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Claude Code for Beginners: Step-by-Step AI Coding Tutorial

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 Artificial Intelligence is changing how developers write software. From generating code to fixing bugs and explaining complex logic, AI tools are becoming everyday companions for programmers. One such powerful tool is Claude Code , powered by Anthropic’s Claude AI model. If you’re a beginner or  an experienced developer looking to improve productivity, this guide will help you understand  what Claude Code is, how it works, and how to use it step-by-step . Let’s get started. What is Claude Code? Claude Code is an AI-powered coding assistant built on top of Anthropic’s Claude models. It helps developers by: Writing code from natural language prompts Explaining existing code Debugging errors Refactoring code for better readability Generating tests and documentation In simple words, you describe what you want in plain English, and Claude Code helps turn that into working code. It supports multiple programming languages, such as: Python JavaScri...
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Social Analytics - How Marketers Will Use

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Of all the windows through which a business can peer into an audience, social media seems most enticing. The breadth of subjects, range of observations, and, above all, the ability to connect and draw inferences make social analytics hugely exciting for anyone who is interested in understanding and influencing past, present and potential customers, employees, or even investors.

As individuals leave traces of their activities - personal, social and professional - on the internet, they allow an unprecedented view into their lives, thoughts, influences and preferences. Social analytics attempts to draw useful understanding and inferences, which could be relevant to marketers, sales persons, HR managers, product designers, investors and so on. Thus, as social tools like Facebook, Twitter, LinkedIn, WhatsApp, and many more, host a plethora of social activities of many people, a humongous amount of data is generated about people's preferences, behaviour and sentiments. Like any data, it is amenable to analysis to gain useful insights.

The challenge comes from the sheer volume, velocity, and variety. It is very difficult to ensure that the analysis is relevant and reliable. Besides the daunting technical intricacies of setting up the appropriate analytics, the aspects of choosing information sources, filtering the right data, and its interpretation and aggregation are susceptible to errors and biases. For example, some social activities are relatively easier to access (like activity on Twitter, or public updates on Facebook), many are not. Some types of data (like text, or location) are easy to search and interpret, many (like pictures) are not. So a good analysis model must judiciously compensate for the nature of the sources included, and hence it could be at times very difficult to assess if the analysis is useful or just meaningless mumbo-jumbo.

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