Data Science Experience

An environment for data scientists to research, create, and collaborate.

About Data Science Experience

IBM Data Science Experience is a cloud-based, social workspace that helps data professionals learn, create, and collaborate across multiple open source tools. As a UX designer on this new product, I was primarily responsible for wireframing end-to-end user flows. The robust prototype I built in Axure was critical in getting design feedback. I also contributed to idea generation, storyboarding, and hi-fi mockups.

Team Role

UX Designer

Project Type

Web Application

Duration

8 Months

The Data Science Process is Fragmented

Data scientists face many obstacles in their workflow. They often use different tools depending on the task and find themselves jumping in and out of several environments. When working on a team, sharing project assets can be frustrating because previous work is either hard to find or created with unfamiliar tools.

—Research Insights—

Frustrations in the Data Science Workflow

Finding Relevant Resources

“You need to read academic papers to understand the data, but finding them is very difficult.”

Working in Multiple Environments

“I have to translate projects from SPSS to R or Python before I even start working with them.”

Collaborating Inefficiently

“You end up asking people where everything is, how it’s organized, and how to interface with it.

IBM Data Science Experience

A platform where data scientists can learn on the job, create analytics using the computing languages they prefer, and collaborate with each other.

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Discover the Community

Explore featured notebooks, articles, data sets, and tutorials from the data science community. Grab a sample notebook and start experimenting.

Discover the Community

Explore featured notebooks, articles, data sets, and tutorials from the data science community. Grab a sample notebook and start experimenting.

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Access Everything on One Platform

Quickly access the community, projects, notebooks, RStudio, and recently opened files without leaving the platform.

Access Everything on One Platform

Quickly access the community, projects, notebooks, RStudio, and recently opened files without leaving the platform.

Collaborate with Peers to Work Smarter

Projects allow data scientists to collaborate across their organization to transform data into actionable data. By taking advantage of shared data sets, notebooks, and tutorials, data scientists can leverage the work of their peers to accelerate their own progress.

Notebooks

Use notebooks to analyze data. Add multiple notebooks to a project.

Data Assets

Include data sets from the community or other sources.

Scheduled Jobs

Schedule future jobs or review the results for complete ones.

Bookmarks

Save direct links to favorite notebooks, tutorials, articles, and data sets.
projects_page

Collaborate with Peers to Work Smarter

Projects allow data scientists to collaborate across their organization to transform data into actionable data. By taking advantage of shared data sets, notebooks, and tutorials, data scientists can leverage the work of their peers to accelerate their own progress.

Add Data

Quickly add data to a project or notebook by dragging files into the data panel. Access databases by entering an API key in the connections section.

Add Data

Quickly add data to a project or notebook by dragging files into the data panel. Access databases by entering an API key in the connections section.

Work in Context

Add comments, view file activity and related data, and troubleshoot various problems while working within the context of a notebook.

Work in Context

Add comments, view file activity and related data, and troubleshoot various problems while working within the context of a notebook.

An Excited Audience Waits for What’s Next

Data Science Experience released its beta version at the Spark Summit on June 6, 2016. Below are some highlights of what our audience had to say about the Data Science Experience.

These are big changes for IBM since a year ago. I like the focus on developers, data scientists.

Nik Rouda

ESG

The data science experience demo was impressive. I could have spent a lot more time exploring…great work.

Donnie Berkholz

451 Research

You laid out an ambitious goal and I think you’re achieving it. It’s all about the community.

Dave Menninger

Ventana Research

—Research & Design Process—

What Exactly is Data Science?

My team immersed ourselves into the field conducting over 90 interviews and contextual inquiries with various types of data scientists. They ranged from aspiring students in school to seasoned professionals at large companies. I even led some of our internal user interviews.

Data Scientists are Storytellers

Their goal is to get deep into the data and draw hidden insights to help their business make better decisions. Data scientists must share their findings by telling a compelling story using data. Their process is highly iterative, and involves a heavy amount of research and data exploration. Data scientists often collaborate with business analysts to understand project goals and data engineers to get access to quality data.

Data Scientists are Storytellers

Their goal is to get deep into the data and draw hidden insights to help their business make better decisions. Data scientists must share their findings by telling a compelling story using data. Their process is highly iterative, and involves a heavy amount of research and data exploration. Data scientists often collaborate with business analysts to understand project goals and data engineers to get access to quality data.

Understanding the Data Science Process

Our team learned that there are several types of data scientists with a diverse combination of skillsets. However, their general workflow is very similar. We mapped out their processes and highlighted the disparities between them.

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Storyboarding Ideas for Direction and Alignment

The team brainstormed several ideas and narrowed them down to 3 concepts based on impact and feasibility. I helped write and create storyboards that addressed how Data Scientists could research, experiment, and collaborate. Our team presented them to data scientists to determine the direction of our product. The storyboards also helped align product management and development.

Wireframing the Experience

As UX Designer, I was responsible for wireframing several end-to-end flows for the product. Specifically, I led the user experience for the community and resources page. The storyboards I helped draft provided the vision for the wireframes.