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Unclaimed ProfileThe Claude Code Automation Bootcamp: From Skills to Systems, developed by the Data Science Academy, offers a comprehensive path to mastering AI-driven automation. Instead of relying on basic prompting techniques, this course teaches students how to engineer robust, scalable systems using Claude Code. Participants will explore practical methodologies for designing reusable skills, establishing multi-step workflows, and integrating external APIs using the Model Context Protocol (MCP). Throughout this training, learners will build real-world projects such as interactive dashboards, content engines, and self-contained automation agents. Additionally, the curriculum covers performance optimization techniques, token management, and utilizing NotebookLM to mitigate hallucinations. Designed for developers, technical analysts, and AI enthusiasts, this program provides the practical tools needed to construct a fully functional personal AI operating system. By the end of this course, students will be capable of transitioning from writing simple instructions to deploying complex, self-executing AI agents that significantly boost productivity. Whether you are looking to automate search engine optimization pipelines, manage browser operations, or coordinate complex sub-agents inside system pipelines, this course bridges the gap between raw AI potential and practical, production-ready engineering. It is an essential guide for anyone eager to master the next generation of generative AI development and leverage Claude's advanced capabilities to solve intricate business bottlenecks.
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Data Science Academy
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Muhammad Usman Mallick, representing the Data Science Academy, is an expert educator and technical practitioner specializing in cutting-edge artificial intelligence, generative models, and advanced automated workflows. With a deep commitment to demystifying complex technologies, Muhammad guides students through the practical aspects of modern AI development. His training programs focus heavily on immediate application, taking learners beyond theoretical machine learning concepts into hands-on implementation of technologies like Claude Code, Model Context Protocol (MCP) systems, OpenAI, and Anthropic APIs. Muhammad's teaching philosophy revolves around project-based execution. He designs his curriculum to ensure that engineers, developers, and tech enthusiasts can build concrete tools,…Show more
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Program Overview
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Price may change · updated within 1–2 weeks
Course language
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Build real AI automation systems using Claude Code instead of relying on simple prompts
- Design and implement reusable skills to create structured, scalable workflows
- Connect external tools and APIs using MCP (Model Context Protocol) for dynamic execution
- Create interactive dashboards, UI systems, and artifacts powered by AI
- Automate end-to-end workflows including SEO systems, browser automation, and content generation
- Use NotebookLM as external memory to reduce hallucinations and improve output reliability
- Orchestrate multi-step workflows using sub-agents and system pipelines
- Optimize AI systems for performance by managing context, tokens, and execution efficiency
- Build real-world projects like an AI dashboard, automation agent, and content engine
- Structure and scale your own personal AI operating system using Claude Code
Best For
- Software developers looking to transition from basic prompting to building robust AI agents.
- Technical professionals aiming to master the Model Context Protocol (MCP) for tool integration.
- Automation engineers interested in architecting scalable, self-executing AI system pipelines.
- Data analysts and power users who want to build custom, AI-driven productivity systems.
Not For
- Complete beginners with no interest in programming or technical systems architecture.
- Individuals seeking only basic, no-code AI chatbot configuration tools.
- Enterprise users looking for theoretical AI ethics or management-level high-level overviews.
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