is often de-prioritized. A good way around this is for companies or teams to carve out time explicitly for open-ended exploration tasks, so that data scientists don’t shy away from doing them when they’re needed.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Ihab Ilyas on the TDS podcast. Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris.
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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