Here are the five most important things a junior data scientist must know about Python coding:
Data Structures: Understanding the fundamental data structures in Python is essential for a data scientist. These include lists, tuples, dictionaries, and sets. Being able to manipulate, access, and modify these structures is a crucial skill for data analysis.
Control Structures: As a data scientist, you’ll be dealing with large amounts of data, and control structures like loops and conditional statements will help you handle and analyze it effectively. These structures allow you to automate repetitive tasks and make decisions based on data conditions.
Functions: Functions are a powerful tool in Python, and as a data scientist, you’ll be creating and using them extensively. Understanding how to write reusable functions that take inputs and produce outputs will help you organize your code and perform complex operations with ease.
Libraries: Python has a vast ecosystem of libraries that make data analysis easier and more efficient. Numpy, Pandas, Scikit-learn, and Matplotlib are some of the most commonly used libraries in data science. Understanding how to import and use these libraries can help you analyze large datasets and visualize results effectively.
Good Coding Practices: Finally, as a junior data scientist, it’s important to develop good coding practices early on. These include commenting your code, using variable names that are descriptive and easy to understand, and adhering to the PEP 8 style guide for Python code. These practices make your code more readable, maintainable, and shareable, which is critical in a collaborative environment.