Everyone follows a different style of coding. There is no hard and fast rule on how the developing problems must be approached or how the solutions should be implemented. However, when it comes to software engineering some standards must be followed.
For instance, suppose some data scientists and a few others teams are working together on a single project. For this, the code needs to be open-source so others can access and work simultaneously with everyone else on the team. The code can also be further used as the production code. Therefore, certain standards have to be followed.
You need to follow certain coding practices which will help you work well with a team.
Let us discuss the top 10 software engineering best practices for data scientists.
Keep Your Code Clean
One of the most essential aspects of coding is a clear, concise code that is readable as well as understandable. This is crucial for effective collaboration and maintenance.
Here’s how you can keep a clean code:
– Make use of meaningful variable names i.e., descriptive & imply type
– Do not use abbreviations that no one can understand
– Do not hard code “magic numbers” in your code
– While naming your objects, try following PEP8 conventions
– Include indentations & whitespaces properly
Make the Code Modular
When a code is organized into logical functions & modules to be reused later in the same project, it is called modular code. It allows you to maintain the code easily and helps you find the pieces of the code that you want to reuse more quickly.
Here are some tips to follow:
– Do not repeat yourself
– Minimize functions & classes
– Functions should only do one thing
Refactor the Code
This aspect recognizes the internal structure of your code with no alterations in its functionalities. Refactoring is done on a working version of the code, which helps in de-duplicating the functions as well as recognizing the file structure. It further helps in adding more abstraction to the code.
Make the Code Efficient
The efficiency of the code can be enhanced in two ways – reducing its execution time and reducing its required memory space. Here is how you can write an efficient code:
– Check the algorithm’s complexity before running anything
– Inspect the running time of each operation to check the script’s possible bottlenecks
– Vectorize the operations & do not use for-loops
Consistent Code Style
Learn the syntax conventions of the programming languages and use the conventions properly. This will help you write clean code and communicate with other developers in the team using the same programming language.
You can use pre-existing libraries to save time. For instance, Python has a huge set of libraries that can handle every type of request a data scientist could throw. Here are some of the most useful libraries that you can use:
Proper documentation of the code is necessary because it helps in the clarification of the complex parts. It can help you to correctly describe to others what the purpose of the code or its specific components is. There are three types of documentation that you can follow –
– Line level documentation
– Function/Module level documentation
– Project level documentation
Using a version control system can have a lot of perks. It can help you keep track of all the changes and allows you to roll back on any previous version of your code if required. The merge and pull requests make the team collaboration more efficient. Moreover, it not only increases the code’s quality but also helps in code review and task assignments of the processes.
One way to make sure that your code is performing well and is following what you designed it for is – Testing!
Write tests to check the behavior of the code. Here are some ways how writing tests will benefit you:
– It helps in spotting mistakes more quickly, making the code more stable
– Helps prevent unexpected outputs
– Can easily detect edge cases
Monitor and track the progress of your code on every step after running its first version. Here’s how you can use logging efficiently:
– Make use of different levels according to the messages that you want to log i.e., debug, warning, info, etc.
– Provide information in logs that helps in solving the related issues
Upskill Yourself with the Right Digital Transformation Partner
Cognixia – a world leader in digital talent transformation – is committed to delivering exceptional training & certifications courses in digital technologies that are designed to help you shape your future and make the most of the rapidly evolving technologies. We strive to deliver the best online learning experience to both individuals & organizations via highly interactive & customized courses.
Cognixia strongly believes that a practical, hands-on approach is the key to meaningful learning & skill development. Keeping this in mind, we integrate real-life exercises alongside some other activities throughout our training sessions, with long-term retention of learning & development in mind.
Learn Data Science with Python
Over time, Python has become one of the most popular and preferred languages in Data Science. And when it comes to building ML systems and performing regular data science & analytics functions, Python offers a powerful as well as a flexible platform to build on.
Taking a hands-on approach, Cognixia’s Data Science with Python training course provides learners with the opportunity to experiment with a wide range of data science and machine learning algorithms.
Designed with the industry’s most sought-after skills in mind, this online data science with python course provides you with a solid foundation in data science & machine learning with Python expertise, ensuring you get a fair opportunity to build a promising & successful career in data science.
Our Data Science with Python training program covers:
- Introduction to data science
- Data science project life cycle
- Basics of statistics
- Discrete and continuous distribution functions
- Advanced statistics concepts
- Introduction to Python programming, Anaconda, and Spyder
- Installation and configuration of Python
- Control structures and data structures in Python
- Hands-on applied statistics concepts using Python
- Functions and packages in Python
- Graphics and data visualization libraries in Python
- Introduction to machine learning
- Machine learning models and case studies with Python