Data Science is not Science.

Summary

The speaker critiques the term 'data scientist,' expressing frustration with its overuse and questioning its legitimacy as a distinct field. They argue that data analysis is already an inherent part of many STEM professions, particularly physics, and that calling data science a separate field is unnecessary and misleading. The speaker believes that data scientists often lack the deep, subject-specific knowledge needed to fully understand the data they work with. They also criticize how universities and industries create new degrees and certificates for financial gain, leading to wage dumping and diluted expertise.

Takeaways

• 😀 Data science as a field is overhyped and doesn't always warrant the term 'science' as many people working in it simply analyze large datasets, often without conducting research.
• 😕 The speaker finds it cringe to identify as a 'data scientist' because it feels like an appropriation of the term 'scientist,' which traditionally involves research and hands-on experimentation.
• 😠 There's frustration with how corporate environments separate data analysis from data acquisition, often giving data science tasks to people outside of R&D, which can slow down research progress.
• 🤔 The speaker believes that many physicists and scientists are capable of handling data without needing a special degree in data science, as data analysis is already part of their work.
• 💼 Data science degrees and certificates are often pursued by individuals who struggle to find jobs in other STEM fields, with many using them to break into programming or higher-paying jobs.
• 🤦‍♂️ The corporate world’s tendency to create specialized roles like data science results in wage dumping, lowering pay for positions that were previously handled by more skilled scientists.
• 📚 The speaker took a data science course mainly to learn the programming language R, finding it useful for their work in physics, but believes it was unnecessary for their qualifications.
• 👩‍🔬 The trend of creating new degrees and certifications, like 'life sciences' and 'medical physics,' dilutes the significance of traditional academic fields and often results in unclear career prospects for graduates.
• 📊 The rise of data science as a separate field has caused a misunderstanding about what actual scientists and physicists are capable of, leading to inefficiencies in industries.
• 😒 The speaker criticizes universities and corporations for creating new, specialized degrees purely for financial reasons, which may not necessarily serve the students or professionals well.

Q & A

What is the speaker's main criticism of the term 'data scientist'?

The speaker believes that the term 'data scientist' is often misused, as many people who call themselves data scientists are not involved in actual scientific research. Instead, they mostly analyze data in corporate settings, which the speaker feels is not 'science' in the traditional sense.

Why does the speaker find it cringe-worthy to call themselves a data scientist?

The speaker feels uncomfortable calling themselves a data scientist because it feels like an appropriation of the term 'science.' They believe their background in physics involves a much deeper understanding of data, and referring to their work as 'data science' downplays the complexity of their actual scientific expertise.

What does the speaker suggest about the origins of data science roles?

The speaker suggests that data science roles have emerged as a way to split up research and development (R&D) departments. This shift is seen as problematic because it takes away responsibilities that were traditionally handled by scientists, like data collection and analysis, and gives them to specialized data science teams.

Why does the speaker believe that the creation of data science roles in corporations is counterproductive?

The speaker argues that outsourcing data analysis to data scientists, rather than letting the R&D team handle it, leads to inefficiencies. They claim that scientists already have the skills to analyze their own data, and outsourcing this work leads to wage dumping and unnecessary division of labor.

How does the speaker view the trend of offering data science certificates and degrees?

The speaker views data science certificates and degrees as largely unnecessary, especially for those with a background in STEM fields like physics. They argue that most of the skills taught in these programs are things they had already learned in their own scientific training.

What personal experience does the speaker share about their decision to pursue a data science certificate?

The speaker reveals that they pursued a data science certificate mainly out of interest in learning the R programming language, not because they needed it for their career. They admit that they could have learned the material on their own without the certificate.

What does the speaker think about the job market for data scientists?

The speaker believes that many people turn to data science when they can't find jobs in their original field of study, such as biology. They mention that some people see data science as a way to break into higher-paying jobs, especially when their original degree doesn't offer many opportunities.

How does the speaker view the relationship between data science and traditional STEM fields like physics?

The speaker believes that data science is just a subset of the work that scientists in fields like physics already do. In their opinion, analyzing data is just one part of the scientific process and doesn't require a separate degree or job title.

Why does the speaker criticize HR departments in relation to data science roles?

The speaker criticizes HR departments for not understanding what STEM professionals are capable of. They suggest that HR departments overvalue buzzwords like 'data science' and fail to recognize that many scientists already have the skills to handle data without needing a separate certification.

What is the speaker's broader critique of universities creating new degrees like 'life science' or 'medical physics'?

The speaker argues that universities create new degrees like 'life science' or 'medical physics' primarily to attract more funding and students, not because these fields truly need specialized degrees. They believe that such degrees often offer a surface-level education and are not as valuable as traditional STEM degrees.