Core Viewpoint - The article emphasizes the transformative impact of AI technology on the investment research industry, highlighting the necessity for financial professionals to embrace AI and Python for enhanced efficiency and data analysis capabilities [1]. Group 1: AI and Python in Data Acquisition and Processing - AI and Python play a significant role in efficiently acquiring and processing financial data, utilizing tools like web scraping with Python libraries such as requests and Selenium to gather key information from financial reports and market data [1]. - The integration of AI tools allows for rapid extraction and analysis of financial data, facilitating comparisons across multiple companies and enhancing the depth of financial analysis through Python's Pandas library [2]. Group 2: Report Generation and Data Visualization - AI excels in generating high-quality financial reports quickly, using tools like Huohua Data and mind mapping to present complex financial data in intuitive graphical formats [3]. - Python libraries such as Matplotlib and Pyecharts enable dynamic data visualization, making reports more persuasive and engaging [3]. Group 3: Automation of Financial Processes - The combination of AI and Python enables automation of financial processes, such as batch file generation and automated auditing, significantly improving work efficiency [4]. - Developing personalized AI systems can provide tailored investment research support, enhancing the overall analytical capabilities [4]. Group 4: Quantitative Investment Strategies - The application of AI and Python in quantitative investment is promising, offering robust technical support for developing and backtesting investment strategies, including K-line chart analysis [5]. - A dedicated quantitative strategy backtesting platform developed in Python allows investors to test and optimize their investment strategies, potentially increasing returns [5]. Group 5: Course Offerings and Practical Applications - The course titled "AI Large Model + Python Empowering Financial Full Process Practice" aims to explore advanced applications of AI and Python in investment research, covering complex strategy construction and intelligent research system development [5]. - The course includes 86 detailed lessons totaling 32.5 hours, providing comprehensive coverage of AI and Python applications in financial research, along with real-world case studies [7]. Group 6: Course Curriculum Highlights - The curriculum focuses on the application of AI large models in financial research, teaching participants how to extract information from real financial documents and optimize analysis reports [8]. - The course also covers practical skills in Python, including data processing, automated data scraping, and the development of tools for financial analysis [10].