Data-Oriented Python Programming and Debugging

In “NumPy and Pandas Basics for Future Data Scientists,” learn programming techniques using Python's NumPy and pandas libraries to write efficient and bug-free code for numerical computing. At the start of the course, you’ll be introduced to the NumPy library and will learn to perform basic NumPy array operations. After understanding the basics of the NumPy library, you’ll explore more advanced array manipulations, including aggregating functions, broadcasting, reshaping, sorting, and joining arrays. By the end of this course, you will have the skills to apply multiple data manipulation techniques using advanced methods and apply functions to your code. This is the second course in the four-course series, “Data-Oriented Python Programming and Debugging,” where you’ll work to strengthen your programming capabilities and enhance your problem-solving skills.

In “Python Debugging Capstone Project: Fixing and Extending Code,” you will undertake a comprehensive coding project that demonstrates your ability to use NumPy, pandas, and SciPy for advanced data manipulation and scientific computing. You will use the skills and best practices learned throughout the series to debug and analyze real-world data. This project will involve working with complex datasets, requiring you to implement data structures and handle missing data. You will engage in systematic debugging using the OILER framework – Orient, Investigate, Locate, Experiment, and Reflect – learned in the first course within the series and other advanced troubleshooting techniques. By the end of the course, you will know how to properly document your processes through clean, maintainable code notebooks.

In “Python Debugging: A Systematic Approach,” you will develop essential coding skills for data science, focusing on writing, testing, and debugging code. You will learn foundational Python concepts, such as looping, control structures, variables, and basic debugging techniques. You will also learn how a structured debugging procedure can help you debug more effectively and efficiently. Throughout the course, you’ll practice essential programming concepts such as map, filter, and list comprehension. You’ll learn how to take a systematic approach to debugging with the OILER framework – Orient, Investigate, Locate, Experiment, and Reflect – allowing you to spot errors more easily and adjust your code. In addition to frameworks to help you improve your code, you’ll explore how documentation, internet resources, and even large language models (LLMs) can help you identify and fix errors. By the end of this course, you should feel confident in your abilities to write clean, efficient, and reusable code. This is the first course in the four-course series, “Data-Oriented Python Programming and Debugging,” where you’ll work to strengthen your programming capabilities and enhance your problem-solving skills.