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Building Your Data Science Portfolio

17 May

A Brief How-To Guide

For those seeking professional roles that require concrete skills and a dynamic understanding of how to leverage and apply them, a strong portfolio is an absolute must. It can mean the difference between receiving an invitation to interview versus having your application left unnoticed. But how do you create a data science portfolio?

Someone like a graphic designer can easily showcase their work in a straightforward, visually driven portfolio. However, because the work done in data science can be complex and abstract, involving unique solutions derived from the analysis of large data sets, putting together a portfolio of evidence to showcase one’s ability and approach can seem like a difficult task.

As in many professions, there’s not one right way to go about creating a data science portfolio. But there are things you can do to help ensure your portfolio is rock solid and a worthy showcase of your skills.

1. Be Visible

It should go without saying that for a portfolio to be effective, it must first make a visual impact. Data scientists have several options for presenting their work, but regardless of platform your portfolio needs to clearly and quickly demonstrate your competence and strategic vision.

Setting up a website as a data science portfolio is one option. This gives you complete control over how you present projects and your role in their success. In this format, it’s vital to express the full scope of your work and the thought process behind it, which may require detailing your work process step by step and clearly defining its purpose to your audience. Another option is to create a profile on an established website that has been designed to showcase coding projects and their designers’ abilities.

2. Articulate Your Ability

Data scientists are expected to have strong command of very specific skill sets. While your portfolio should absolutely highlight your soft skill capabilities, such as organization, creativity and communication—proving that you are able to digest, synthesize and effectively visualize your data findings—it is by far its main goal to demonstrate your mastery of hard skills like programming and math modeling.

By showcasing a balance between the technical skills and business acumen required of an effective data scientist, you will leave no lingering questions for the hiring manager. They’ll know that you have the diverse qualifications to handle the job.

3. Be Visual

You have heard many times that a picture is worth a thousand words, and this is certainly so when presenting data science analyses and solutions. With this in mind, it is critical that your data science portfolio highlight your ability to visualize data analysis and insight. In addition to demonstrating your coding ability and proficiency with mathematical formulas, be sure to visualize your analysis with graphs so that you can easily explain the results of your work. Data are complex, and graphing skills are essential in understanding the input data, as well as the conclusions, of an analysis.

4. Showcase Your Process

Once you’ve demonstrated your technical knowledge and ability, it’s time to show off your approach to assignments and problems. After all, this is essentially what data science is all about—developing unique business solutions by implementing custom systems and analyzing data. Simply put, the ability to identify a need and then address that need through machine learning and creativity is essentially what sets data scientists apart from one another. While many data scientists have similar skill sets, their ability to apply their skills and use them to benefit the organization and its customers is where the true differentiators are found.

How to best show your process is up to you. However, it’s usually a good idea to walk the viewer of your portfolio through each step of a project, articulating your thought process along the way. This provides a powerful demonstration of what a set of data offered before the project versus what its new value became after you applied your expertise. And as you work through your own process, consider that the main steps of an analysis are: make a problem statement, acquire data, explore the data to gain insight, determine an appropriate methodology to address the problem, build a math model or perform an analysis, draw conclusions and deploy the results.

5. Stand Out From the Crowd

Anything that can be used to differentiate yourself in the professional world can be helpful to landing the right job for you. For data scientists, a portfolio provides the opportunity to showcase strategic thinking, technical abilities and perspective in a dynamic fashion. In many ways it’s an introduction to you and your work, and if done well, with thought and care, it can prove to be extremely useful when applying for jobs.

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