Hamdan Azhar - An Ode to Permissionless Data Science
In a recent YouTube video, Hamdan Azhar discusses the evolution of data science and the unconventional trajectory that led him into the field. His insights illuminate how the perception and practical use of data science have shifted markedly since it first gained prominence.
Azhar recalls a time when his entry into data science was driven more by curiosity than by the tools and buzzwords that now dominate resumes. He critiques the trend where aspiring data scientists focus heavily on listing technical skills like machine learning and Python libraries without articulating the problems they wish to solve. This shift, which he states is evident in job applications today, reflects a narrowing of the original vision of data science, which was more holistic and human-centric.
He cites a 2012 Harvard Business Review article that described data science as “the sexiest job of the 21st century.” In that context, the emphasis was not just on data manipulation tools, but on the potential of these tools to help answer profound questions about society, economics, and the universe. Azhar contrasts this with a contemporary data science resume cluttered with jargon and technologies that may not address real-world problems.
A numerical example underscores the ongoing challenge in data science: In reviewing 300 resumes, Azhar highlights the commonality of a standard set of technical buzzwords. Yet, he argues, understanding problems—much like solving complex equations or deriving meaningful insights—requires critical human skills that can’t be easily quantified by a simple “list of tools.”
Azhar’s personal narrative enhances his argument for what he calls “permissionless” data science. He posits that you do not need formal approval or a gatekeeper to practice data science. He points out that with today’s abundance of data—more available and accessible than ever before—individuals can engage in meaningful projects simply by tapping into their passions.
He illustrates this with a personal account where he encouraged his sister to start organizing her community instead of waiting for a formal title or requisite experience. This embodies his essential message: real impact comes from taking initiative, not just from accruing credentials.
In discussing his own career, Azhar recounts an experience from the 2012 Ron Paul campaign. Here, he transformed into the campaign’s “national statistician” not because of any traditional role or top-down assignment but because he identified a need that he could fulfill with his statistical skills. While initially making calls in a phone bank, he recognized the power of data to improve campaign strategy and lent his expertise to identify effective messaging and voter outreach. This anecdote emphasizes how data science thrives on problem-solving rather than rigid structures.
His work with emoji data further illustrates this approach. Azhar mentions that during his time at Facebook, he ventured into what many considered frivolous—the study of emojis. Faced with NLP libraries that sought to eliminate emojis, he turned this obstacle into an opportunity. He exploited the sheer volume of emoji data to derive insights, ultimately contributing to technology underpinning emoji recommendations on social media platforms.
Quantitatively, Azhar assessed emoji usage before and after major political events, such as Brexit. He noted a correlation: positive sentiments reflected with party emojis and negative sentiments with crying emojis—basic yet vital this kind of analysis captures emotional states during significant societal shifts.
His closing remarks encourage data scientists and aspirants alike to start with their passions, to pursue inquiry that matters to them, and to leverage the available data. This, he asserts, constitutes the very essence of permissionless data science—mobilizing individuals to translate raw data into meaningful narratives simply because they can.
Azhar’s talk reinforces a fundamental yet frequently overlooked point: data science is about inquiry, understanding, and human experience, beyond mere tool usage. Emphasizing practical engagement over formal pathways can lead to innovative discoveries and real transformative impacts in society.