Data-Nash
The video discusses the six regrets of a data scientist from their first year, emphasizing the importance of learning GitHub and Version Control, understanding the business context, having a clear project template, and becoming an effective communicator. The speaker also regrets setting unrealistic deadlines, not keeping up with the wider industry of data science, and focusing too much on data science in isolation. They suggest following data industry experts on social media platforms like Twitter, YouTube, and Medium to stay up-to-date. The speaker aims to share their journey towards becoming an elite data scientist and invites viewers to subscribe to their channel.
In this section, the speaker shares six regrets from their first year as a data scientist and touches upon the importance of learning GitHub and Version Control. The speaker points out how data scientists usually feel like solo adventurers just hacking their way through the data science forest, which can get overwhelming. Even though Version Control could be learned within a few hours, many data scientists push it off. The speaker emphasizes the importance of GitHub, especially since it can save a data scientist from an embarrassing situation. The speaker then moves on to discuss the most critical mistake they made, which was focusing too much on data science and learning it in a vacuum. Instead, the speaker recommends data scientists to learn the business context to help organizations achieve specific results using data.
In this section, the speaker expresses regret for not having a clear and rigorous template to conduct data science projects from end to end. In school, the data sets were given to them, and the problem statement would also be known. However, in the real world, the speaker found it difficult and time-consuming to figure out what data to use and which techniques to apply. The speaker recommends having a clear acceptance criteria to avoid wasting energy trying to get the model a little bit better, which does not generate business value. Additionally, the speaker emphasizes the importance of becoming an effective and engaging communicator as having good data skills is not enough to get a job as a data scientist.
In this section, the speaker shares two regrets from his first year as a data scientist. The first is about setting unrealistic deadlines, as he would agree to complete projects in a short amount of time to impress others, which would lead to disappointment and raised expectations if he missed the deadline. The second regret is not keeping up with the wider industry of data science and immersing himself in the world of data science outside of his job. He believes that learning how other people in the industry approach problems and keeping up with emerging technologies is crucial to avoid becoming stagnant. The speaker suggests following data people on Twitter, signing up to Medium, and watching data science people on YouTube as ways to keep up with the industry.
In the introduction of the video, the speaker introduces himself as a beginner data scientist who wants to share his journey towards becoming an elite. He invites viewers to join him on his journey by subscribing to his channel.
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