With the vast array of computer programming languages available, it is unsurprising that there is a wide range of opinions on which is the most effective. This is especially true when it comes to discussing which language is most suited to the implementation of the cutting-edge technologies, as no definitive answer has been agreed upon. The same is true when it comes to the field of data science.
Data scientists draw from a broad range of computer languages, such as Python, R, Java, SQL and even Scala, to manage large datasets and to fulfil a diversity of data science objectives. Although each language has its own unique pros and cons, the consensus among data scientists appears to be that Python is the best choice for data science. So, what are the reasons behind this preference? The following provides some insight.
First, however, let’s go over some data science 101 material.
How Do I Choose a Language for Data Science Programming?
In the present day, businesses are equipped with the capability to quickly accumulate and dissect vast amounts of data pertaining to their customers, markets, competitors and even entire industries. By utilising advanced data science algorithms, businesses are able to sift through these enormity of data and derive actionable insights, enabling them to predict seasonal changes, discover untapped markets and much more.
Developing strategies to address such a complex issue is certainly not an easy task. Raw data, which is data that has not been processed following collection from multiple sources, is notoriously unreliable. Following this, the parameters of the algorithm must be clearly defined, detailing its scope of application and its analytical processes.
Having the capacity to deal with multiple variables and form a dependable data-training set are essential for success. It makes sense then that software developers will go for a strong yet approachable programming language for developing the platform that does the data analysis. This is the reason why there are so many Python programmers in the data science industry.
Let’s quickly go through why Python is such a great tool for data science-focused software developers.
Python’s Advantages in the Field of Data Science
It is true that Python is not the only language that can be used for data science, but those new to the industry can benefit greatly from gaining experience with it. Python offers a number of advantages in the field of data science, the most notable being its ability to allow programmers to easily create programs for the training of machine learning models and the cleaning of data. Python is an incredibly useful language to have in the data science toolkit, and makes it simpler for programmers to tackle two of the most difficult challenges in this field.
Given that Python is freely available to the public, a vibrant and active community has developed around it, offering ready-made solutions to many common data science-related problems. By writing your code in Python, you will be able to take advantage of a wide variety of libraries and modules created by other Python developers, such as statistical code implementation tools and web-based data connection applications. This comprehensive collection of resources greatly simplifies the process of creating data science solutions in Python.
The Python programming language is a popular choice for developers specialising in data science due to the wide array of data science libraries available. StatsModels and Scipy are some of the most widely used libraries, and the Python community is constantly releasing new libraries, giving new data scientists plenty of ready-made tools to work with. Consequently, the range of data science libraries available to Python developers continues to grow.
Thanks to the generous support available from the Python community, if you experience any issues with your data science projects, you can find the help you need to get back on track. Experienced Python developers are often willing to provide assistance with particular issues in a range of online communities and subreddits. With so many people using Python, it is highly likely that you will be able to find the information you require.
Python is widely acknowledged as one of the easiest programming languages to learn. Therefore, if you are a novice in the software development field, it is possible to quickly get up to speed with the data science sector by studying Python and taking advantage of its libraries. Python offers a more viable alternative compared to other languages used in data science, such as R and MATLAB.
It is critical to stress that Python’s scalability is renowned and should not be underestimated. This is especially pertinent when considering the fact that data science projects and platforms are often required to simultaneously process large volumes of data and cater to a multitude of users. Python’s high throughput and low latency make it an excellent option for constructing data science algorithms, as these must be capable of rapidly analysing and interpreting large datasets.
Beyond Data Science
If you aspire to a career in data science, it is essential that you become proficient in Python. This programming language has become the go-to for data scientists, as it offers many benefits for their work, is relatively simple to learn, and has a wealth of pre-existing tools and libraries to assist with any task.
Despite being predominantly used within the data science community, Python also has an additional advantage that can be utilised by all programmers. Its impressive versatility has seen it become increasingly popular in many other sectors, such as web development and video game programming, presenting learners with a plethora of exciting opportunities.
Stop your search right now. Starting out in data science, Python is the only language you’ll ever need. It can accomplish whatever you can dream of.