6 Useful Python Libraries & Tools For Data Science Beginners.

Python is kind of like the frozen yogurt of programming languages — it’s extremely popular and versatile on its own, but it’s even better when you add toppings. Of course, by “toppings,” we’re talking about the many Python libraries and tools that level-up what you can do with the language. With data science, in particular, there are lots of pre-written Python code packages and extra tools that allow you to work with data in more advanced ways, explains Ada Morse, Codecademy Curriculum Developer in Data Science. In the new free course Getting Started with Python for Data Science, you’ll get to use Pandas, a Python module that’s used for data manipulation. read more...

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Fixing Unix/Linux/POSIX Filenames

Traditionally, Unix/Linux/POSIX filenames can be almost any sequence of bytes, and their meaning is unassigned. The only real rules are that "/" is always the directory separator, and that filenames can't contain byte 0 (because this is the terminator). Although this is flexible, this creates many unnecessary problems. In particular, this lack of limitations makes it unnecessarily difficult to write correct programs (enabling many security flaws), makes it impossible to consistently and accurately display filenames, causes portability problems, and confuses users. more ....

Backing up MySQL data.

Backing up files and directories is relatively easy; databases, however, have some special quirks that you need to address. Our examples use MySQL, but the same principles apply to PostgreSQL and other relational databases. This article is excerpted from O'Reilly's recently published book Linux System Administration .

Many Companies Hold Vast Data but Are Unprepared for LLM Fine-Tuning: How to Solve It and What to Do About It

Many Companies Hold Vast Data but Are Unprepared for LLM Fine-Tuning: How to Solve It and What to Do About It In today’s data-driven world, companies across various industries generate and store vast amounts of data. From customer interactions and sales transactions to sensor readings and user-generated content, organizations are sitting on treasure troves of information. However, when it comes to leveraging this data for fine-tuning large language models (LLMs), many companies find themselves unprepared. The growing need for AI-powered solutions requires adapting these models to specific organizational needs—a task that demands both the right infrastructure and expertise. The Challenge: Vast Data, But Lacking Readiness for LLM Fine-Tuning Large language models, such as OpenAI’s GPT or Google’s Bert, have revolutionized industries by providing AI capabilities for natural language understanding, generation, and analysis. However, these models are typically pre-trained on generaliz...

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