Programming is one of the important skills that a data analyst should be proficient in. Data analysts communicate with a database using a structured query language. However, they need R or Python when it comes to visualizing, analyzing, cleaning, and manipulating data.
Difference between R and Python
R and Python are free languages that can be run on:
- Windows
- Linux
- MacOS
These programming languages are capable of handling all data analysis tasks. They are considered to be very easy in terms of learning for beginners. Deciding on what language to learn first entails knowing the difference between them. Having an idea of what they are could help you decide.
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Python
This is a general-purpose language. It has an intuitive syntax. It is almost like a natural language. With Python, you can work on many types of tasks. The most popular tasks associated with Python are:
- Web application development
- Data science and analysis
A high-level programming language means that it has a syntax that is easily read and understood by us. Low-level language cannot be understood by humans, only machines can. High-level languages in programming include:
- Java
- C#
- C++
- Python
When code is written using high-level languages, it has to be converted to a low-level format. This is what a computer can run and recognize.
R
This statistical and environment programming language is built for data visualization and statistical computing. This language has many abilities, including:
- Data visualization
- Statistical analysis
- Data manipulation
Choosing between the two
Regarding R or Python, there isn’t a wrong choice because both languages are what we call in-demand skills that allow a data analyst to perform his duties. However, the better option usually depends on career goals, interests, and background.
Considerations to make include:
The numbers
Programming indices such as redmonk, PYPL, stack overflow, and TIOBE show Python being more popular than other tech languages. This may not mean that the language is better. It means that it is more used than others in terms of development and support.
Learning curve
R and Python are easy to learn. On its creation, Python was meant for use in software development. With C++ or Java experience, Python can be straightforward to learn. R is easier to grasp if you have a statistics background.
When comparing the two, you notice that python’s syntax is much easier, offering a smooth learning curve. R is steeper when starting. However, things get much easier when you start grasping the features. Once you learn one language, learning others becomes easier.
Company
Your choice of coding language also depends on the company you will be working for. You should learn the language they use to make it easier to collaborate on projects and share code. However, for someone starting, you may still need to find out where you will end up. In such a case, check company job listings or industries in which you may want to work. Find out whether they list Python or R requirements. This can help you decide.
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Weaknesses and strengths
The two languages can handle similar data tasks but have their strengths. Therefore, if you spend too much time on some tasks, consider making the language that handles such tasks a priority.
Python is ideal for:
- Handling tasks that are non-statistical such as running workflows, web scraping
- Building Models for deep learning
- Handling a lot of data
R is ideal for:
- Statistical packages
- Building statistical models
- Creating data visualizations and graphics
Career path
Pick a language that meets your career goals in the long term. For example, R is a good fit if your passion is data visualization and statistical calculation. Python is a good choice for those interested in the following:
- deep learning algorithms
- artificial intelligence
- big data
You have to consider whether your professional or personal interests go beyond just data and into computer science, development, and programming. In such a case, Python can be considered general purpose and handle more tasks than R.
Learning Python or R
The two languages are a good choice for data analysis. However, they are equally suitable for beginners with a form of coding experience. In addition, some materials can push you forward and allow you to get started.
Guided projects
You can get a taste of each through guided projects to find out which language you prefer. Guided projects are a great way of offering hands on introduction to you without demanding software download.
Guided projects handle the basics of the languages. For example, with R, it can be basic commands or learning to import data sets or install packages. Python may handle creating loops, decision constructs, and variables.
Practicing Python skills using tutorials
These step-by-step guides can be used to troubleshoot basics like handling loops, if-else exceptions and statements, and syntax. The contents of a tutorial depend on who or where it is being offered.
Taking a course
In case you decide to focus on a single skill at a time or when you want to add a coding language to the already existing skill set, either of the two can push forward. The choices differ from one-course provider to the next. Get to understand the contents of each course before getting started. It is also important to ensure that the course you pick adds value to you and the path you have picked for your career. An idea of the company or industry you might end up in can help you immensely.
Bottom line
For aspiring data scientists, R or Python are great programming language options that you can choose to learn. You should know that R emphasizes number crunching and statistical analysis. This is what makes it such an ideal choice for data analytics. As for Python, it is a very versatile choice and has wide usage in other fields. If you hold interests in other areas, Python could stand out.
Python and R have advantages and disadvantages that depend on factors such as:
- personal preferences
- the team language of choice
- the intended use
However, learning one language allows you to pick up the next. The skills you acquire in one language can be transferred to other languages.
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