More people are becoming interested in open-source and open-access tools, with many proclaiming them the future of science and technology. Offering multiple options for both users and creators, open-source software and tools make it easier to collaborate on projects and modify programs to fit your needs. These tools also provide an ease of accessibility that can sometimes elude users when engaging with proprietary software. Consequently, we are seeing open-source software expand across fields and industries.

From user-generated libraries to programming packages and online forums, open-source software not is not only known for its technological capabilities but the community that coalesces around these tools. Now, with so many open-source tools to choose from, we are also seeing what the open-source movement can do for data science. This article offers an overview of some of the reasons why data scientists should use open-source software, as well as a list of the top five open-source tools within the field and how you can learn more about them.

Why Data Scientists Use Open-Source Software and Tools

The popularity of open-source software and tools amongst data scientists is primarily tied to the affordances of open-source vs. closed software. Open-source software, or OSS, is any software that has a license that allows it to be edited, modified, updated, and changed. In contrast, proprietary, or closed, software tends to be licensed to whoever purchases it and includes some restrictions or reservations when it comes to what a user can or cannot do with the software. A primary reason that data scientists use open-source software is the ability to modify the programs and code that the software relies on.

Open-source software is also a part of the open-source movement, which created a community of programmers who are committed to editing and updating programs and code. Using open-source software is especially useful for data scientists that want to work with libraries and other data science tools that are regularly updated. open-source communities also ensure that there are more tools and content available to work with particular data sets or to perform specific types of data analysis. In this sense, open-source software not only encourages community, but education, collaboration, and contributions to both small and large-scale data science projects.

With so many data science tools to choose from, it can also become quite expensive for data scientists to purchase multiple proprietary software products. Especially if you are a data scientist working outside of a company or large corporation, there are many barriers to accessing the more widely used enterprise tools. Open-source software is an excellent option for people that want to work with a variety of tools, but don’t have the means or need to access more expensive software. By working with open-source tools, you can also get a better idea of the different types of software that are used within the field, which helps to increase your skill set and makes you a more attractive candidate to prospective employers.

5 Top Open-Source Tools for Data Scientists

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Noting the many reasons that data scientists use open-source software, the following list includes some of the most popular open-source tools within the data science industry. Each of these tools is not only known for being open-source but also being easy to use and accessible to users from different backgrounds and with different goals.

1. RStudio

With R being known as an open-source programming language, RStudio is one of the most well-known open-source tools for data scientists. RStudio is also compatible with Python, another open-source programming language, and it includes several open-source software products including RStudio Desktop, RStudio Server, Shiny Server, and the many R packages that can be downloaded onto a hard drive or used within a web browser or applications. In particular, R is known for its data science packages and libraries, such as the tidyverse, which exemplify all of the collaborative and community-based benefits of using open-source tools.

2. Apache Spark

Compatible with multiple programming languages, Apache Spark is an engine that is primarily used with SQL. Offering the unique feature of working with distributed datasets, data-frames, and Apache-specific algorithms, this tool is useful to data scientists who are interested in machine learning and text mining. Similar to other open-source tools, there is a large community of users that contributes to the Apache software foundation.

3. TensorFlow

Presented as an open-source machine learning platform, TensorFlow offers several resources to data scientists that want to train and build models and recommendation systems, as well as work with artificial intelligence. Communal spaces, such as the TensorFlow Forum also allow users to gather resources and give feedback about the platform, in addition to troubleshooting problems and sharing projects.

4. Apache Hadoop

As another open-source platform from the Apache Software Foundation, Hadoop operates using the Java programming language and includes its own community of unique users. Hadoop is a software library that includes a variety of large-scale datasets that can be accessed from one or multiple computers at the same time. Due to this fact, Apache Hadoop is a great tool for data scientists that are working collaboratively on a project or as part of a team.

5. RapidMiner

As an “end to end data science platform,” Rapid Miner can be used for every stage of the data analysis process, from the preparation and organization of data to the creation of machine learning models and visualizations. Primarily used for predictive analytics, the RapidMiner Studio software is committed to supporting an open-source community of business analysts and data scientists who can update and improve upon the software.

Want more open-source data science tools?

Noble Desktop’s data science courses offer instruction in some of the most popular open-source data science tools. For students and professionals interested in learning R, the Data Analytics with R Bootcamp includes a comprehensive overview of this popular open-source programming language and its packages. Live online data science classes and bootcamps also have similar curricula to offline classes, so students have the option to take in-person data science classes from multiple locations. Find a class near you and start learning more about open-source data science tools!