Reading Notes 2025 Nov - Dec

My Medium Articles!

In the past two months, I continued writing articles per month on Medium and Towards Data Science. Here you go:

1. A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play: After 500 days of playing LinkedIn Games, I finally turned this little habit into a product data science case study. In this article, I share my thoughts from the perspective of a data scientist: Why might LinkedIn have built Games in the first place? How does the feature create a habit loop that drives retention? Why do we see small UI changes appearing from time to time? How would a Product Data Scientist design experiments and measure success?
2. I Tracked Every Article I Wrote in 2025 — Here’s What the Data Says: In this article, I double-clicked into my 2025 writing journey – 12 articles, 36.5k reads, and $2,960 earned. It shows a data-driven breakdown of what actually worked, what didn’t, and the trade-offs behind those outcomes.

Reading List in Past Two Months

Now, let’s talk about the great articles I came across in the past two months. A common theme at the end of year is definitely 2026 trend forecast :)

Data Science & Analytics

1. What’s Trending in Data Science and ML? Preparing for 2026: Top trends in 2026, including from traditional analytics to agentic analytics, small language models are the next big thing, and the rise of specialized multi-agent systems.
2. Business Intelligence Trends for 2026: How Data Products Help Improve BI: How will AI transform business intelligence in 2026.
3. 7 Websites I Visit Every Week as a Data Scientist (to Stay Ahead of the Curve in 2025: A good collection of data science online resources.
4. How to Build a Data Analytics Platform: Best Practices: What is needed for a good data analytics platform.
5. Why Statistical Significance Fails in Big Data (And What to Use Instead): A good overview of statistical vs practical significance.
6. Top 10 Data and AI Trends That Will Shape 2026: Another good article on how the data science field will evolve with AI in 2026.
7. 12 Visualization Hacks That Turn Data Into Stories: A practical and easy-to-follow guide of visualization tips.
8. Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction: Detailed walkthrough of Bonferroni vs. Benjamini-Hochberg for P-value correction.

Data Career

1. The End of Personal Websites: Why creators no longer use personal websites, and why do Substack, Medium, or Notion work better?
2. The Quiet Waste Of Manager 1-On-1s: A better structure for your 1:1 with your manager.
3. Impostor Syndrome In Data Science: A Rational Approach: Why data scientists are more vulnerable to the imposter syndrome and how to overcome it.
4. Machine Learning vs AI Engineer: What Are the Differences?: A great breakdown of machine learning engineer vs. AI engineer and which one should you pursue.
5. Ben, My Coworker, Taught Me How to Push Back on Leadership Like a Pro.: A great guide to push back with concrete examples.
6. The Path to Success in Data Science Is About Your Ability to Learn. But What to Learn in 2026?: Four pieces of advice on what to learn in 2026.
7. The Best Data Scientists are Always Learning: Three ways to find what to learn.
8. Data Science in 2026: Is It Still Worth It?: The fact that data science is a large family of roles and why it is still relevant today.

AI and LLM

1. 10 GenAI Use Cases in ETL You Can Implement Today: Practical examples of AI use cases in data engineering.
2. The Truth About AI Jobs in 2026 That No One Talks About: 95 percent of AI pilots at major companies are failing and why.
3. Building a Semantic Layer for the AI Era: Beyond SQL Generation: What the next generation of Semantic Layer will look like for AI use cases.
4. What Stack + AI Tools do Data Scientists Actually Use Now?: Data scientists tech stack before and after the AI era.
5. 4 Techniques to Optimize AI Coding Efficiency: Very handy techniques that make AI coding better and easier.