Data scientists have always been pictured to write clean and comprehensive codes. This gives competition and complexity to those who have just entered the realm of coding.
If you are looking to provide reliable with your neat codes, we will share some of the best practices. So you do not have to look any further for writing clean and readable codes.
Best Practices for Data Scientist to Deliver Big Data Solutions
Read Documentation
Every software or tool development firm that releases a convenient tool also releases documentation for its users. The tools that ease the work of any developer will only be fruitful when they truly know how to use them.
Junior coders fail to consult the documentation part, which makes the development phase extended. Also, developers may not be able to avail the absolute benefits of the library. Hence, it is the first and most essential point to keep in mind before beginning to code.
Documenting Your Code
This is a good practice to start to become a data scientist. Like any other good coder, you must also pen down about the use of your code.
Be it a simple one-line code or have multiple aspects within, giving a word about your code will further help your teammates or other people who use code after you. When your code becomes useful to many, it encourages you to do better.
Commenting where Needed
Before you develop a code, you research and study what senior and renowned data scientists have written. There you may find that these developers simply code for its readers by adding extra content above and in between the code.
You can also follow the same pattern when documenting your code. Explaining the input and the output or the logic used in the function can make your code more comprehensive.
Designing the Code
This should be the second most essential step. You must evaluate the purpose of your code as well as the input and the output of the code.
The input and output: Deciding on what you will write depicts the output and input. You can either have a request from business stakeholders or a type of JSON you found online.
Purpose of the Code: Every step must have a reason, so think before writing the code. Evaluate the short term as well as the long-term goal your code will deliver. Also, project what your code will achieve in any project phase?
Remove Redundancy
When you write content, you don’t need to write it 100 percent correct in the first place. Your code requires analysis before deploying and documenting. You can try by writing codes on Notebooks (Jupyter) and see the outcome so that you can make amendments where needed.
To make things simpler, you can write down your code in the draft notebook and implement the functionality step-by-step before transferring the code to a final notebook. This is a great way to organize the code and have only the useful lines in your code.
Following Naming Convention
Engineers must follow a few naming guidelines. Such as
- Variable names starting with a lowercase letter and the next word in the variable name’s first letter must be capitalized.
- Function names must start with a capital letter.
You can decide on the style you want to follow – capital letter (NewVariable) or no capital letter (newvariable), underscore (new_variable), or no underscore (newVariable). Name your variables, functions, and classes based on their role. Keep it simple and short for others to read easily.
Utilizing version control
This means keeping the code on the renowned GitHub. Your changes made in the code will be kept confidential without breaking anything. This helps in the future in different ways –
- Your code does not get deleted when you seek it.
- You won’t have to rely on a single developer for the code and worry about him leaving the company.
You can actively use the Version Control feature during the project. This helps you stay organized and welcome new people to join your team.
Use the Efficiency of Debugging
A brilliant way to find what goes wrong when your code breaks. Also, the most common skills that data scientists lack and pull them from becoming brilliant coders.
You can avoid this problem by inculcating this skill in you. You must know ways to communicate your code. This could include changing values in variables, commenting out lines, printing the value of variables to analyze everything working as expected.
Getting too eager to fix all the problems at once can lead to longer debugging. Hence, you should change variables that cause trouble one by one or at max two at a time.
To Conclude –
If you observe data solutions providers closely, you will find them downloading data, drafting notebooks, writing snippets, and working on purposeful inputs and outputs. Keeping the code safe with GitHub will free you from saving code.
Take time to structure your code at the beginning of a project in order to avoid hours to bring things right.
