If you’re aiming for a job in analytics, your portfolio can set you apart from other candidates. A great portfolio demonstrates your technical skills, problem-solving abilities, and understanding of business contexts (if you’ve chosen a business-related problem, I recommend you consider this approach). More importantly, it showcases how you can add value to an organization and demonstrate that you’re a self-starter.

My data projects have come up time and again in interviews and networking. They’ve also allowed me to explore technologies and skills I didn’t have the opportunity to work with on the job. I set up a computer vision system for one project to detect the school bus passing my house daily and leverage AWS to send text alerts. Am I an AWS expert? No, but I now understand what is happening better and can speak to my work.

In this guide, I’ll cover the essentials of building a standout analytics portfolio project that wows hiring managers and gives you the confidence to rock your next interview.

1. Start with a Real-World Problem

In an ideal scenario, your portfolio project should mimic the work you’ll do in your target job. This starts with selecting a real-world problem that resonates with your audience. Luckily, real-world problem data has become much easier to find. 

How to Choose a Problem:

• Industry Relevance: Focus on industries or domains related to the role (e.g., marketing analytics, supply chain optimization, or healthcare data).

• Company-Specific: Tailor your project to the challenges faced by companies you’re applying to (e.g., improving customer retention or informing how to optimize operations).

• Passion Projects: Use data to explore something you’re genuinely interested in—your enthusiasm will show in your work.

Then, hunt to find an available dataset that matches these criteria. Often, making a final choice on a dataset can be the most challenging part. Make sure to time-box your search for an appropriate dataset so you don’t get derailed. I’ve added some options here to kick off your search. If you haven’t nailed down a dataset after a couple of days, come back here and choose an option that is most related to the industries or companies you’re targeting.

Some sites with publicly available data include:

• Kaggle Datasets: Offers datasets across diverse domains.

• Google Dataset Search: A search engine for publicly available datasets.

• Maven Analytics: offers datasets for various industries and use cases.

• Data.gov: Government datasets on healthcare, education, and climate.

Some specific datasets you might want to check out:

• Online retail dataset: “This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.”

• E-Commerce Sales Dataset: “This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on various sales channels, including Shiprocket and INCREFF, and financial information on related expenses and profits.”

• Telco Customer Churn: "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs. [IBM Sample Data Sets]”

• HR Employee Attrition Dataset: “Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists.”

I love the collaborative nature of using Kaggle datasets.  Although you might be using Excel, Tableau, or PowerBI, you can scroll through the Python and R notebooks to understand how others have approached the problem and identify some interesting insights you might’ve missed.

2. Prioritize Aesthetic and Clarity

The design and presentation of your portfolio project can make or break its impact. Even if your analysis is groundbreaking, poor visuals or a cluttered presentation can distract from your message.

Tips for Presentation:

• Clean Design: Use simple, readable fonts and a clean layout. You might even consider using the brand colors for the company you’re applying to for your dashboard. The key takeaways and plots with the most relevant insights should go near the top.

• Data Visualization: Leverage tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) to create compelling visuals. Think about how you’ll use color to highlight the main takeaways in your chart and avoid trying to communicate too much information with a single chart. The takeaway should be clear almost immediately. Try to make sure that you don’t have to explain the visual for another person to understand what it represents, and use colors that are easily discernable by someone who is color blind.

3. Highlight Business Impact

Analytics dashboards are tools to drive decisions, not just present data. Make it clear how your dashboard contributes to solving business challenges or achieving goals. Add a concise summary at the top of your dashboard to highlight your analysis's main takeaway or impact. This also doubles as a note for yourself when remembering the most important points of your work.

Summary Examples for Dashboards:

• Sales Analysis: "This dashboard identifies the top-performing regions and products, helping prioritize marketing efforts for a projected 15% increase in revenue."

• Cost Analysis: "Insights reveal overspending trends in vendor contracts, presenting opportunities to reduce costs by $20,000 annually."

• Operational Efficiency: "By tracking delivery delays, this dashboard identifies bottlenecks and suggests improvements that could reduce delays by 25%."

Focusing on actionable insights and their tangible benefits keeps your dashboard relevant, impactful, and aligned with business objectives. You’ll notice words like “could” and “projected” here. These are estimates that could be part of your recommendations; you just want to be able to explain your reasoning and how you came up with them.

4. Document and Share Your Work

To ensure your analytics dashboards reach the right audience (and you can access them in an interview), you’ll want to host your dashboard on one of these sites. These specific locations are perfect for showcasing your work:

• Tableau Public: Perfect for hosting interactive dashboards. Make your work visually appealing and easily accessible to showcase your data storytelling skills.

• Power BI Community Gallery: If you're using Power BI, the community gallery is a great place to share dashboards and gain visibility among professionals.

• Maven Analytics Showcase: A platform designed explicitly for showcasing dashboards and projects. Great for building a professional portfolio.

Consider writing a blog article or post about your dashboard for further reach. I’ve had a lot of luck getting eyes on my work using these platforms.

• LinkedIn Posts: Present your dashboard as a case study or story. Walk your audience through your process, findings, and business impact to build engagement.

• Reddit (r/DataViz or r/DataScience): Share your dashboards for feedback and visibility in communities focused on analytics and visualization.

• Medium or Substack: Write a detailed article explaining your project and embed your dashboard for an in-depth walkthrough.

These platforms allow you to share your dashboards, receive feedback, build credibility, and engage with the analytics community.

5. Practice Speaking About Your Portfolio

A big part of your project’s success lies in how you communicate it during an interview. Be prepared to explain your project clearly, confidently, and concisely.

Key Questions to Prepare For:

• What problem were you solving, and why would this be important to the business?

• How did you approach the data-cleaning process?

• Why did you choose this specific methodology or tool?

• What would you do differently if you had more time or resources?

Tip:

Use the STAR method (Situation, Task, Action, Result) to frame your answers.

Be prepared to speak about the complete analytics workflow. A stellar portfolio project doesn’t just include results; it highlights your approach and methodology. This shows hiring managers your understanding of the analytics lifecycle.

Key Steps to Showcase:

1. Data Collection, Cleaning, Creating New Variables:

   • Explain how you sourced and cleaned your data, highlighting the challenges you overcame.

2. Analysis:

   • Use visuals and descriptive statistics to uncover insights and tell a story.

3. Business Insights & Recommendations:

   • Translate your findings into actionable insights, showcasing how they address the business problem.

In an interview, you’ll want to be concise.  This may mean that you’re not talking about data cleaning and methodology. However, you’ll also want to be prepared if the interviewer wants to go deeper into the project. Going deeper allows you to show the interviewer that you understand the analytics pipeline, but you’ll want to be ready with a polished response.

6. Get Feedback and Iterate

Before using your project in an interview, share it with peers, mentors, or online communities (e.g., LinkedIn or relevant subreddits like r/datascience). Constructive feedback can help refine your work and ensure it’s polished. Sharing your portfolio on Maven Analytics' portfolio Showcase also allows you to receive comments. Getting feedback from others helps you to improve your work before presenting it to a hiring manager.

Final Thoughts

Creating a fantastic analytics portfolio project takes time, effort, and attention to detail, but the payoff is worth it. By tackling a meaningful problem, showcasing your skills, and focusing on business impact, you can make your portfolio stand out and leave a lasting impression in your job interviews. 

Ready to start building your portfolio? Take inspiration from this guide, choose a dataset, and begin crafting your next data project!