4 Software Development Techniques to Level up Your Data Science Project
By: Sara A. Metwalli
Tags: Data Science, Software Engineering, Software Development, Programming

Software development is the process followed by developers and programmers to design, write, document, and test codes. Regardless of what programming language you use or what your target application field is, following the specific guidelines of good software development is essential in building a high-quality, maintainable project.

Data science projects — may be more than other types of software projects — should be built with the mentality of maintainability. That is because, in most data science projects, the data is not constant and is frequently updating. Moreover, it is expected from any data science project to be extendable and to be crash-resistant. It should be immune to any mistake in the data.

Because every single part of the code in a data science project is build to fit a specific shape or form of data, if a wring data is given to the code, it might break it down. Of course, you never want your code to break, no matter what data it is fed. Hence, when designing and building the code, there are a few things to keep in mind to make your code more resilient.

There are many guidelines to follow to design and write good, stable code. However, in this article, we will focus on what I think is the 5 most important rules — or skills — needed to build a solid data science project.

So, let’s get right to it…

Documenting

We can’t talk about good software without mentioning documentation. Now, there are two steps to keep your code clean and well documented. The first step is commenting on your code. Comments are critical to walking people reading your code and — most importantly — your future self, through your thought process when you wrote the code.

Comments need to be simple, not more than two sentences, and straight to the point. Never forget writing a descriptive docstring whenever you define a class or a function or create your own modules. When writing comments, always remember:

Comments are not there to explain code to people; code is there to explain comments to the computer.

Once your codes and comments are done — well, for the time being since code is never done — you need to build sufficient documentation to your code. Documentations are external explanations of the code written — usually — in plain English. Documentations are often created using documentation processing tools, such as Sphinx and DocUtils. Documentations are often a part of your project’s website.

When it comes to best practices, it’s a good idea to start writing your documentation before you start coding. It will act as a guide to what needs to be done. Unfortunately, most of us — including myself — don’t follow this rule. However, we all need to start practicing it.

Testing

When we write code, we often write it based on some variables and datasets. However, it is very common that your code may contain some bugs that will only appear in some particular cases or with a specific dataset. Therefore, testing your application before deploying it can be crucial.

But, testing can get quite complicated, especially when it comes to data science projects. Often, data science projects are tested using reviews from other data scientists because most of the well-known testing methodologies are difficult to apply in case of data science projects.

That is because a simple change in data could lead to significant changes in the performance of the code. Through the years, researchers and developers have looked for the best way to test data science projects. They found out that the best way to test data science applications is through unit testing.

Unit testing is a type of testing that is used to detect changes that may break the flow of your program. They help with maintaining and changing the code. There are many Python testing libraries that you can use to perform unit testing.

Unittest is the built-in library in Python that is used to perform unit testing. Unittest is often referred to as PyUnit, and it is an easy way to create unit testing programs. Pytest is a complete testing tool — which is my favorite. Pytest has a simple straight forward approach to build and uses unit tests. Hypothesis is a unit test-generation tool. The goal of developing Hypothesis is to assists developers in creating and using unit tests that tackle the edge cases of your code.

Data Management

Getting a little bit specific to data science projects, when dealing with data, one thing we need to be careful with is managing our data. We need to consider many things, such as how are your data created? How big is it? Will it be loaded every time or stored in the memory?

When working with data, we need to be very careful with memory management and how the code is interacting with the data. One thing to consider is how Python functions call affect the memory usage of your code. Sometimes, function calls take up more memory than you realize.

One way you can overcome that is by using Python’s automatic memory management capabilities. Here’s how Python deals with function calls:

Every time you call a function and object is created with a counter of the number of places; this function is used. Whenever we use or reference this function, the counter is incremented by 1. When the code reference goes away form the function object, the counter is decremented by 1 till it hits 0. Once that’s done, the memory will be freed.

If you’re wondering how you can write code that uses this automatic memory management, wonder no more. Itamar Turner proposed 3 different way you can make your functions more memory efficient:

Try to minimize the use of local variables. If you can’t, then re-use variables instead of defining new ones. Transfer object ownership of functions that takes a lot of memory usage.

Using Domain-specific tools

Last but not least, to help you build resilient projects, make use of tools built specifically for data science. Of course, there are well-known tools, such as IPython, Pandas, Numpy, and Matplotlib.

But, let me shed some light on two not very known tools:

GraphLab Create: is a Python library used to build large-scale, high performing data products quickly. You can use GraphLab Create to apply state-of-the-art machine learning algorithms, such as deep learning, boosted trees, and factorization. You can perform data exploration through visualization, and you can quickly deploy your project using Predictive Sevices. Fil: is a Python memory management tool for data science. You can use Fil to measure peak memory usage in your Jupyter notebook. As well as to measure peak memory usage for normal — none Jupyter-based — Python scripts, and debug out-of-memory crashes in your code. Moreover, Fil can help in reducing your memory usage significantly.

Takeaway

Nowadays, building a good data science project is not enough to make you stand out. You need your project to be crash-resistants and memory efficient. That’s why using some software development skills; you can take your data science project to the next level and make it stand out.

The software development skills we discussed in this article are:

Efficient documenting and commenting. Testing, testing, and then some more testing. Wise data and memory management. Special tools that can ease up your work and increase the efficiency of your project.

What we didn’t talk about, though, is the most crucial skill any developer must obtain, which is the ability always to be working on improving your skills and knowledge base, as well as keeping up to date with recent technologies and tools.