My Article Series

Extract SQL tables, insert, update, and delete rows in SQL databases through SQLAlchemy — In a data science project, we often need to interact with Relational databases, such as, extracting tables, inserting, updating, and deleting rows in SQL tables. To accomplish these tasks, Python has one such library, called SQLAlchemy. It supports popular SQL databases, such as PostgreSQL, MySQL, SQLite, Oracle, Microsoft SQL Server…

Extract Data from PDF Files Effectively — Background In the previous article, I talked about how to use tabula-py and Pandas in Python to scrape data from both structured and unstructured data from PDF files. In this article, I’m going to introduce an alternative way to scrape data from PDF files: PDFQuery. Required Libraries PDFQuery: to scrape text from PDF…

The differences in terms of cost functions, Ordinary Least Squares (OLS), Gradient Descent (GD), and Maximum Likelihood Estimation (MLE). — In this article, I’ll cover a roadmap for statistical model development. Specifically, we’ll talk about how the development of linear regression differs from logistic regression. We’ll also discuss why some methods work in one model, but not in another model. The following is an overview of this roadmap.

Observational Study, Experimental Study, Regression Model, Instrumental Variable, Difference-in-difference Model, Parametric Test and Non-Parametric Test — As data science practitioners, we’ve been asked a lot that “Does X drive Y?” Y is the outcome that we care about. X could be a new feature, product or drug. For example, a website owner would like to ask if a new web page design leads to a higher…

Good practice to improve the prediction with unseen data — When we talk about prediction using machine learning models, it’s important to understand prediction errors (i.e., bias and variance). The goal of any machine learning model is to find a model that minimizes the prediction errors on unseen data. There’s a tradeoff of a model’s ability to minimize prediction errors…

Understand OLS Linear Regression with a bit of math — Heteroskedasticity and Autocorrelation are unavoidable issues we need to address when setting up a linear regression. In this article, let’s dive deeper into what are Heteroskedasticity and Autocorrelation, what are the Consequences, and remedies to handle issues. A typical linear regression takes the form as follows. The response variable (i.e…

Understand OLS Linear Regression with a bit of math — The OLS estimator is known to be unbiased, consistent and BLUE (Best Linear Unbiased Estimator). But what do these properties mean? Why are they important for a linear regression model? In this article, we will discuss these properties. A typical linear regression looks like something as follows. The response variable…

Discussion of Exogenous variable, Exogenous variable, Omitted variable, Measurement Error, and Simultaneity Bias — In my previous article, we discussed some common issues when designing a linear regression — Omitting Important Variables and Including Irrelevant Variables. In this article, we’ll discuss Endogeneity in a linear regression model, especially in the context of Causal Inference. A linear regression model is a popular tool used to…

Understand Bias and Variance: Omitted Variables, Confounding Variables, Irrelevant Variables, and Multicollinearity — In my previous article, Causal Inference: Econometric Models vs. A/B Testing, we discuss how to use an econometric model, namely, linear regression to investigate the causal relationship between the treatment variable and the response variable while controlling other covariates. …