Reading List
The All-time Best Guides to Data Science Writing
Learn how to write better for your colleagues and peers
The data science blogging ecosystem is rich and growing. TDS alone has an archive of more than 20,000 posts across numerous topics. Many experts have launched Substacks, newsletters, or personal blogs. If you’re looking for great new reads to add to your roster, check out Vicky Boykis, Randy Au, or start from this list of ten foundational ML blog posts.
Many of us are also interested in contributing our thoughts and perspectives; one of my goals for 2021 is to write and publish more. But for those of us more technically inclined, writing can feel harder than coding. How do you get started? What topics should you cover? What does the data science community want to read? How can you get better at technical writing?
I’ve taken a deep look into the TDS archives and assembled a shortlist of the 12 most useful writing guides and honest reflections about the challenges of online writing. They’re also strong examples of writing that is engaging and substantive.
There are many sub-categories of data science writing. We’ll explore three today:
If you want to learn more about why you should start writing too, I’ve added some links at the end to articles that candidly discuss the many benefits (and struggles!) that come from publishing regularly online. Writing improves soft skills that can help your career, surfaces serendipitous networking opportunities online, and is a great tool for learning new things.
Business Reporting
Clients don’t read code, they read reports. It’s important to spend enough time thinking about how to translate complex models and statistical results into actionable insights for a non-technical audience. The following posts look into the building blocks of strong analytical reports.
The best writing resources come from practitioners who have tested and refined their strategies based on real-world feedback. Wenling Yao created her own Analyst Style Guide, complete with a template, that covers four principles analysts should keep in mind when producing data reporting for stakeholders and senior management:
Kyle shares the ultimate guide to writing up your data so that others in the company can generate insights:
It isn’t enough to only get the SQL queries or complex modeling right. William Chon explains how we should communicate technical information to stakeholders and decision-makers:
Susan Li shows how to combine strong narrative storylines with data visualizations when presenting analytical reports to your boss:
Data scientists looking to managerial roles will find some actionable takeaways on how to get there from this article by Fernando Tadao Ito. He breaks down how strong communication skills are essential for senior leadership positions:
Sonali Verghese shows you the steps to build a strong data narrative:
Why is writing effective design docs popular in companies like Amazon and Google? Vincent Tatan shares how data scientists can use them to ensure better project outcomes:
Blogging
Most blog posts online, on TDS and elsewhere, aim to reach a general audience. These include 101-type explainers, opinion pieces, and tutorials. These posts share a wide range of perspectives, from the specific challenges of writing “viral” articles, to how to write about highly technical and advanced concepts to beginners.
Tony Yiu wrote one of the most popular online guides to random forest (it appears as the second link if you Google the algorithm), and it happened entirely by accident. He shares how the post came to be, and how replicating that success has proved difficult:
Thushan Ganegedara reflects on his experience authoring a self-published NLP book, and shares tips for writing technical articles that engage a general audience:
Pier Paolo Ippolito, an experienced writer and Editorial Associate at TDS, shares his checklist to get an article ready for publication on TDS:
It’s increasingly difficult to find something that no one else has already written about. Aaron Frederick offers a humorous take on what it’s like writing in saturated topic areas:
Research & Other Projects
The academic field of data science is growing, and growing fast. There are so many data science conferences, peer-reviewed journals, and open-source publishing platforms. These posts explore writing for academic or professional readers.
Amine Hadj-Youcef, PhD., an experienced researcher, wrote the article he wished he had read before writing research papers:
Will Koehrsen believes that “well-written blog posts can have a long shelf life, giving you a portfolio for potentially years to come,” so write up that project you shipped last week:
Start writing today
If you’re still looking for a reason to get into data science writing, these candid reflections by fellow data scientists might motivate you to start today.
Rebecca Vickery writes to teach herself new tools and techniques (Feynman technique):
Finn Qiao’s five takeaways from a month of data science writing:
Serdar Korur found that writing created career opportunities and offers 9 tips for better articles:
Juan De Dios Santos discusses how writing technical tutorials had positive spillover benefits for his coding. It made him a better engineer:
Conor Dewey argues persuasively that “[d]ata scientists should ruthlessly prioritize impact”, and that writing is particularly effective at adding value to a data science career:
Megan Dibble tells her story about how publishing popular explainers helped her break into data science from engineering:
Do you have favourite resources on writing better data science articles? Who are some of your favourite authors? Let us know in the comments!
