1. 3 Principles to Achieve Anything — Think and Grow Rich

It’s Desire-Faith-Persistence.

2. Why AutoML Is An Essential New Tool For Data Scientists

3. Featurewiz: Fast way to select the best features in a data

1. 12 Python Snippets That Will Boost Your Productivity

I learned some interesting things from this blog, such as below:

• The itertools and collections are very useful, they have high performance functions that can help you save a lot of time in optimizing your code.
• By applying a simple trick, build an interleave list is much easier than I think

2. 25 Github Repositories Every Python Developer Should Know

Learn from Pros is always a good strategy. I find some good repos which are perfect for me to learn Scala and advanced Python.

3. DeepMind Combines Logic and Neural Networks to Extract Rules from Noisy Data

It’s good to me to understand what Googlers have achieved. They always play role model in the AI world. The example in this blog is…

Hi everyone,

I’m preparing for some interviews in next week, so I challenge myself to solve quant interview questions as many as possible during one hour.

Quite sure that I can’t solve all, I try with 21 problems as below:

1. For a 3 sets tennis game, would you bet on it finishing in 2 sets or 3 sets?
2. I have a square, and place three dots along the 4 edges at random. What is the probability that the dots lie on distinct edges?
3. You have 10 people in a room. How many total handshakes if they all shake hands?
4. Two…

1. Indicator selection and stock return predictability, by Zhifeng Dai , Huan Zhu

In this research paper, the authors tried to solve the problem that how to build a winning portfolio from previous simple indicators/strategies. The method that the authors used is momentum-determined indicator-switching (MDIS) strategy.

Though this research is applied to stock market return, and the predictors include micro- and macro-variables, but the idea is worth to try to apply to stock prediction.

2. Forecasting stock prices, by Arie Harel, Giora Harpaz

In this paper, the authors group the stock to three types: overpriced stocks (OP), underpriced stocks (UP), and fairly-priced stocks (FP). Then, the authors show how to calculate evaluation metrics for a prediction model. The metrics are quite popular…

Interesting news/blogs that I read during a week.

1. 16 Must-Know Bash Commands for Data Scientists

It is a good article for people who begin to work in Linux/MacOS environment.

The author is fairly an excellent engineer and a good blogger: his articles are simple, clear and useful to everyone who wants to get familiar faster with Python.

2. 4 Tricks for Making Python Pandas More Efficient

It’s very interesting for Pandas fan (all Python programmers, I guess)

3. 22 Code Snippets That Every Python Programmer Must Learn

This is cool stuff for Python beginners.

4. Should we “reject” Reject Inference? An Empirical Study

At the moment, I’m building a credit scoring model with banking customers dataset. …

Interesting news/blogs that I read during a week.

1. DataPrep v0.3.0 has been released

EDA is not an easy stuff for me because I’m not type of person who enjoys going to details. That’s why I’ll search for EDA python libraries which help me do EDA. Before knowing about DataPrep, I knew Sweetviz. You can see there are other python libraries can help you doing EDA in the link below:

To be honest, I’m not happy with Sweetviz because I love something simple and easy-to-use. …

Today is May 3rd 2021. My learning plan is as below:

• Practice coding: Do at least 3 coding questions per week from the book Daily Coding Problemof Alex Miller and Lawrence Wu.
• Learn Scala programming: should spend 2–3 hours per week
• Learn SQL: tbd
• Write Clean Code and Unit tests: should review codes that I have written.

Hi everyone,

I have made a decision that I need to improve my coding skill day by day to keep track with the lightning pace of data industry’s evolution. In this blog, I’m happy to share with you all problems from books that I challenge myself everyday. Hope that it would be useful for you too! Let’s go!

Problem 1: Given an array of integers, return a new array such that each element at index i of the new array is the product of all the numbers in the original array except the one at i.

For example, if our…