To segregate value from information, Data Science combines several fields such as statistics, logical strategies, Machine Learning (ML), and data analysis. Data scientists are people who work in the field of data science. The primary goal of Data Science is to discover patterns in data. It analyses and concludes data using various statistical techniques.
People who want to be Data Scientists spend a lot of time writing code. However, when we look at the big picture, the core of Data Science is not writing code, but comprehending data and extracting value from it.
The coding portion is simply a means for accomplishing this objective. One cannot dodge writing code, and doing so is likely to be detrimental to the process; however, one can reduce the amount of time spent doing so. This article discusses the best methods for writing reusable code for data science projects.
Modular
Modular code denotes that our code is divided into small, independent parts (such as functions) that each does a single task. Each function, whether written in Python or R, is made up of several parts:
- The function’s name.
- Arguments for our function – This is the data we will pass into our function.
- The function’s body – This is the section in which we define what our function does. In general, we will write the code for our function and test it with an existing data structure before incorporating it into a function.
- A return value – This is what our function will return once it has finished writing. We must specify what we want to return in Python by adding return (thing to return) at the bottom of our function. In R, the output of the last line of our function body is returned by default. It is one of the most effective methods for producing reusable code for data science projects.
Readable
“Readable” code is code that is simple to understand even if it is a person’s first time seeing it. In general, the more words that describe what variables and functions do/are, the easier it is to read the code. Furthermore, comments describing what the code does at a high level or the reason for making certain choices can be useful.
Names can be enhanced by following a couple of regulations:
In variable names, use some method for indicating the spaces between words. Because one can’t use actual spaces, snake_case and camelCase are two common ways for doing this. One will most likely be suggested by the user’s style guide.
Use variable and function names to describe what’s in the variable or what a function does. Sales_ data_jan, for instance, is more descriptive than just data, and z_score_calculator is descriptive than just calc or norm.
It’s fine to have less-than-ideal variable names when we’re still figuring out how to write a piece of code, but once we’ve got it working, it is recommended to go back and improve the names. It is one of the most effective methods for generating reusable code for data science projects.
Adaptable
Adaptable code solves an issue that will occur more than once and forecasts data variation.
Data scientists must do and know a wide range of tasks: we’ve presumably got a better use for our time than meticulously polishing every line of code we ever write. Investing time in polishing our code begins to make sense when we know it will be reused. It is one of the most effective methods for generating reusable code for data science projects.
Creative
It is critical to be creative when writing reusable code for data science projects. It encourages us to look for pre-existing libraries or modules to solve our problems. If someone else has already written the code we require and it is available under a license that allows us to utilize it, we should presumably just utilize it. It is one of the most effective methods for generating reusable code for data science projects.
