Your Portfolio
Proof beats promises.
Hiring managers see hundreds of applications, and all of them list Python, SQL, and machine learning. Everybody claims the skills. What stands out is proof. A small, sharp project beats a list of certificates every time.
- Hundreds of applications
- Everyone claims the skills
- Proof stands out
Describing a project
Describe each project like this. The question, the data, the methods, and above all, the result, stated as a decision or a dollar value. Link to the code. A reader should be able to get the point in twenty seconds. Most people describe what they did. You should describe what changed.
Project: Should Harbor Coffee close a cafe?
Question: Which cafes lose money after rent?
Data: 70,000 sales rows, 12 stores
Methods: joins, bootstrap, trend comparison
Result: Recommended shrinking two cafes,
worth about $84,000 more over three years
Code: github.com/you/harbor-analysisWhat makes a project stand out
Choose a question that you actually care about. Use messy, real data, rather than the same famous datasets that everyone else uses. State a clear conclusion. Include a chart that makes the point. Write a short post about it. And show your mistakes too, as admitting what didn't work shows real maturity.
Summary
Show what changed, and not just what you did. You've now got a complete data science toolkit. Let's use it. Next, a series of complete case studies.
Key takeaways- Lead with the result
- Use real data
- Proof beats certificates