The-Almost-Astrophysicist
The speaker, a data scientist, highlights two common reasons why individuals fail at being good data scientists. Firstly, many data scientists don't involve themselves in consulting or advocating for their projects and limit their growth, and secondly, many have difficulty communicating complex concepts and business value to non-technical stakeholders. The speaker stresses that being a good data scientist requires one to continually learn, understand data infrastructure, build models, and communicate work effectively. They recommend seeking out different projects and feedback, as well as emphasizing the importance of enjoying the role and feeling like one is making an impact.
In this section, the speaker, who has been a data scientist for the last three years, shares her observations on why some people fail at being good data scientists. She points out that the first distinguishing factor is the lack of willingness to learn and suggests that data scientists need to be continually learning to avoid being siloed into one particular area. She explains that being part of the end-to-end process is the best thing about being a data scientist and that to be a good one, data scientists need to understand data infrastructure, know how to map solutions to potential models, know how to build models, and understand the math behind them. Additionally, the speaker recommends seeking to work on different types of projects and talking to other data analysts and machine learning engineers to learn as much as possible. She concludes by emphasizing that data science is a very creative and impactful role, and if one is not enjoying it or feeling like they're making an impact, they might be at the wrong company.
In this section, the speaker discusses two main reasons why most people fail at being a good data scientist. The first reason is that many data scientists don't get involved in consulting or advocating for their projects, and instead, they silo themselves and limit their growth. The second reason is that many data scientists struggle to communicate complex concepts and explain the business value of their output to non-technical stakeholders. The speaker emphasizes that to be a good data scientist, one needs to have the ability to learn quickly and communicate their work effectively. They advise data scientists to get themselves out there, present their work to others, and actively seek feedback.
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