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# Introduction
If you have been writing code in the past couple of months, I am pretty sure you must have noticed a shift. AI is no longer something that just suggests snippets; it has gone beyond that, it’s starting to act. Developers are moving from assistive tools like Copilot…
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# Introduction
Why do people misread your data? Because they’re data illiterate. That’s your answer. Done. The end of the article. We can go home.
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Yes, it’s true; data literacy is still at low levels in many organizations, even those that are “data-driven”.…
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# Introduction
Data has become an indispensable resource for any successful business, as it provides valuable insights for informed decision-making. Given the importance of data, many companies are building systems to store and analyze it. However, there are many times when it’s hard to acquire and analyze the necessary data,…
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# Introduction
There are numerous tools for processing datasets today. They all claim — of course they do — that they’re the best and the right choice for you. But are they? There are two main requirements these tools should satisfy: they should easily perform everyday data analysis operations and…
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# Introduction
When you hear the word data science, you probably think of two words: programming and statistics. In fact, the prerequisite of learning statistics often discourages people from pursuing a career in data. It doesn't help that most data science job descriptions make it seem like you need a…
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# Introduction
Model Context Protocol (MCP) is a standard that defines how artificial intelligence systems connect with the outside world. Instead of each assistant or agent requiring custom code to use a database, file store, or API, MCP gives them a shared way to talk to these resources. At a…
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Machine learning has powerful applications across various domains, but effectively deploying machine learning models in real-world scenarios often necessitates the use of a web framework.
Django, a high-level web framework for Python, is particularly popular for creating scalable and secure web applications. When paired with libraries like scikit-learn,…
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# Introduction
Finding real-world datasets can be challenging because they are often private (protected), incomplete (missing features), or expensive (behind a paywall). Synthetic datasets can solve these problems by letting you generate the data based on your project needs.
Synthetic data is artificially generated information that mimics real-life…
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# Introduction
Ready for a practical walkthrough with little to no code involved, depending on the approach you choose? This tutorial shows how to tie together two formidable tools — OpenAI's GPT models and the Airtable cloud-based database — to prototype a simple, toy-sized retrieval-augmented generation (RAG) system.…
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# Introduction
You know the basics of Python's standard library. You’ve probably used functions like zip() and groupby() to handle everyday tasks without fuss. But here's what most developers miss: these same functions can solve surprisingly “uncommon” problems in ways you've probably never considered. This article explains some…