Got yourself a computer science degree? Why not consider a data science career? You’ll be at the forefront of exciting new innovations. Technologies that inform the world of tomorrow, like AI or the metaverse, all come from data scientists. What’s more, the industry is booming. By 2030, it’s predicted to have a value of $378.7 Billion.
If you’re realistic about a career in data science, there’s one skill that is essential. We’re talking, of course, about coding. There’s no denying it’s a daunting concept. And with so many languages to choose from, where do you even start?
Luckily, this article is here to put your mind at rest. We’ll look at some top programming languages that can get you started. We’ll also uncover some of the questions that people have about Data Science Programming languages and explore the latest Data Science trends.
So, let’s get started!
There’s a good chance that you’ve heard of Python already. There’s a reason for that; it’s the most popular programming language in the world and according to the TIOBE index, it’s held this title since 2001. Platforms that we all know like Google, YouTube and Spotify, were all written in Python. Given its continued longevity, it’s a good idea to learn Python for data engineering. Let’s look at some of the reasons for Python’s popularity:
Pros of Python
It’s excellent for beginners: if you’re new to coding, there are few better places to start than Python; it’s one of the easiest programming languages to learn. Even if you’re looking at code written by someone else, you should be able to understand it without too much difficulty.
It boasts a huge community: one of the advantages that comes with having a huge user base like Python’s is community. There are thousands of free online resources to help you get started and you can join groups of professionals for advice.
It’s great for AI development: there’s no denying that AI will transform the future. From product recommendations to AI-generated art, technology is already making an impact. Python is a great choice if you want to be at the forefront of this change and is considered the best language for AI development. Why not set yourself up an AI domain and use Python to create the next big thing?
It’s extremely portable: one of the biggest issues with some programming languages is the lack of compatibility. You might have written your code to run on one platform. But will it work on another without requiring you to make any alterations? Python runs on most platforms, making your life easier.
Kubernetes Monitoring enables proactive identification of compatibility issues, resource utilization, and efficient troubleshooting, ensuring smoother deployments and improved compatibility across diverse environments.
It’s great for building APIs: let’s say that you’re developing something that requires multiple apps to exchange data. In this case, you’ll need an application programming interface (API); Python is one of the most efficient languages for building APIs (although you might need the help of an API guide to ensure that you interface properly).
Cons of Python
It’s not as effective for mobile users: there’ll be many times when you have to write code that will function on mobile devices. Sadly, this is where Python falls short: it’s not supported by either IOS or Android due to its low-rate processing.
It uses lots of memory space: Python is capable of carrying out many complex tasks but doing so requires a great deal of memory space. For certain coding tasks (i.e., those that need memory optimization), this could be an issue.
It’s difficult to test: it can take a lot of time to test software written in Python because before you can launch the output, every issue needs to be dealt with. Adding to this headache, the more systems you interact with, the longer the testing duration.
Like Python, Java is a commonly known programming language and is an excellent choice if you’re looking for a career in data science. Java has also been around for a while, first appearing in 1995 and since then, the language has grown significantly, today powering many popular web applications. It’s both secure and versatile and the chosen language by many programmers.
Pros of Java
You can write once and run anywhere: Java’s Creator, James Gosling, had a clear vision for his language. He summed this up in a single sentence “Write once, run anywhere.” With Java's capabilities and dedicated hosting solutions, your code can be sent to anyone, regardless of the platform they are using. If one team member is using Mac and another Linux, both would be able to run code without any issues thanks to Java.
It is secure: with 66% of small to medium-sized businesses having experienced a cyber attack in the past 12 months, you want software that is highly secure. Java contains many different security options, including a security manager that allows programmers to define the rules of access for classes
It has efficient memory storage: Java splits memory allocation into two areas. These are Stack Area and Heap Area, making memory storage more efficient and less demanding for your system.
Cons of Java
It has a slow speed and performance: if you’re looking for a quick programming language, Java probably isn’t the solution for you. Each code must be interpreted to the machine-level code, making Java much slower than some other popular programming languages.
It doesn’t have a backup option: losing code is never fun, especially if you’ve spent long hours perfecting it. Sadly, Java provides little help in this regard as it applies a greater emphasis on storage, rather than on backing up data. It’s not hard to see why this is a big issue for some programmers.
It’s difficult to read: compared to a programming language like Python, Java is much more difficult to read. The language has been described as ‘verbose’. Each piece of code is made up of long sentences and can take you a while to decipher.
Let’s think for a moment about databases. They’re used by businesses of all shapes and sizes. An organization can dip into a database and get the information that they need in seconds. But how were they able to do so? Well, it’s all thanks to SQL; with SQL, we can modify, withdraw, and interface with data--an essential programming language for any data scientist.
Pros of SQL
It’s quite responsive: queries are processed in seconds with SQL data modeling tools. When you need to find an important piece of data, you can do so quickly. With SQL, you can withdraw large amounts of data without any problem.
It requires minimal knowledge: one of the great advantages of SQL is that you don’t need to be a master coder to jump in. You can manage a database using only small amounts of code and the language is completely made up of English statements (eg. INSERT, UPDATE, DELETE).
It’s a standardized language: SQL is a standardized language that has been approved by ANSI and ISO. This means that it has detailed documentation and is recognized globally.
Cons of SQL
It lacks control: thanks to hidden corporate rules, SQL doesn’t afford a programmer full control over a database. This means you’ll be limited in what you can do with your data.
It can be expensive: some versions of SQL can burn a hole in your wallet. If you’re using SQL Server Enterprise, for example, you’ll be paying a whopping $5,434 per year.
Start Your Data Science Journey Today!
If you’re after a career change, data science represents an exciting opportunity. But the concept of learning a coding language is a barrier to many. Hopefully, after reading this article, the idea seems a little less daunting.
As you can probably tell, there is no shortage of programming languages. And many of them are accessible to beginners! But regardless of whether you are new to coding, or just fancy learning a new language, you’ll have access to a huge community online.
Any of the languages listed here could open the door to data science. Why not choose one and jump in?