07-04, 16:30–16:50 (Africa/Cairo), Hall 2 (Ground Floor)
Unlock the potential of clean code in data science!
Join our enlightening talk inspired by "Clean Code" by Robert C. Martin.
Enhance readability, maintainability, and efficiency in your data science projects.
Python is a powerful and flexible language. However, its flexibility can sometimes lead to suboptimal code. For instance, consider this snippet taken from a real-world codebase: {v: [] for v in [a for b in p for a in b]}.
Many data scientists who use Python on a daily basis lack traditional software engineering education, resulting in code that may be difficult to maintain and debug.
Fortunately, best practices of clean code in software engineering have existed for many years and can help to avoid these problems before they occur. In this talk, we will review fundamental concepts from the influential book "Clean Code" by Robert C. Martin. The book was written in Java, but I assure you that I have enough examples of bad code also in Python :)
We will discuss when and how to incorporate these concepts into your daily work, providing practical examples of clean code dos and don'ts in Python.
If you're a team lead, software developer, or data scientist interested in producing better code and spending less time debugging, this talk is for you. Join me to learn how to level up your team's skills and write maintainable, efficient code that will save you valuable time.
Hebrew
Target audience –Other (please specify below)
Other (target audience) –Managers, Data scientists and developers
Galit, an algorithm team lead at Mobileye and former first data scientist at the Ed-Tech startup Matific, is a big fan of Python and believes that clean code is crucial to producing high-quality software. She is always striving to improve her team's coding practices and promote the use of Python for efficient, maintainable code.