my happy place — After several months of continuous writing, I have published a great number of blog posts and I feel it is time to organize them in one place. I cannot express how grateful I am for those who view, read, clapped, and responded to my articles. Watching the number of followers…

part 1: preprocessing text data — It is estimated that 80% of the world’s data is unstructured. Thus deriving information from unstructured data is an essential part of data analysis. Text mining is the process of deriving valuable insights from unstructured text data, and sentiment analysis is one applicant of text mining. It is using natural…

Python decorator is a very useful tool to help us code efficiently. As I mentioned in my previous article, coding efficiently is one of the principles I follow to be a better data scientist: Seven Principles I Follow To Be a Better Data Scientist Setting up my North Startowardsdatascience.com In this article, I want to uncover the use cases of Python decorators and discuss how…

My journey as an Econ PhD — As a data scientist with a PhD in economics, I always get a lot of questions about why I chose to be a data scientist, and how was my journey like. …

Embrace Causality — As the recent announcement on the 2021 Nobel Prize in economics, causal inference techniques and empirical studies based on observational data have come back to researchers' attention. The announcement says: This year’s Laureates — David Card, Joshua Angrist and Guido Imbens — have provided us with new insights about the…

Part 2, A beginner’s guide to the LDA Model — In my previous post, I introduced the clustering algorithm and discussed the K-Means algorithm in detail as the first part of the topic modeling series: Part 1: A beginner’s guide to K-Means Part 2: A beginner’s guide to LDA (this article) Part 3: Use K-Means and LDA for topic modeling…

Part 1: A beginner's guide to K-means — Clustering is one of the most used unsupervised machine learning algorithms. You can think of clustering as putting unorganized data points into different categories so that you can learn more about the structures of your data. Clustering has a variety of applications in extracting information from data without labels. For…

conduct reliable causal inference with historical data — A causal relationship, unlike a correlation, is a much stronger relationship between two variables. Although it is hard to claim a causal relationship, it gives meaningful insights and informative guidance once proven. In my previous article, I have discussed what, why and how regarding causal inference: Causal Inference: What, Why, and How validate the decision-making processtowardsdatascience.com