Hiring Manager Explains: Data Portfolio Do’s and Don’ts

Summary

Christine, a seasoned data analyst turned data director and hiring manager, advises early-career data analysts on building a portfolio that showcases business-relevant insights and technical skills. She emphasizes the distinction between learning projects and those meant for showcasing, urging job seekers to focus on projects that apply technical skills to real business questions. Christine provides a roadmap for creating impactful portfolio projects, including using GitHub for showcasing work and tailoring resume bullet points to reflect the project's value. She also offers insights on how to effectively discuss these projects in interviews.

Takeaways

• 😀 Building a portfolio is crucial for early career data analysts, especially for those transitioning from other industries.
• 🔍 Projects in the portfolio should demonstrate business-relevant insights and the application of technical skills to stand out.
• 📚 There's a distinction between projects for learning and those for showcasing; the latter is essential for job applications.
• 🚫 Avoid generic projects like Google Analytics case studies, as they are too common to make an impact in today's job market.
• 🛠 Focus on projects that show the application of technical skills to solve real business questions, highlighting business metrics and insights.
• 📈 Use GitHub for portfolio projects to demonstrate familiarity with the data ecosystem commonly used by data analysts.
• 📝 Include a README file in each GitHub repository that outlines insights, recommendations, and the context of the project.
• 📊 Create dashboards using industry best practices for simplicity, clarity, and cleanliness to showcase your data storytelling skills.
• 💼 Tailor the portfolio to reflect the type of business model you're interested in, making it widely applicable to your target industries.
• 📋 Ensure the resume includes impactful bullet points that highlight the tools used, the metrics analyzed, and the insights provided.
• 🗣 Prepare for interviews by being able to discuss the data cleaning process, challenges faced, and how insights were discovered and communicated.

Q & A

What is the main purpose of building a portfolio for early career data analysts?

The main purpose of building a portfolio for early career data analysts is to demonstrate their ability to apply technical skills to business-relevant questions and to stand out from the competition, especially when transitioning from another industry.

Why are some online resources not sufficient for building a strong portfolio?

Some online resources are not sufficient because they often do not emphasize the importance of creating projects with business-relevant insights and do not show how to apply technical skills to real-world business scenarios.

What is the difference between portfolio projects for learning and for showing?

Portfolio projects for learning are those done to get comfortable with tools like Excel, SQL, and Tableau, and are primarily for personal development. Projects for showing, on the other hand, are meant to demonstrate the application of technical skills to business questions and should include business insights and recommendations.

Why is it not recommended to use Google's data analytics certificate projects as part of your portfolio for showing?

It is not recommended because such projects are often generic and commonplace, which do not help in standing out in the job market. They are more suitable for the learning phase rather than showcasing advanced skills and business insights.

What are the critical dos and don'ts of portfolio projects that Christine mentions?

The dos include focusing on projects that show the application of technical skills to relevant business questions, demonstrating understanding of business metrics, and providing insights and company recommendations. The don'ts include creating generic project names, focusing solely on the technical process without insights, and not using GitHub to showcase projects.

Why should a portfolio project not just focus on the technical tools used but also on the insights and recommendations derived from them?

Focusing only on the technical tools used does not demonstrate the candidate's ability to apply these tools to solve business problems. Insights and recommendations show a deeper understanding of the business context and the ability to communicate findings effectively.

What is the significance of using GitHub for portfolio projects?

Using GitHub is significant because it is a common tool in the data ecosystem used by data analysts to store and share projects. It shows familiarity with industry-standard tools and provides an accessible platform for hiring managers to review work.

How should the structure and design of a Tableau dashboard in a portfolio project reflect industry best practices?

The structure and design of a Tableau dashboard should be simple, clear, and clean, following industry best practices. It should include tables for reporting, line graphs for trend analysis, and mix graphs for distribution analysis, making it easy for non-technical audiences to understand.

What is an example of a company-relevant portfolio project that Christine provides?

An example of a company-relevant portfolio project is the analysis of subscription data from Zoom, focusing on overall trends and recommendations for the marketing and sales team. It uses a widely applicable business model and provides insights and recommendations based on the analysis.

How should insights and recommendations be presented in a portfolio project to effectively communicate business value?

Insights and recommendations should be presented in a way that is directly related to the business metrics and dimensions, focusing on the 'so what' of the findings. They should guide people on where to spend time investigating further, rather than providing conclusive recommendations.

What is an example of an impactful bullet point for a resume that reflects a portfolio project?

An example of an impactful bullet point is one that not only mentions the tools used but also highlights the business metrics, insights, and recommendations, such as 'Conducted analysis in SQL to surface insights on sales trends and SaaS metrics for a self-created Zoom data set containing 100K subscription records.'

How should a portfolio project be structured on GitHub to make it accessible and representative of real-world data analyst work?

A portfolio project on GitHub should have one to three public repositories, with one repository per project. Each project should include a README file that walks through the insights and recommendations, and provides context as if the candidate is an actual data analyst at that company.

What kind of interview questions should a candidate be prepared to answer when discussing their portfolio projects?

Candidates should be prepared to answer questions about the data cleaning process, challenges encountered, interesting insights discovered, how to make insights understandable to non-technical audiences, and questions about the structure and design of a Tableau dashboard they built.