Updated: Mar 4
There are multiple data analytics platforms available: Microsoft Excel, R, Python, SAS, and SPSS, to name a few. Ask yourself some of these questions before choosing a programming language.
1. What programmes do you have access to?
Does your company require the use of licensed products like SAS or SPSS, or would they be using open-source software - software that can be used by corporate and individual users for free - like R and Python?
If you only have access to Excel, take note that while it is ubiquitous, Excel is not able to handle large amounts of data and does not give the user as much control when manipulating data sets. It would be better to learn a new programming language to make sure you're able to deal with the demands of large data sets of the digital age.
2. Are you willing to pay to use the programme?
Willing to pay in exchange for ease of use? Check our SAS and SPSS' packages, which can range from $100 to $800 per month for the most basic packages.
Not willing to pay? You're not the only one. In fact, programmes like R and Python are popular specifically because despite being open-source, they are well-equipped with functions and packages for data analysts. However, their learning curve can be steeper than paid programmes.
3. What will you use the programming language for?
Do you want to use data to make better business decisions? Learn R programming. It is easier to access data analytics functions and do complicated analytics, allowing you to systematically produce insights using data. However, the learning curve can be steep, especially for beginners that want to self-learn the language.
Do you want to easily extract on and analyse data from software and web apps? Learn Python programming. It is easier to read and maintain code in Python, but remember that Python's libraries can be hard to configure, and basic concepts are more abstract in Python.
4. Why do you want to learn programming?
Is there a specific function that you require? For instance, most people would use R for more statistics, complicated analytics and data visualisations. On the other hand, most use Python to integrate code with software or web apps.
Is it for your own personal interest and/or professional development? If so, what are you hoping to achieve with your new skills?
If you want to be a data analyst, it would be best to learn more programming languages as proficiency in more languages gives you higher flexibility, allowing you to use different languages for their respective strengths; for instance, if you are fluent in both R and Python language, you can use R when you need high statistical power, and Python when you need to integrate your findings with other platforms. This will also make you more employable than those that can only use one.
If you are learning to programme to stay relevant in the digital age and don't have a preference, we suggest learning R. R is platform-independent, which means that skills learnt here can be applied to other platforms as well. From there, you can get a better gauge of what you want to specialise in and choose the right language for you.