How to use R and Python for data science

With both Python and R being open-source languages, the use cases for each are virtually endless. From data visualization and analytics to workflow automation and application development, these two codes enable you to complete both everyday and complex business tasks with ease.

What is Python?

Python is considered to be one of the most readable coding languages available thanks to its extensive use of white space. Because it’s an open-source language, it’s widely used among both beginner and professional developers and programmers for tasks such as website development, machine learning, data visualization, and more.

The general-purpose, object-oriented programming language is ideal for production and deployment tasks such as data processing, software testing, application development, and scientific computing. Additionally, it features high-level data structures which make it a popular choice for machine learning tasks.

What is R?

Similar to Python, R is an open-source programming language. However, R is built specifically for data visualization and statistical analysis. The programming language features an extensive library data modeling tools that allow you to create in-depth data visualizations using just a few lines of code. The programming language is frequently used by statisticians, scientists, and researchers for its rich data models and robust analytic tools.

The advantages of R and Python in business

According to MicroStrategy’s 2020 Global State of Enterprise Analytics report, 94% of businesses identified data science and analytics as being a key to their digital transformation. Those who are investing heavily in analytics are increasing productivity, streamlining decision making, and improving their overall financial performance.

Many of the tools used for data science are built on R, Python, or a combination of both. In fact, more organizations are realizing the benefits of using both languages for a variety of data science tasks. Where one language has its limitations, the other does not, enabling teams to create robust, flexible applications that enable you to go far beyond simple data analysis.

R and Python use cases

Both R and Python can be used in a variety of ways within the data science realm. Below are just some of the ways in which companies across industries have utilized the programming languages’ tools and libraries to reach business goals.

Finance

R is frequently used within the financial industry for tasks such as financial data mining, risk assessment and management, time-series analysis, and stock market modeling. R’s powerful data visualizations support the decision-making process by presenting data in a variety of formats, including density plots, candlestick charts, and more. This helps to bridge the gap between complex, technical data and real-world application. Top financial companies such as American Express, Citibank, and Bank of America each utilize R in the technological infrastructure.

Additionally, tools like Domo’s Magic ETL are built on both R and Python and are used for lending optimization. MagicETL leverages model insights that allow for prescriptive early warning systems to target and remediate default risk early on in the process. This allows Domo to optimize underwriting via applicants who pose the greatest risk.

Data science

Both Python and R are used in a variety of AI tasks—Python in particular. Powerful Python libraries such as SciKit-Learn and TensorFlow are ideal for creating and implementing machine learning algorithms. R also features powerful tools such as C50 (a tree algorithm building application) and Gmodels (an application that compares the relationship between two nominal variables) are also ideal for AI tasks.

Manufacturing

Manufacturers looking to predict future demand, streamline processes, or improve satisfaction utilize tools and capabilities from both R and Python. The Philips semiconductor facility in Fishkill, NY utilized Python to completely rebuild their system architecture, a move that resulted in a faster development cycle and lower maintenance. Ford relies on R to analyze customer feedback which helps them improve marketing strategies and improve future designs. Domo uses a combination of both R and Python to identify bottlenecks and friction points involving product anomalies or outages and leverages early warning systems to optimize the production process.

The future of R and Python in business

As more data becomes available, businesses are constantly looking for new ways to gather, analyze, and use this data. While Excel spreadsheets worked in the past, the sheer amount of data available today has made the tool obsolete.

Programming languages such as R and Python are the frameworks on which the data science tools businesses so desperately need will be written on. The rich data visualizations featured in R and the versatility and power of Python make it ideal for machine learning, AI, and big data tools that will only continue to rise in popularity.

Additionally, solutions such as Domo’s Magic ETL are eliminating the silos created when these two programming languages are used separately. This enables teams to build more robust and versatile solutions that are crucial for surviving today’s rapidly changing business environment. By productionizing this code, the use cases and options available to current and future businesses will only continue to expand.