The author discusses their reasons for learning JavaScript as a data scientist. They highlight two main reasons: building visualizations with D3.js and becoming a “full stack data scientist.” They argue that learning JavaScript expands their programming skills and allows them to work with different parts of the tech stack. They acknowledge that JavaScript may not be directly relevant to data science but believe it is valuable to their overall skillset.
**Why I’m Learning JavaScript as a Data Scientist**
In the world of Data Science, Python has been the go-to language for quite some time. But did you know that JavaScript can also be a valuable tool for Data Science? In this article, I’ll explain why I’m investing in JavaScript and why it feels like I’m venturing into unfamiliar territory.
**Reason #1: Building Cool Visualizations with D3.js**
JavaScript has a powerful library called D3.js that allows you to create stunning data visualizations. One of my favorite use cases is scrollytelling, which combines scrolling and storytelling. With D3.js and Scrollama.js, you can create interactive charts and animations that change as the user scrolls through a webpage. Check out this example I built using public data from Google’s Covid-19 Community Mobility Reports: [link]
What makes D3.js even more appealing is its ability to directly deploy visualizations onto a website. Unlike other tools like matplotlib or Power BI, D3.js doesn’t require an additional tool to publish your visualizations online. Plus, D3 visualizations are highly customizable and can be embedded into webpages using a simple HTML tag.
**Reason #2: Becoming a “Full Stack Data Scientist”**
As a Data Scientist, you often rely on other teams to make your work actionable. You need data engineers to provide raw data, ML engineers to deploy models, and software engineers to integrate those models into customer-facing products. To overcome these dependencies, many companies are hiring “Full Stack Data Scientists” who can handle all aspects of a data science project.
By learning JavaScript, you can expand your skill set and become a “Full Stack Data Scientist.” JavaScript allows you to work with user interfaces, websites, APIs, and other parts of the tech stack that are not typically associated with data science. This knowledge enables you to understand how your work fits into the bigger picture and gives you the ability to build end-to-end projects.
**Conclusion: Embracing the Dark Side?**
Learning JavaScript as a data scientist may seem unconventional, but it offers valuable opportunities for growth and versatility. While it may not have an immediate impact on traditional data science tasks, it broadens your understanding of the tech stack and allows you to create innovative projects. Embrace the challenge and open the door to new possibilities.
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