Goodbye, data science (ryxcommar.com)

Article summary

The author, a former data scientist, shares their personal experience of transitioning to a data engineer role and finding it more fulfilling. They express frustration with the data science field, citing issues such as poor management, low-value work, and a lack of understanding of good and bad data science practices. The author also criticizes the tendency for data scientists to prioritize superficial knowledge over practical skills. They find data engineering to be a more autonomous and valuable field, with a focus on building and maintaining data pipelines.

Main themes

• data science frustrations
• data engineering
• management issues
• statistical knowledge
• career transitions
• field definitions

What commenters say

• Some commenters agree that data science can be a frustrating field due to poor management and a lack of understanding of good practices.
• Others disagree, arguing that data engineering is not a clearly defined field and may come with its own set of problems.
• There is a perceived difference between data science roles in the US and UK, with the latter focusing more on applied machine learning work.
• Some commenters note that data science interviews can be overly focused on technical skills, while others argue that many data scientists lack fundamental statistical knowledge.
• The distinction between data science and data engineering is seen as blurry, with some arguing that data engineering is a more intermediate step towards backend development.
• Some commenters criticize the data science field for prioritizing buzzwords and superficial knowledge over practical skills and statistical understanding.
• The market for data science talent is seen as imperfect, with some companies hiring candidates who lack essential skills and knowledge.