This article will explore two of the most popular programming languages for data analytics, Python and R, to see which one ultimately comes out ahead for the daily tasks of a Data Analyst.
Using Python for Data Analytics
Python is an object-oriented, interpreted programming language that was developed in 1991 by Guido van Rossum. Its simple yet powerful syntax is easy to learn and read. Python supports a variety of programming paradigms in addition to object-oriented programming, including functional and procedural programming. It can be used as an extension language for apps that require a programmable interface. Because Python can run on Unix variants like Windows, macOS, and Linux, it’s an extremely portable and versatile language.
Python has several powerful libraries that are applicable to those working with data science or analytics. These free libraries are considered to be some of the most robust available for coding. Some of the most popular are:
- Pandas, which offers a variety of cleaning and analysis tools
- StatsModels, which contains popular statistical methods
- NumPy, which allows users to execute numerical computations quickly and efficiently
- Keras, which helps users construct deep learning systems
Most programmers currently working with Python use Jupyter Notebook to write and edit code. Documents are easy to create using Jupyter Notebook and can contain a combination of code, data, visualizations, and prose. This simplifies the process of documenting work and enables it to be reviewed and replicated by others.
Using R for Data Analytics
R was created in 1995 by two New Zealand statisticians, Robert Gentleman and Ross Ihaka. This free software environment is an implementation of a prior statistical coding language, S, which originated in the 1970s. The beta version of R was released in 2000, and in the time since, this powerful suite of tools continues to be used for graphing and statistical modeling.
Because R is an interpreted language, it doesn’t have to be run through a compiler prior to running the code. This extensible language enables users to easily call R objects from a variety of other programming languages. R code is typically written and edited using RStudio, a collection of open-source, free tools that allow data science teams to share work. The growing popularity of R has led to it overshadowing older and more traditional statistical packages, such as SPSS and SAS.
Which Comes Out Ahead for Data Analysts: Python vs. R?
While both Python and R are used by Data Analysts, they differ in several important respects:
- Popularity: Python and R are considered to be the two most popular programming languages for data science. Yet there is a clear winner in terms of overall popularity: As of September 2021, Python ranks second of all programming languages, whereas R is 18th most popular.
- Purpose: Python is a general-purpose programming language, focused largely on production and deployment. Users can collect, store, analyze, and visualize, all in Python. On the other hand, R is more tailored to data visualization and statistical analysis. R’s capacity for creating attending-grabbing data visualizations in the form of charts, graphs, and plots, makes this language a go-to for Data Analysts seeking to uncover insights such as trends, patterns, or outliers in datasets.
- Data Exploration: For basic data analysis needs, such as probability distributions, data mining, and clustering, R works well without the installation of additional packages. Python’s library Pandas allows users to easily filter, organize, and visualize data, a powerful tool for data exploration. Therefore, Python and R offer relatively equal data exploration options.
- Data Manipulation: Those using R can choose from several libraries for shape manipulation and data aggregation. On the other hand, Python’s open-sourced library, Pandas, is able to perform a variety of types of data manipulation. This popular tool is designed for data analysis and managing data structures. This single library allows Python users to streamline their interactions with data, and therefore it comes out ahead of R for data manipulation.
- Data Visualization: Even though Python has the Matplotlib library tool, with which users can create a variety of plots and interactive figures, Python is not considered to be as strong at data visualization as R. R incorporates charts, graphs, and plots to depict the results of statistical analyses in an engaging and aesthetically appealing format. In addition, one of R’s most popular packages is ggplot2, which allows users to construct nearly any type of graph or advanced plot. That means, for data visualization needs, R takes the lead.
- Ease of Learning/Use: Python’s syntax is easy to learn, and to read. That said, for those who have no prior programming skills, R is generally easier to learn than Python. In addition, users can begin performing data analyses immediately using R, and as they learn more complicated functionalities and analytics, they can begin to integrate these concepts into their programming. So, for Data Analysts who haven’t formally studied programming and are hoping to begin work immediately, R is the better option.
Takeaways: In terms of popularity, Python comes out ahead, yet in terms of purpose, R is more suited to Data Analysts due to its ability to display information via stunning data visualizations. Both R and Python work equally well for data exploration, whereas for data manipulation needs, Python’s Pandas library comes out ahead of R. While both Python and R are relatively easy-to-learn programming languages, for Data Analysts who may not be trained as programmers, R is a better option.
Hands-On Coding Classes
Learning to code is an in-demand skill for those working with data. It can open professional doors and also lead to upward career mobility within a Data Analyst role. Noble Desktop has a variety of coding classes available for interested learners. They are taught in-person in NYC, and are also available in the live online format. These classes and bootcamps cover topics like SQL, machine learning, HTML, CSS, and Python.
Noble Desktop’s Python bootcamps provide a great learning option for those who are interested in an intensive learning experience. Courses are available with a focus on topics like Python machine learning, Python for data science, and data science, among others.
In addition, over 100 in-person and live online coding classes are available from a variety of top providers. These small classes are designed for novice coders, as well as intermediate and advanced learners.
For those searching for a Python coding class nearby, Noble’s Python Classes Near Me tool provides an easy way to locate and browse nearly 100 Python classes that are currently offered in the in-person and live online formats. Course lengths range from six hours to 28 weeks and cost $399-$19,974. The Coding Classes Near Me tool can also be used to browse more than 500 coding classes. These courses are between two hours and 72 weeks in length, and cost between $149 and $27,500.