Reading Notes 2024 Nov - Dec

My Medium Articles!

In the past two months, I kept the pace of writing 2 articles per month on Medium. Here you go:

1. My Medium Journey as a Data Scientist: 6 Months, 18 Articles, and 3,000 Followers: I started writing on Medium six months ago, sharing my data science knowledge, growing as a writer, and connecting with the community. Fast forward to today, and I’ve published 18 articles, gained over 54,000 reads, and reached 3,000+ followers. In this article, I combine my passion for data science with my love for storytelling to break down 1. how I grew on Medium, 2. data insights from analyzing my Medium stats, 3. data-driven writing strategies.
2. From Data Scientist to Data Manager: My First 3 Months Leading a Team: Taking the leap from an individual contributor to a people manager has been one of the biggest challenges of my career so far. In this article, I reflect on the lessons I’ve learned, the surprises I’ve encountered, and the changes I’ve experienced in my first 3 months as a manager.
3. 5 Essential Tips to Build Business Dashboards Stakeholders Love: Among all DS work,dashboarding is often unfavored, unavoidable and undervalued. In this article, I share my top 5 tips to build business dashboards that stakeholders will appreciate.
4. How ChatGPT Became My Best Solo Travel Buddy in 2024: As the last article of 2024, I decided to write something less DS but more personal. In this article, I talk about my experience using ChatGPT to enhance my solo travel experience.

Reading List in Past Two Months

Now, let’s talk about the great articles I came across in November and December. I have to admit that I’ve read less given the holiday break :)

Data Science & Analytics

1. ** Your Data Quality Checks Are Worth Less (Than You Think)**: How to improve visibility of data quality issues, reduce the number of data incidents, and increase trust.
2. From Data to Insights: Segmenting Airbnb’s Supply: Airbnb’s approach to segment supply types based on availability, streakiness, and seasonality, how to validate and productionalize the segmentation, and applications.
3. How Spotify Implemented Personalized Audiobook Recommendations?: Spotify walks through its personalized audiobook recommendations using graph neural networks.
4. Time Series — From Analyzing the Past to Predicting the Future: Basic components of time series and common forecast methods.
5. Autocorrelation For Time Series Analysis: An explanation of the autocorrelation formula and plot for time series analysis.
6. How we tackled FB Prophet’s Monthly Seasonality Issue: Prophet does not provide a native monthly seasonality parameter and here is the workaround.
7. Information at a Glance: Do Your Charts Suck?: Practical advices on making better data visualization with examples.
8. Propensity-Score Matching Is the Bedrock of Causal Inference: What is propensity-score matching and a Python implementation example.
9. Synthetic Data in Practice: A Shopify Case Study: A detailed walkthrough of how to evaluate your synthetic data.

Data Career

1. Three Crucial Data Lessons That I Learned from a Data Conference That’s Not Related to AI: Learnings about cost containment, value of the data teams, and data storytelling.
2. Unpopular Opinion: It’s Harder Than Ever to Be a Good Data Scientist: What makes a good data scientist with the fast development of GenAI and LLM and what are the challenges.
3. My Path Towards Data @ Netflix: Lisa Herzog, an Analytics Engineer at Netflix shares her path into data and the most important data skills with helpful resources.
4. How to Fix Confusing Docs and Products: A practical guide on making docs and products more intuitive.
5. Are You Sure You Want to Become a Data Science Manager?: What does it mean to be a DS manager, the differences, danger zones, and opportunities.
6. The Ultimate Productivity System for Data Science Leaders: Where to spend time and how to prioritize projects as a DS leader.
7. The Most Expensive Data Science Mistake I’ve Witnessed in My Career: Why true success in machine learning goes beyond optimizing a single metric.
8. Part 1: A Survey of Analytics Engineering Work at Netflix: Netflix introduces several important functions in its Analytics Engineering team.
9. 5 Mistakes I See New Managers Make (and that I made myself): Five things you should stop doing as a manager.

AI and LLM

1. The AI Productivity Paradox: Why Aren’t More Workers Using ChatGPT?: What leads to the low adoption of ChatGPT in professional users, and why leadership support is critical.
2. The Name That Broke ChatGPT: Who is David Mayer?: An exploration and discussion of why ChatGPT refuses to answer anything about this David Mayer, and what it tells us about LLM.