{"id":1348,"date":"2019-02-15T10:32:04","date_gmt":"2019-02-15T07:32:04","guid":{"rendered":"http:\/\/kusuaks7\/?p=953"},"modified":"2023-08-08T14:13:40","modified_gmt":"2023-08-08T14:13:40","slug":"the-effect-of-naming-in-data-science-code","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-effect-of-naming-in-data-science-code\/","title":{"rendered":"The Effect of Naming in Data Science Code"},"content":{"rendered":"

Ready to learn Data Science? Browse courses<\/a>\u00a0like\u00a0Data Science Training and Certification<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

\"experfy-blog\"<\/p>\n

Even though there are tools allowing to practice data science without coding, they are far from sufficient. Data scientists will be writing and reading code. Reading code that has poor readability is a horrible experience. This post focuses on the importance of naming entities (e.g. variables, functions) and how easily it improves the quality of your code.<\/p>\n

\u201cThere will be\u00a0code\u201d<\/strong><\/h3>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u201c\u2026 I also expect that the number of domain-specific <\/em><\/h4>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0languages will continue to grow. This will be a good thing. <\/em><\/h4>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0But it will not eliminate code.\u201d<\/em><\/h4>\n

wrote\u00a0Robert C. Martin<\/a>\u00a0in the first page of his book\u00a0Clean Code: A Handbook of Agile Software Craftsmanship<\/a>, in the first chapter called \u201cthere will be code\u201d. We, data scientists, write and read code. Even though there are helping tools, we will be still writing and reading code. It is one of our core practices. This is how we analyze the data, train models, predict outcomes and many more. I strongly believe that there is no escape from code for a data scientist.<\/p>\n

We can still practice data science without coding, at some level. From early days to today there were graphical tools that allow analyzing data or practicing machine learning. One example of such tools is\u00a0WEKA<\/a>. WEKA is a bundle of machine learning tools with a graphical user interface. According to Wikipedia, its development started in 1993*<\/a>. It allows users to conduct machine learning experiments and more without writing a single line of code. Then why I insist on saying writing and reading code is fundamental to data scientists? Because there will be custom operations.<\/p>\n

Graphical tools have only so many operations. If you are not doing same tasks everyday, there will be a time when tools will not have the operation you need. This can be an analysis, a machine learning model, or some other operation. You need control, customization, and expansion over your operations at some level. The level depends on the task, libraries, or tools. As many problems require new or custom approaches, it is quite soon that you will overgrow those graphical tools.<\/p>\n

It is seldom that we work alone. Often organizations do not have a single data scientist, they have data science teams. Even if it is not the case, data scientists work with other disciplines. This collaboration requires good data science code quality.<\/p>\n

Moreover, if you are working for a company, your colleagues from other teams will need your code. In order to embed your models into backend, frontend or another system, you will need code. A model that cannot be deployed or integrated would be useless for your company.<\/p>\n

If you agree that there will be code in data science, let us talk about how to write good code for data science. Writing good code is a hard task. There are well written texts explaining why it is necessary and how to achieve it. In this post, my goal is to focus on a tiny bit of that. The bit that requires no training or education, but will improve your code quality significantly. That bit I will focus on is\u00a0naming,\u00a0<\/strong>and it will improve the\u00a0readability of your code<\/a>.<\/p>\n

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One Easy Trick:\u00a0Renaming<\/strong><\/h3>\n

It is a horrible experience reading a piece of code that has poor readability. Let us look at this simple example below:<\/p>\n

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Try to guess what this code does. Author of this code knew the goal of each line, and the goal of entire code while she was writing it. However, as a reader you see a code snippet you need to decipher. As the author did not pay attention to readability, you will spend more time and energy trying to understand this code. Furthermore, you will be more prone to make mistakes. Let us dissect this example to see why it is bad:<\/p>\n