This course is designed to equip learners with practical skills for using artificial intelligence to make data analysis faster, clearer, and more useful for real decision-making. It is structured to move from responsible AI use and environment setup into no-code analysis, AI-assisted coding support, spreadsheet intelligence, dashboard automation, and the communication of insights. The course therefore does not treat AI as a gimmick, but as a practical analytical support system that helps learners work more efficiently with data. The course responds to a major challenge in modern work and research: many people now have access to data, but not everyone has the speed, technical depth, or workflow discipline needed to turn that data into useful insight. Students, researchers, analysts, administrators, managers, and business professionals often spend too much time cleaning data, writing formulas, repeating spreadsheet tasks, or struggling to explain results clearly. This course addresses that gap by teaching how AI can support analytical thinking, technical execution, reporting, and communication. A major strength of this course is its layered structure. It begins with data privacy and responsible AI use, which is essential because analytical speed must never come at the cost of careless handling of sensitive or organizational data. Learners are then introduced to the data analysis environment, including foundational exposure to tools such as R, Python, and Cursor, so they understand where AI fits in a modern analytics workflow. The course then becomes more strongly applied. Learners use ChatGPT and Julius for no-code data analysis, showing that useful insight can be produced even before advanced programming skill is mastered. They also explore AI agents for data analysis, which helps them understand how AI can support coding, debugging, cleaning, and interpretation. From there, the course extends into spreadsheet intelligence, where learners use AI for formulas, pattern detection, and data checking, and then into dashboard and automation tools, where repeated reporting and presentation of results can be improved. The final part of the course focuses on interpreting and communicating insights, because data analysis has little value if the results cannot be translated into clear business, academic, or operational meaning. By the end of the course, learners should not only know how to analyze data faster, but also how to present findings in a form that supports action.