As a data analyst, your resume needs to stand out from the crowd. One way to do this is by showcasing impressive projects that demonstrate your skills. But what makes a project truly impressive?
In this post, I’ll share five data analyst projects that can help take your resume to the next level. These projects are practical, relevant, and easy to explain to non-technical stakeholders.
1. Analyzing Customer Purchase Behavior
Choose a publicly available dataset (like the Walmart dataset on Kaggle) and analyze customer purchase behavior. This project shows your ability to work with large datasets, identify trends, and provide actionable insights.
2. Visualizing COVID-19 Data
Use datasets from reputable sources (like the CDC or WHO) to visualize COVID-19 cases, deaths, and vaccination rates over time. This project demonstrates your ability to work with real-world data and create informative visualizations.
3. Predicting Stock Prices
Select a publicly traded company and use historical data to predict its stock prices. This project showcases your understanding of machine learning models and ability to apply them to real-world problems.
4. Analyzing Employee Turnover
Use a dataset from a company (like IBM or Google) to analyze employee turnover rates. Identify factors contributing to turnover and provide recommendations for improvement. This project highlights your analytical skills and ability to provide actionable insights.
5. Creating a Dashboard for a Non-Profit
Choose a non-profit organization and create a dashboard to track its key performance indicators (KPIs). This project demonstrates your ability to communicate complex data insights to non-technical stakeholders.
Remember, the key to a great project is to:
- Choose a topic you’re passionate about
- Use publicly available datasets or get permission to use company data
- Keep your project concise and easy to explain
- Highlight your skills and achievements clearly
By including one or more of these projects on your resume, you’ll be able to showcase your skills and stand out from other data analyst applicants.