Part 3/8:
Focus on mastering the fundamentals and actively build automation projects. Learning elements like web scraping and familiarizing yourself with Python libraries like NumPy, Matplotlib, and Pandas will enable you to work competently with data sets. Understanding APIs and integrating them into your projects is also essential. The goal here is to write code practically, paving the way for more advanced topics down the line.
Step Two: Become Data Literate
In the realm of AI and ML, data is king. Being data literate means understanding how to manage, manipulate, and visualize data effectively. This stage involves learning SQL, including basic joins and select statements for querying data, and gaining deeper knowledge of libraries like Pandas for data manipulation.