From open science movements to the rise of big data, collaboration has become a popular topic within the world of data science. Data science teams made up of professionals with varied backgrounds often form within companies and organizations to work together on complex projects. These teams include analysts and data scientists, but also developers, engineers, and other stakeholders who contribute to working on a product or project deliverable.
In response to this trend of collaborative data science teams, companies have developed data science tools for effective collaboration. Many of these collaborative data science tools are cloud-based, making it easier for teams to work together on the same project at the same time from various spaces and machines. Every data science professional should know how to incorporate collaboration and collaborative tools into working on projects.
The Benefits of Collaboration in Data Science
Since the development of computers and the investment in the open-source movement, industries rooted in data, science, and technology have relied on collaboration in order to make progress. It’s why many popular programming languages have shared grammar and syntax. The similarities between platforms and programming languages ensure that developers, data scientists, researchers, and other information technologists are able to communicate and work together, regardless of their background or location. From creating shareable resources through data science libraries, blogs, and forums to innovating on software and data science tools that make the work easier, there are numerous benefits to embracing collaboration.
A main benefit of working in collaboration with others on data science projects is the ability to complete complex, labor-intensive tasks in a shorter period of time. When analyzing big data, data scientists who work on a team are able to specialize in specific aspects of the project or product development.
For example, if a team is developing a mobile application, the team members could include developers, engineers, and analysts who can each bring their unique skill set to the creation of the product. Within more research-focused environments, having a team of data scientists also means that you can include individuals with knowledge of different data types, databases, and programming languages.
Top 5 Collaborative Data Science Tools and Platforms
Quite a few data science tools and platforms are geared towards collaboration. While some of these products are standalone, many are utilized in conjunction with other data science tools, creating a collaborative working environment to share data and project deliverables.
1. Google Cloud Platform
One of the most popular cloud providers on the market, Google Cloud includes products and software that can be used to collaborate on data science projects. Acting as a one-stop shop, the Google Cloud platform of products not only includes Google Sheets (which can be used to collect and organize data) but also database management systems like Google Cloud SQL. There are also collaborative data visualization tools like Google Data Studio and dozens of other products and services for team-based working environments.
2. Github
Collaborating on a data science project requires sharing files. Github is widely considered one of the best platforms ofr uploading and sharing of files. Github is known for its version control features, which can be accessed through the Git software. These ensure data files stay stable and retrievable even if many people are working on the same dataset.
3. Jupyter Notebooks
For data science students and professionals, Jupyter Notebook is known for its capabilities in collaborating on and editing code. Jupyter Notebook is commonly employed in classrooms when students have to work together on a data science project. Within companies and organizations, Jupyter Notebook is used to share code and other research findings in a manner that includes all aspects of a data science project in one space.
4. Tableau
Tableau is known as a data visualization tool, but this software also has features that make it a must for team-based collaboration on data science projects. Tableau Creator licenses allow data science professionals to work on projects in a shared virtual dashboard. This shared space is useful for creating presentations and reports, and also for commenting on analyses so that all the team members can view and respond to them. With this feature, it’s easy to collaborate within a data science team. Tableau also makes it convenient to share data with key stakeholders outside of the team.
5. Databricks
Databricks is usually employed for collaboration on big data projects. With Databricks, you can create data lakes to store large collections of data across databases. Like other big database management systems, Databricks allows individuals on a data science team to utilize database clusters and Python notebooks in order to query large datasets. Databricks is related to another well-known collaborative data science tool, Apache Spark, and has initiatives which focus on building up the Apache platform.
Want to use more Collaborative Data Science Tools?
Within the data science industry, it is becoming more common to work on a team of collaborators instead of being the only data scientist within a company. It is important for data science students and professionals to not only learn how to collaborate with others but also how to use collaborative data science tools. Many of Noble Desktop’s data science classes include instruction in many popular collaborative tools on the market. The Data Analytics Certificate gives students access to multiple tools which can be used in the development of data science projects, such as Tableau.
Python is known for its collaborative and open-source data science tools and packages. Many of Noble Desktop’s Python class and bootcamps focus on teaching students how to write code using collaborative data science tools like Jupyter Notebook. The FinTech Bootcamp includes instruction on analyzing financial data and developing financial technology, along with training in Jupyter Notebook and some of the most well-known Python data science libraries. The Python Developer Certificate offers instruction in collaborative tools useful to both data scientists and developers, such as editing code with GitHub and Visual Studio Code.