Learn to build intelligent apps without worrying too much about software development. — You may have already used GPT3 or GPT4 and may have started using it in your everyday work. Yet, you may still use it repeatedly for the same reasons, changing one or two small pieces in your prompt, generating the output, and even using it in your work like before. …

The easiest, the most flexible, and the most comprehensive ways to do web scraping in Python — Web scraping is a daunting fortress of complexity to the uninitiated. It feels like approaching a grand symphony with nothing but a humble kazoo. But, there is rhythm in the madness, which can be yours to command. Python, our trusty baton, offers us a multitude of approaches to web scraping…

Dramatically speed up reusable computationally intensive tasks. — Computationally intensive tasks are everywhere now. We are using resource-intensive techniques such as LLMs and Generative AI a lot these days. Whoever uses precious resources would know how daunting it is to do the same task again, even though we know the results will be the same. …

ETLs don’t have to be complex. If that’s the case, use GitHub Actions. — If you’re into software development, you’d know what GitHub actions are. It’s a utility by GitHub to automate dev tasks. Or, in popular language, a DevOps tool. But people hardly use it for building ETL pipelines. The first thing that comes to mind when discussing ETLs is Airflow, Prefect, or…

Matching text where there’s no perfect match — The real world is not perfect. People use different forms of the same piece of information. Even well-established systems use different standards. You’d have seen city names misspelled, like “Santana” instead of “Santa Ana” or “St. Louie” instead of “St. Louis.” When working with real-world data, this is inevitable. Thus…

You don’t always need Python or R to fit models — Postgresql has covered the basics — SQL programmers hardly fit any ML models. Someone else will do it unless they have either Python or R knowledge. While Python and scikit-learn are often my go-to tools for machine learning, it is worth noting that SQL can also do some quick and dirty model fitting. Regression models are…

Simple hacks to try before moving to a different data model altogether — Several databases have emerged to solve specific software development and data science needs. Yet, relational databases are still the most used and preferred by many. The query speed is the most common reason people think of a different data model. Document-based, column families and graph databases help solve for speed…

Bringing the best of both worlds together — I’m keen on finding ways to speed up analytical tasks. I’ve written several posts in the past about measuring memory usage, running time, and tips to speed up data-intensive tasks. Since we use Pandas primarily daily, I keep researching for improved performance. How to Do Fuzzy String Matching in Pandas Dataframes Matching text where there’s no perfect matchtowardsdatascience.com

Pipes are an incredibly fantastic technique for more human-friendly coding — Your code is your documentation too. People say that great programmers do not add comments to their code. They believe that if the code is hard to create, it should also be challenging for others to comprehend and alter. Hence they write straightforward code. Although I don’t advocate for not…