Data Science

Someone asked me last week about the benefit of developing Python on a dockerized environment vs. using a virtual environment (VE): The short answer is the level of reproducibility — Docker provides a higher level of reproducibility with respect to VE. Regardless if you are a…

And a short summary for each. — It was only six months ago when OpenAI released ChatGPT, the mind-blowing player in the large language model (LLM) ecosystem. ChatGPT definitely changed people’s minds on using LLMs. Approximately two months after it was released, ChatGPT became the fastest growing consumer software application, gaining more than 100 million users. It…

Mathematicians have a habit of finding order in the most unexpected places. A famous example of this is when Leonhard Euler was asked to find a method to cross each of the Seven Bridges of Königsberg only once. He proved that this was impossible, and his proof served as a…

The art of finding the right topic—and the right scope—for data science and ML projects — What is it that makes a data science project stand out? At a time when job applicants are expected to have shiny portfolio sites and when data teams need to demonstrate their business value on an ongoing basis, the question has rarely been more crucial. Technical ability is a major…

Introduction I started off in education as a college tutor hired as a High School Teacher. At the high-school level, I taught Engineering courses in AP Computer Science and Digital Electronics. Now I know what you may be thinking. “She was teaching a technology course so the transition would have been…

Improving machine learning models’ performance with hyperparameter optimization. — Hyperopt is an open-source hyperparameter optimization tool that I personally use to improve my machine learning projects and have found it to be quite easy to implement. Hyperparameter optimization, is the process of identifying the best combination of hyperparameters for a machine learning model to satisfy an objective function (this…

After more than 5 years working as data scientist (IC) and manager at Gojek ($GOTO), I’d like to summarize top lessons learned working in the field of data science in a fast-pacing company: Not all problems should be solved with Machine Learning Treat your business stakeholders as thinking partners Your…

There are no error messages — that’s what makes them subtle — Learn to avoid the six most serious mistakes related to machine learning theory that beginners often make through Sklearn. Often, Sklearn throws big red error messages and warnings when you make a mistake. These messages suggest something is seriously wrong with your code, preventing the Sklearn magic from doing its…

Some advice to use ChatGPT to learn to program while actually learning it — If you’d like to start to program but you’re afraid AI will steal your job, then this article is definitely for you. Especially if you’re thinking about changing your career to become a Software Engineer or a Data Professional, I really feel how uncomfortable you are right now. I started…