Reading Notes 2021 Jan - Feb
This year, I put reading data science related Medium & blog posts on my resolution list – the plan is to read three posts on Friday night and three on Sunday night every week, topic could vary, as long as the title catches my eyes :). Very foturnately, I also find a friend doing this with me so we can share thoughts, great articles, and brainstorming. And not surprisingly, in just two months, my reading notes have grown to 20+ pages. Therefore, I decided to pick the best ones I read in the last two months, and share it here.
Product Experimentation and Causal Inference
- Experimentation Analysis at Lime: A very systematic article on experimentation framework, process, and considerations
- Casual Impact @ Coursera Series: Introduced four most common causal inference techniques with practical examples at Coursera
I - Controlled Regression
II - Instrumental Variables
III - Regression discontinuity
IV - Difference-in-difference - Key challenges with Quasi Experiments at Netflix: challenges and solutions of Quasi Experiments at Netflix
- There is more to experimentation than A/B: Introduced how Booking.com built their non-randomised experiment platform
- Causal Inference Cheatsheet (related reading): A very good summary of causal inference techniques, robustness, pros and cons
Customer Lifetime Value
- Rethinking Customer Lifetime Value using Machine Learning at Hellofresh: Detailed approach on how Hellofresh calculates LTV given the its flexible subscription that can be paused anytime
- Calculating customer lifetime value: A Python solution: LTV implementation at Azure, a more traditional way
- Customer Behavior Modeling: Buy-til-you-Die Models: Introduced Buy-till-you-Die (BYTD) model - a family of statistical models specifically built to address CLV problems
Sentiment Analysis
- Sentiment Analysis: Concept, Analysis and Applications: General ideas of sentiment analysis and examples
- Simplifying Sentiment Analysis using VADER in Python (on Social Media Text): VADER is a rule-based sentiment analysis model specifically designed for social media text
Data Infrastructure
- Data Quality at Airbnb (1 and 2): Talks about how Airbnb improved their data quality across the company
- Growth Engineering at Netflix: Introduced the Growth Engineering team at Netflix and some use cases
1 - Accelerating Innovation
2 - Automated Imagery Generation
3 - Creating a Scalable Offers Platform - Analytics at Netflix: Who We Are and What We Do: Introduced the data-related functions and teams at Netflix
Other Machine Learning Related Topics
- Supporting Content Decision Makers with Machine Learning: How Netflix utilized transfer learning to optimize content display
- Improving Deep Learning for Ranking Stays at Airbnb: Talked about the considerations and solutions to improve the listing ranking model at Airbnb