Readings
Summary of Books, Papers, Blogs for DS
As Big Data, Data Science, and Artificial Intelligence gain more popularity, become essential parts of online and offline products and services, and benefit more businesses and people’s daily lives, it’s ironically harder to stay on top of the trend. While you can access various forms of information such as presentations, research papers, blogs, courses, and videos, you will find that it’s impossible or impractical to review all.
In this section, I will provide summaries of the books, papers and articles from the manually curated list of high quality sources. You can consider that this list is to maximize the precision (in the concept of precision vs. recall tradeoff) to give you the best return on your time investment. I recommend you go beyond the list, explore more materials, and even suggest to me as you find meaningful readings.
Structure
You can find a countless number of data science blogs and subscribe to their updates. But soon, you’ll be overwhelmed by the amount of emails and posts that you find in your inbox. For the signal-to-noise ratio, you can review the curated high quality readings in this section, organized by 3 groups and 8 tags.
| Books | Papers | Blogs | |
|---|---|---|---|
| Stats | 0 | 0 | 0 |
| Experiments | 0 | 0 | 0 |
| ML Infra | 0 | 0 | 0 |
| ML Algorithm | 0 | 0 | 0 |
| Data Platform | 0 | 0 | 0 |
| Product | 0 | 0 | 0 |
| Leadership | 0 | 0 | 0 |
| Management | 0 | 0 | 0 |
10 Bullets
I have summarized each reading in up to 10 bullet points with two goals in mind: (1) to grasp the main messages with a small time investment, which will help you make a decision whether to invest more time to get to learn the details, and (2) to move up the forgetting curve by periodically revisiting the material and quickly going over the key points through your mobile device.
An Illustration Of The Forgetting Curve: 