DataFramed [DataFramed Careers Series #2] What Makes a Great Data Science Portfolio
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May 31, 2022 Nick Singh, co-author of 'Acing the Data Science Interview' and a career coach with a background at Facebook and Google, shares insights on creating standout data science portfolios. He highlights how portfolio projects build experience and the types of projects that attract hiring managers. Nick discusses common pitfalls to avoid, the importance of visuals, and the MVP mindset for project selection. Plus, he provides concrete examples of impactful projects that can enhance job prospects in the competitive data landscape.
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Gain Experience through Projects
- Build portfolio projects to gain practical experience, especially when switching careers or lacking formal credentials.
- This self-made experience demonstrates your skills and makes you a less risky hire.
Experience in Data Science
- Data science roles often demand provable experience due to the field's interdisciplinary nature and high stakes.
- However, job requirements are frequently inflated, so don't be discouraged if you lack some listed experience.
Who Needs Portfolio Projects?
- Focus on portfolio projects if you're early in your career, switching industries, or upskilling in data science.
- Even seasoned professionals can benefit from projects to showcase modern skills like Spark or Python.
