How to Build a Data Analyst Portfolio That Showcases Your Skills?

How to Make a Data Analyst Portfolio?

Breaking down the steps of creating a data analyst portfolio can seem difficult. The following steps will help to understand everything needed to create a data analyst portfolio which will be ready to show employers.

Step 1: Choose Portfolio Platform

• Choose a place to put your work. This could be GitHub, a free website or Tableau Public.
• Choose something that is easy to update. Also easy to navigate.
• Keep it simple and clean.

Step 2: List Key Skills

• List a few key skills you have. E.g. Excel, SQL, Python Programming, Power BI Tool.
• Look at multiple job postings and take note of what skills they ask for.
• Only choose a few skills and show off these skills.

Related Article: Data Analyst Syllabus: Skills and Tools to Learn

Step 3: Choose Relevant Projects

• Choose projects that match the type of job you want.
• Try to include a few different types of project. For example data cleaning, dashboard creation and insight identification.
• Have around 3-5 good quality projects.

Step 4: Use Real-Life Data

• Use public data.
• This can be found on sites like Kaggle, data.gov and other city/state data sources.
• Use messy data as there isn’t much perfect data in real life.
• Choose something you are interested in. This could be sales, health, sports etc.

Step 5: Write Up Analysis Process

• Write out the problem question, methods and tools.
• Explain your reasoning in simple terms.
• Include code with comments so people can understand easily.

Step 6: Include Visualizations and Conclusion

• Include simple visualizations to help explain your results.
• Write a short conclusion explaining your findings and why it was important.
• State your findings. e.g. Trends, problems and useful tips.

Step 7: Finalize and Publish Portfolio

• Order your projects. Starting with your best and most important.
• Include an about me, resume and links to your social media.
• Check through and publish online. Share the link on LinkedIn and when applying for jobs.

What are the Best Data Analyst Portfolio Projects to Include?

The best data analytics projects will showcase learned skills and abilities by solving real world problems. A mix of both easy and challenging projects will help to show employers how capable you are.

Some good project ideas include:

• Sales Analysis: Analyze the sales data to find out which are your best selling products, months and slow periods.
• Customer Churn Analysis: Understand why customers are leaving and what can be done to keep them.
• Data Cleaning Project: Pick a messy dataset and clean it up by correcting any issues and missing data.
• Dashboard (Tableau/Power BI): Create an easy to understand dashboard to show your key metrics for e.g. sales, profits, website views.
• SQL Database Project: Write queries to answer business questions using a sample database.
• Marketing Campaign Analysis: Find out which adverts and channels were the most successful.

Which are the Best Platforms to Showcase Data Analyst Portfolio?

Choosing the best platform to showcase work in very important for data analysts. Choosing the correct platform will make it easy to access projects. 

Here are some of the best options to consider:

Top Tips to Make an Excellent Data Analyst Portfolio

Portfolio should contain the best projects and be very clean. Here are some tips to making a best data analyst portfolio.

Tips to make a standout portfolio:

• Quality over quantity: Show off a few good projects (3-5)
• Be Descriptive: Describe what you were trying to achieve, how you went about it, what tools were used, what you learnt.
• Use a Variety of Tools: Show your skills using SQL, Excel, Python, Power BI, Tableau etc.
• Use Statistics: Use numbers to show your impact.
• Simplicity is Key: Use clean and concise dashboards/visuals.
• Keep it up to Date: Keep adding new work and improving your work.

Common Mistakes to Avoid When Building a Data Analyst Portfolio

Even skilled analysts can make small mistakes that hurt their portfolio. The good news is that most of these are easy to fix. 

Conclusion

People don't have to be a perfect data analyst to have a great portfolio. They just want a few well done projects with clear descriptions and practical results. Keep it simple, use real data, provide code and explain each chart. Keep it up to date and don't make the same mistakes. Steady work will lead to great opportunities for work. 

Do you need help to create a portfolio? Schedule a free consultation with our experts today!