How to Build a Compelling Data Science Portfolio & Resume

Talk by William Chen from Kaggle CareerCon 2018 including my summary notes.

Length - One page preferably. Simple design, avoid visual clutter. One column recommended. A good LaTeX template.

Objectives - Career objectives doesn’t add much, save for possible cover letter.

Coursework - Do add relevant coursework (college and other).

Skill Ratings - Doesn’t mean much, skip.

Skill Listing - Do list technical skills mentioned for the job. The order of the list says a lot, reorder based on job.

Projects - Don’t add common projects or homework. If adding for example Kaggle competitions, include percentile rank on final leaderboard (above top 10% would be considered good, extra info such as diff to top score and median can give richer understanding). Include links to writeups etc., and focus on real world problems.

Portfolio - Online presence on LinkedIn, Kaggle, GitHub, blog etc. is helpful. Ensure for example repositories have descriptions.

Experience - Experience is the core. Relevant experience and projects are essential. Tailor the CV to show relevant experience based on the job description.

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