Data science teams today look very different from the way they were just five years ago. No more data silos. No more fragmented workflows. No more being married to office desks or installing complex software or toolkits. With the pandemic forcing organizations to find remote working solutions, data scientists are no longer feeling the pinch of limited opportunities due to their locations.
Thanks to remote collaboration tools, data scientists can now work in powerful teams across the world and contribute to the organization just as much as the guy sitting at the office. That said, remote collaborations do add a layer of complexity that companies must navigate to further streamline data science workflows.
6 Top Remote Collaboration Tools Every Data Scientist Should Know About
While remote collaborations have become inevitable, data scientists must aim beyond basic remote desktop technology and prioritize a combination of project management, coding, and communication when choosing a tool.
Moreover, with the increased need for data security, privacy, and data-driven decision making, you must choose a tool that streamlines communication, workflows, makes sharing resources easier, and empowers your data scientists to constantly innovate and perform better.
#1 GitHub
Data scientists collaborating on a project, whether across the office or the world, often need to share numerous files. In most cases, you may not have access to robust network monitoring tools to help you eliminate data silos and maximize visibility across your network, especially when working on a cloud infrastructure. To work around this, use a cloud-based platform like GitHub to seamlessly upload and share files while emphasizing version control.
The features of the Git software help maintain the stability of your data files and ensure that all the data is easily retrievable, even if multiple people are working on the same dataset. Moreover, GitHub also offers the perfect platform for the data science community to discuss and build connections with one another, regardless of their location.
#2 Tableau
Most people consider Tableau a data visualization tool, but when used right, it can help you seamlessly collaborate with your remote team members. Its quick installation and onboarding make it a preferred tool among data science teams.
To empower better team-based collaboration on data science projects, Tableau offers a virtual dashboard that helps data science professionals from various locations access and work on projects. Plus, you can even use the tool to create collaboration kits by combining Slack, Google Drive, or Email with your Tableau account.
The shared space by Tableau can help create presentations and reports, and even allow members to comment on analyses, making it easier to collaborate. Moreover, Tableau even allows and makes it easier to share data with stakeholders inside and outside the team.
#3 Jupyter Notebooks
According to a survey conducted by Kaggle in 2022, Jupyter Notebooks is the most popular data science IDE, with over 80% of respondents admitting to using it.
Similar to Tableau, Jupyter Notebooks can help combine code such as Python, SQL, and R, the output of the code, and other rich text elements like formatting, figures, or links seamlessly within a single document. The standout feature of Jupyter Notebooks, however, is that it lets you include commentary with your code, eliminating the hassle and errors arising from creating separate reports.
More importantly, data scientists, engineers, and mathematicians working within the same organization at different locations can easily share code and other findings of a data science project within a single space.
#4 Databricks
Dealing with big data projects and volumes while collaborating with a large team that spans continents?
Well, you need a tool that can help you scale your data projects effortlessly and store data effortlessly across databases. Databricks lets individuals utilize database clusters and Python notebooks to query large datasets, allowing collaborations across large data science teams.
Founded by the people behind Apache Spark, Databricks helps extend the functionality of Apache Spark and offers a managed and collaborative environment that not only brings remote teams onto the same page but also helps them get access to clean data and advanced tools.
#5 Domino Data Lab
A cloud-based, collaborative platform designed specifically for data science and AI teams, Domino Data Lab offers several crucial features like version control, model deployment, and project management. The platform is designed to help data scientists and engineers streamline data science workflows and boost collaborations by viewing and analyzing any real-time changes or comments.
Domino Data Lab also doubles up as a unified, enterprise-grade AI platform that lets you access data, tools, and models across any environment, making it easier for organizations to handle data science workflows more seamlessly and efficiently. It works especially well for code-first teams that want to accelerate research, data analysis, and shareability within their teams.
#6 Google Colab
If you have been working with data or code for a while, you have probably heard of Colab notebooks. Hosted by Jupyter Notebook, which doesn’t require you to go through any setup process to get started, Google Colab is ideal for data scientists looking for free access to computing resources.
While the simplest way to describe Colab is that it acts like Google Docs for developers building AI workflows by helping them test logic and data connectors easily. The best part is that you can effortlessly link to other tools like GitHub or Jupyter to make your environment more informed and detailed.
Concluding Remarks
With processes and overall business performance increasingly relying on data and insights, companies deploy entire teams of data scientists. The combination of the tools mentioned in this list with a strategic human touch can boost collaboration in companies and help companies manage remote teams more easily.