Does the world need another newsletter about data science and AI?
Everywhere you turn there are podcasts, videos, or headlines about AIs writing novels and music, debates about the ethics of AI, plus a whole slew of data science acronyms from ANOVA to Z-score.
For many people who aren’t data scientists, the conversation can feel like it’s happening in another language. That’s why we created The Data Drop.
Read the first issue: Traditional Automation, GenAI, or Agents?
Translating complex concepts into clear, relatable insights
Information about AI and data science is often either very technical or overly simplified. Nontechnical professionals are left in the middle, trying to make sense of what these tools actually mean for their work.
The Data Drop was born out of a simple idea: to translate complex concepts into clear, relatable insights that anyone can use.
It’s designed for curious professionals – managers, educators, business owners, students: For people who want to engage with AI and data science and start using it. Our assumption is that you don’t need another “Top 10 AI New Stories” or a tutorial on how to code. Instead, we want to provide:
- Plain-language explanations of key concepts (What is data in 2025?)
- Practical takeaways to help you spot opportunities for using AI in your everyday work (For example: What makes a task a good fit for agentic AI?)
- Best practices from your peers (For example: Which tasks are not great for agents)
Our approach
Every issue of The Data Drop follows a simple structure:
- One concept explained clearly, without any jargon
- An interview or best practice from the community
- Perspectives on the data science and AI space
#1. One concept explained clearly, without any jargon
Each edition of The Data Drop breaks down a single concept or technique into a short, clear explanation. Our goal is that these explainers give you the confidence to use these concepts without confusion. So you might find explanations on: “Probability vs. Likelihood” or “Data-driven vs. Data-aware,” “How to avoid fool’s gold in analytics,” “How to spot AI bias,” and more.
We’ll add in infographics and quizzes to help you remember what you’ve learned. And we’ll feature free courses and webinars led by experienced instructors. So when you’re ready to expand beyond the five-minute read, you’ll know exactly where to go next.
#2. An interview or best practice from the community
Learning isn’t just about definitions; it’s about seeing how others do it. The newsletter features interviews with learners and experts who share their routines, tips, and best practices. It’s a way to borrow what works for others and integrate it into your own work.
#3. Perspectives on the data science and AI space
It’s a busy space to navigate without a compass. We’ll share opinion pieces, topics we here at KNIME find inspiring or discussion-worthy, plus recommended reading from experts. The idea is to help you identify which new techniques and technologies you want to focus on
Join us
With The Data Drop we want to make conversations about data science and AI more accessible to everyone. Each edition will focus on clarity, context, and relevance. We see the newsletter as an evolving resource, and shaped as much by your questions and perspectives as the topics we choose. If there’s a concept you’d like us to break down, email us at data-drop@knime.com.
Meet the writers behind The Data Drop
We are Heather Fyson and Swetha Kannan! We may occasionally invite other friends at KNIME to write, otherwise it’s mostly us behind The Data Drop! We both love writing, but then our interests go off at a tangent. Together, though, we think we have the right mix to write a newsletter for you that makes data science learning more accessible, fun, and enjoyable to read.
Swetha Kannan
I’m a data scientist and content marketer at KNIME with a background in mathematics. On the Brand Awareness team, I focus on blending analytical thinking with creative storytelling to make data science and AI more approachable for people at all levels.
I write KNIME Blog articles on AI, statistics, and financial analytics, and I created the 5-Day Email Mini Series to guide beginners through building their first workflow in KNIME. I also craft infographics for social media, e-guides, and other learning resources. Across all of these, my goal is to take complex topics and turn them into something clear, engaging, and useful, helping learners feel confident as they explore data science.
Outside of work, I love traveling to new cities, catching up with old friends, and binge-watching true crime documentaries!
Heather Fyson
I’m a content marketer and the editor of the KNIME blog. My background is in translation. My acquaintance with data science began when I worked as an assistant at the Chair for Bioinformatics and Information Mining at Konstanz University. Initially, I was copyediting papers about “pruning” and “decision trees,” but I gradually became more familiar with information mining and increasingly in awe of the data scientists around me.
Despite the fact that I hated maths at school (I'm still not that keen), I now write about topics ranging from upskilling flywheels to data sleuths, to how to start thinking about data science. My goal is to make sure every piece of content is clear, interesting, and accessible.
When I’m not at work, I love old ballads, sung around grimy tables, in folk clubs.