Between 2016 and 2018, most of my projects were heavily written in R. At its core, R has a strong emphasis on functional programming. For example, I enjoyed using packages like dplyr to seamlessly chain data cleaning and modeling steps. In R, everything operates as a function call—even for loops function under the hood as calls.

for (i in 1:10) {
  print(i)
}
# is same as 

`for`(i, 1:10, print(i))

In this post, I introduce the fundamental concepts of functional programming, using R code as illustrative examples.

As part of my learning journey, I’ve been using several subscriptions. In this post, I’ll share my experiences with three of them in 2024:

1. ACM Skills Bundle Add-On
2. Heptabase
3. AWS Skills Builder

DVC (Data Version Control) is an open-source version control system for machine learning projects. It enables data scientists and machine learning engineers to manage their data, models, and experiments efficiently. This post explores how to use DVC to manage machine learning pipelines and lifecycles.

When developing Lambda functions, you may need to verify the behavior of your code in a test environment before deploying it to production. For example, your Lambda function needs to send a email to your client, but you don’t want to disturb your client while still developing the code. In this post, we’ll explore how to use the AWS Serverless Application Model (SAM) and samconfig.toml to manage different environments for your Lambda functions.

AWS Lambda, a serverless computing service provided by AWS, executes code in response to events and automatically manages the compute resources required by that code. AWS SAM (Serverless Application Model) is an open-source framework for building serverless applications and provides a simplified way of defining the AWS services needed by your serverless application. This post explores configuring AWS Lambda deployment using an AWS SAM configuration file

Testing plays a crucial role in software development. When you’ve completed a code module and wish to merge it into the main branch, it’s essential to ensure that the code adheres to the style guidelines and functions correctly. This is where testing becomes crucial. Since testing is a repetitive task, automating the testing process can greatly reduce the time and effort required to validate the code. Therefore, in this post, I’ll demonstrate how to utilize GitHub Actions for automating the testing process, which encompasses checking code formatting, linting, and running unit tests

A hallmark of clean code is consistent formatting, which becomes important when collaborating in a team. While manually using formatters or linters to check code is possible, it’s easy to forget. A more effective approach is to use tools like pre-commit hooks for automated code inspection before committing