FIU Data Science Bootcamp Application Deadline is Oct. 3 - Apply Now

How to Build a Data Science Portfolio: The 5 Phases

By Carlos Russo • April 22, 2021

Photo by Skye Studios on Unsplash

How to Build a Data Science Portfolio

Creating and implementing an effective data science strategy has become a must for business success and survival. The challenge lies in choosing the right projects to build an impactful project portfolio a business's data science team is capable and prepared to implement. 

Building an effective data science portfolio consists of four distinct and sequential phases: challenges and opportunities, pitch, scope, and plan.

Building a Data Science Portfolio, Phase 1:

Identifying Challenges & Opportunities

During Phase One, all relevant stakeholders generate numerous ideas to uncover new opportunities and challenges. It's essential to think about a potential data science portfolio in terms of the problem needing solving, not the solution. Present the challenge or opportunity the company is facing and allow the team to generate data science-based approaches. 

Building a Data Science Portfolio, Phase 2:

The Pitch

Once Phase One is complete, stakeholders move to the pitch phase of portfolio planning, which consists of three sub-phases: preliminary scoping, pitching, and winnowing.  

Preliminary Scoping 

The pitch creator(s) begin preliminarily analyzing the pitch’s impact, the impact hypothesis (how the data science will transform into business impact), and the risks involved with the process. 


All stakeholders present their pitches that have undergone preliminary scoping. Aptly titled the “elevator pitch party,” it is meant to be positive and exciting, where stakeholders feel free to share any pitches they have. 

Winnowing Pitches

After receiving all pitches, business managers and primary stakeholders decide which pitches have made the cut and will move on to the next phase in the data science portfolio creation process.  

Building a Data Science Portfolio, Phase 3:

The Scoping Process

Each pitch that's made the initial cut is analyzed technically, non-technically, or somewhere in between. 

Technical Data Science Scoping

Technical scoping reveals potential data-based solutions and performance estimations based on data science metrics (e.g. accuracy or precision). 

Nontechnical Data Science Scoping

Stakeholders formulate impact hypotheses for all relevant pitches, identify internal or external impacted parties, and assess the negative and positive impacts of both success and failure. 

Somewhere In-Between Scoping

Some pitches will require input from all parties technical and nontechnical alike to estimate the cost of failure and the benefits of success.

Document, Share, and Save

During the long scoping process, new opportunities or challenges may present themselves. If you've documented the entire process and all pitches involved, your team won't have to reinvent the wheel to redo scoping that is already complete.

Building a Data Science Portfolio, Phase 4:


Finish building your data science portfolio by plotting the cost/benefit of each project, building a path by connecting projects, then selecting a course that achieves your goals. 

Plot Projects

Plot projects using a traditional cost vs. benefit analysis — add a range, examine size, and place risk or unknownness at critical points. It’s also very important to add time and cost saving dependencies, where one project can springboard to another easily.

Build Paths

Once all projects have been visualized it's time to chart out potential paths by connecting several projects. One or many paths can represent a project portfolio, where the member paths have a cumulative benefit and possibly cumulative cost. 


The path you select will become your data science portfolio. Paths that consist of numerous intermediate projects are more flexible. Approaches that have fewer intermediate projects are more rigid.

Building a Data Science Portfolio, Phase 5:

Evolve Your Data Science Portfolio

The last step in the process is to evolve, which means staying aware of the changing environment and standing ready to pivot your portfolio as required. 

Similar Posts

business resource
VIDEO: An AI4 Panel Discussion on The State of AI in Banking

By Carlos Russo • September 23, 2020

Metis Sr. Data Scientist Javed Ahmed recently took part in a panel discussion about The State of AI in Banking during an online Ai4 event. He and the other panelists talked about upskilling, challenges related to COVID-19, and more. Watch the recorded panel discussion here.

business resource
VIDEO: Building a Successful Data-Driven Culture to Boost Business Value

By Carlos Russo • March 16, 2021

Metis President and Co-Founder Jason Moss recently moderated a panel discussion on Building a Successful Data-Driven Culture to Boost Business Value. Watch the recording here.

business resource
Corporate Training For Non-Technical Employees: Data Analysis Using Spreadsheets

By Carlos Russo • March 04, 2021

Learn about our new Data Analysis Using Spreadsheets Corporate Training course, designed to empower non-technical teams, no prerequisites required.