Building a Data Analyst Portfolio

Jul 11, 2024

Building a Data Analyst Portfolio

Importance of a Portfolio

  • Critical for Job Search: A portfolio is crucial for becoming a data analyst and is often the first thing a recruiter will look at.
  • Representation: It represents your skills and can make or break your chances of getting a job.

Getting Started

  • Quick and Easy Setup: Creating a portfolio can take about 15 minutes and can be done for free.
  • Content: It's only as good as the projects you include.

Types of Portfolios

GitHub

  • Popular for Code Repositories: Useful for uploading code and projects.

Kaggle

  • Data Science Community: Allows you to upload projects and code, and share links.
  • Good Starting Point: Ideal for beginners who don't yet need a full portfolio website.

Creating a Personal Portfolio Website

Using Wix

  • Free and Simple: No coding required; sign up for a free Wix account.
  • Template Selection: Choose a template, such as the 'Creative CV' template, which is user-friendly for beginners.

Steps to Build Your Portfolio on Wix

  1. Create an Account: Sign up for free on Wix.
  2. Selecting a Template: Browse and select from various free templates.
  3. Customize: Use Wix's user-friendly tools to add personal information, social media links, and a summary.
  4. Adding Projects: Focus on summarizing your projects in a few lines and add relevant images.
  5. Link Projects: Add links to full projects on Kaggle or GitHub.
  6. Optional: Upload Resume: Decide if you want to include a resume page.
  7. Domain Name: You can use Wix’s free domain or buy a custom domain from Namecheap.

Example Projects to Include

Project 1: Movie Ratings

  • Dataset: Use the IMDB movies dataset from Kaggle.
  • Steps:
    1. Clean and structure the data using SQL.
    2. Explore the data and generate insights.
    3. Create visualizations using tools like Tableau or Power BI.
  • Questions to Answer:
    • Which actor prefers a specific genre?
    • What genre brings in the most revenue?
  • Documentation: Document your process and findings to show your understanding.

Project 2: Online Retail Sales

  • Dataset: Use an online retail sales dataset from Kaggle.
  • Steps:
    1. Analyze sales data using SQL.
    2. Answer key questions like product revenue, customer purchase patterns, and regional sales.
    3. Visualize the data on a map.
  • Questions to Answer:
    • Which products generate the most revenue?
    • Are there customers making repeat purchases?
    • Are products often bought together?

Final Tips

  • Focus on Quality over Quantity: 2-4 high-quality projects are ideal.
  • LinkedIn: Upload your projects to LinkedIn for additional visibility.

Conclusion

  • Importance of Good Projects: Ensure you have at least two quality projects before building your portfolio.
  • Additional Resources: Further information and tips available in other tutorial videos.