Harnessing Object-Oriented Programming for Database Manipulation
When diving into data science projects, setting up an efficient data collection pipeline is essential. Unlike static datasets found in traditional challenges like Kaggle, real-world Machine Learning involves constantly changing data. Think of it as navigating a bustling market where you need to gather fresh ingredients. You might scrape websites or pull data from APIs, resulting in a bit of chaos. To tame this disorder, structuring your code based on best practices is crucial.
Once you’ve pinpointed your desired data sources, it’s crucial to gather and store your data systematically. For instance, if you’re training a Language Learning Model (LLM), you might require sources with three key fields: author, content, and link. Collecting this data efficiently will allow you to create a robust model.
After downloading your data, the next step is to implement SQL queries to manage your database. Typically, you’ll need to execute CRUD operations — that’s Create, Read, Update, and Delete — the foundational functionalities for managing persistent storage. Having a strong grasp of these operations will empower you to effectively interact with your database.
