For me, tunnel vision happened when my research was going through a rough patch. At the very worst of times, feelings of resignation over science in general, started creeping in. It was during these low points that I learned one of the most valuable lessons in all of grad school — sometimes you need to take a step back, breathe, and remember why you are in science in the first place.
I began to step outside of my research field by reading articles, listening to podcasts , and talking to colleagues. It was a reminder of why I fell in love with science in the first place. Feeling inspired, I felt ready to go on with my own research. I discovered a new found love of science communication.
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I picked up on different presentation and communication elements that I could adapt to my own presentation style. The more articles and podcasts I exposed myself to, the more ideas I had about my own science communication. So where did stepping out of the research tunnel take me? I started a blog , picked up more writing and communication opportunities, and am now thinking about a potential career in a communications-related field.
Computational notebooks for data science have exploded in popularity in recent years, and there's a growing consensus that the notebook interface is the best environment to communicate the data science process and share its conclusions. We've seen this growth firsthand; notebook support in Mode quickly became one of our most adopted features since launched in This growth trend is corroborated by Github, the world's most popular code repository.
The amount of Jupyter then called iPython notebooks hosted on Github has climbed from , in , to almost two million today. Data from the nbestimate repository shows that the number of Jupyter notebooks hosted on GitHub is growing exponentially :. This trend begs a question: What's driving the rapid adoption of the notebook interface as the preferred environment for data science work? The notebook interface draws inspiration unsurprisingly from the research lab notebook.
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In academic research, the methodology, results, and insights from experiments are sequentially documented in a physical lab notebook. This style of documentation is a natural fit for academic research because experiments must be intelligible, repeatable, and searchable. As research began to transition to computational environments, the lab notebook underwent a virtual transformation. The first computational "notebook" interface was introduced almost 30 years ago in the form of Mathematica. But what, exactly, differentiates a notebook interface from any other programming environment?
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What makes it better than the shell, a text editor, or an IDE? While having many differences both visually and linguistically, the current landscape of notebook interfaces all share a common design pattern:. So why has this design pattern driven such widespread adoption of the notebook interface in data science applications?
As a scientific practice, data science draws much of its established processes from the scientific method. In her book Doing Data Science , Cathy O'Neill claims that the data science process is really just an extension of the scientific method:.
Similar to the scientific method, data science is largely an exploratory and iterative process. You deconstruct your problem space into logical chunks worthy of independent exploration and iteratively build towards a more refined and nuanced conclusion. This approach makes it especially important for data science code to be modular and repeatable.
The notebook interface addresses this need by allowing you to break down what would otherwise be monolithic scripts into modular collections of executable code. This enables the user to run code blocks and examine their respective outputs independently from the rest of the notebook. This functionality has become critical for managing cognitive and computational complexity in increasingly elaborate data science workflows.
When examining the differences between the scientific method and Cathy O'Neill's proposed data science process, we see an increased emphasis on one final step; communicating results. In his keynote address at JupyterCon , William Merchan of datascience. He goes on to claim that this disconnect between potential and realized business impact stems from the fact that communicating and delivering insight from data science work is becoming nearly impossible to orchestrate.
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This is because as data science continues to become a more critical function in every industry, the number of stakeholders whose jobs are dependent on the output of data science work continues to increase. As the number of these stakeholders increases, the data literacy gap widens.
Effectively delivering and communicating tangible insight from data science work becomes increasingly more challenging for the modern data team. The notebook interface addresses the inherent communication challenges in data science by leveraging a style of programming called Literate Programming.