論文ID: JMJ25-0015-R
Python is one of the most popular programming languages to learn and use. It is ubiquitous in fields like data analysis, artificial intelligence, image processing, robotics, and web development. Its versatility and reliability make it ideal for major companies, including Tesla and Netflix. NASA even uses it to calibrate the James Webb Space Telescope. With the advent of artificial intelligence (AI), writing a Python program can be as simple as prompting an AI assistant for a solution. For research scientists, this lowered barrier could spark a generational change, transforming every research stage from idea conception to publishing. My interest in AI-assisted programming inspired my lecture on how Python can assist PhD students in their work. This lecture begins with basic terminology and presents three mini projects as first steps for learning Python. These projects cover text extraction and analysis, statistical data analysis, and virtually staining histological samples into digital image stacks. Small variations of these projects could lead to code that extracts reagents from manuscripts, creates stunning statistical graphs, and advances histopathological methods.
