Approaching data science like a team sport is still a relatively new concept in the field today. In the past, data scientists have typically worked alone as ‘unicorns’ and have been expected to wear multiple hats. However, more and more companies are building teams that contain a mix of roles from BI experts, data analysts, data scientists and data engineers, all the way up to Chief Data Officer.
Effective collaboration and communication are critical to the success of data science projects, not just within the data science team but across other teams too. This means building cross-functional teams that work together toward a common goal.
In a world that is also becoming increasingly remote, companies have been trying to find more effective ways of collaborating on projects across almost all business units. Deepnote is working to solve this problem for data scientists with their cloud-based notebook platform. Deepnote was built with data teams in mind, taking the headache out of many of the unique challenges that these teams usually face.
These are the three biggest challenges that small teams face when working collaboratively on data science projects:
- Working together in real-time — being able to see the code and output in real-time, where ideas can be shared and feedback can be given in the moment.
- Building reproducible data science projects that can be shared with new and future team members quickly and with little effort.
- Going from data science to data storytelling – giving presentations on the results of data science projects in the form of a story to leadership and executive teams in the company.
In this post, we’re going to expand on these issues and demonstrate how Deepnote has addressed them.
Working Together in Real-Time
When working in a remote environment, it’s easy to lose sight of the importance of collaboration. You can’t see what a team member is doing or what they’re working on unless you get on a Zoom call and share your screen or take a series of screenshots and send them over Slack or email. This adds friction and is definitely not the most efficient way to collaborate. It slows down your work, and projects end up taking longer or get scrapped altogether.
Being able to write code alongside teammates allows you to each get involved in the project, sharing ideas and feedback in the moment. You can bounce ideas off of each other and explore different ways of approaching a problem. By sharing knowledge, team members can learn faster and find better solutions to business problems.
This is particularly useful when you’re stuck on a challenging problem or get lost down a rabbit hole that takes you in the wrong direction. Being able to interact with one another and demonstrate, in real-time, what you’re doing is an element of collaboration that is missing from a lot of the current processes and tools that data teams use.
Deepnote reduces the friction, allowing teams to easily work together in real-time. You just share your notebook with team members and their avatar will show up on the screen. Clicking on their avatar allows you to quickly jump to their position in the notebook to avoid excessive scrolling (very useful for large notebooks).
Deepnote also gives you the ability to take your communications offline by leaving comments on specific code cells. This allows teams to have discussions and share knowledge and ideas even if real-time collaboration is not possible (e.g., when working in different time zones).
Building Reproducible Projects
Reproducibility is a key prerequisite for reliable science. Since the package versions, environment, and computational requirements are not bundled with Jupyter notebooks, reproducing a colleague’s analysis can be frustrating.
On the other hand, if you’ve ever tried onboarding a new team member, you’ll know how time consuming it can be to get their environments set up with the correct software and packages so that they can work on current projects and also be able to refer back to past ones.
This is why reproducibility matters — it will save you time in the long run and you can produce projects that are more accurate, more efficient, and with fewer bugs.
There are 3 main areas of a project that need to be reproducible: code, environment, and compute. With Deepnote, you can build projects that are reproducible in all 3 of these areas and you don’t even have to really think about it.
Every project on Deepnote comes with pre-installed packages that are commonly used in data science projects and everything just works. However, you do still get a lot of flexibility to set up your own custom environments if you need it.
Bonus tip: you’re not even restricted to just using Python if that’s not your thing — you can choose a different kernel to run on Deepnote, such as R or Julia. You can also directly insert SQL blocks for querying your data so that everything stays in one place.
Deepnote also offers a choice on the type of machine you want to use for your project. However, for the majority of data science projects, their Standard machines offer more than enough compute. The benefit here is that once you select a machine for a project, you never have to worry about it again. There is no maintenance or admin required and you can easily start or return to a project in seconds.
Lastly, there’s the code. Deepnote comes with automatic version histories and snapshots of files. This makes it easy to keep track of the changes that collaborators made to the project and roll back to previous versions.
A good philosophy to follow is to design notebooks that can be read, run and reused.
From Data Science to Data Storytelling
Data storytelling is one of the most overlooked skills in data science. While having the technical skills needed to properly analyze data is incredibly important, it doesn’t mean anything unless you’re also able to communicate the insights and results. Without this piece of the puzzle, no one will understand or much less, use your model.
Effective data storytelling allows the insights and results of data science projects to inspire people to take action. This moves the company toward its strategic goals and highlights the valuable contribution of data teams.
One way to communicate the results of a project is with a static presentation. You take a bunch of screenshots of your results, create a few charts and stick it all in a PowerPoint presentation and hope for the best. While this may be one of the most common ways to prepare presentations, it is not a good way to tell a compelling data story.
The most effective form of data storytelling is a combination of good visuals and interactivity. Luckily, Deepnote allows you to do both! Using their Publishing Editor, you can transform your analyses into either an article with a more readable layout or a dashboard with interactive blocks that can be moved around and resized. In other words, you can turn your notebooks into apps!
Improving our ability to collaborate within and across data teams is a crucial factor in the success of data science projects. While data teams do face some unique challenges, there are ways to overcome them.
With Deepnote, you can collaborate in real-time, create reproducible projects, and create interactive web apps from notebooks — all in the same platform.
Click here to start building your data science projects on Deepnote!
This is a sponsored blog post, but all opinions are my own.