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3 Data Science Projects That Got Me 12 Interviews. And 1 That Got Me in Trouble.
3 work samples that got my foot in the door, and 1 that almost got me tossed out.
Tired of the Titanic dataset and want ideas to inspire a unique, marketable portfolio? Learn how with my free project guide.
Analyzing My Data Science Portfolio Projects
Like many graduates, I did not have a job lined up after earning my data science master’s degree; the reason is because I didn’t apply anywhere.
For three months.
I graduated in spring time but didn’t begin my job search, in earnest, until summer.
Either fear or foresight compelled me to believe I wasn’t ready, and that emotion drove me to invest the hours I wasn’t working an hourly job at a local hotel into crafting a marketable, desirable data science portfolio.
When I receive comments and LinkedIn messages with questions related to breaking into the data industry, my first tip is always to create a GitHub-hosted portfolio that you can use as a professional calling card.
Bonus points if you create engaging documentation or share that content on a platform like Medium or LinkedIn to connect with potential hiring managers.
While I’ve written about the importance of having a side project, even as a working professional, and shared my communication tips for presenting data projects, I realized that I’ve never shared the contents of my own portfolio.
My hope is that, for those of you wondering where to start when it comes to displaying your work, you’ll see real examples of what kinds of projects catch a recruiter or interviewer’s eye and why.
Before I share my work and process, I have to acknowledge that while I had no formal tech background, I did have the following traits recruiters and managers found desirable (based on feedback, not my egoism, I swear):
- A master’s degree in data science
- Domain knowledge (I purposely applied for roles within the media, education and hospitality industries, all of which I’ve worked in before)
