What are the best ways to present your portfolio in a data science interview?

1 Choose relevant projects

Your portfolio should include projects that are relevant to the role and the company you are applying for. You want to show that you can solve real-world problems using data and that you have the skills and knowledge that match the job requirements. For example, if you are applying for a data analyst position, you might want to include projects that involve data cleaning, exploration, visualization, and reporting. If you are applying for a data engineer position, you might want to include projects that involve data pipelines, databases, and cloud platforms. If you are applying for a data scientist position, you might want to include projects that involve machine learning, statistics, and experimentation.

2 Prepare your materials

Before the interview, you should prepare your materials and make sure they are accessible and organized. You should have a resume that highlights your education, work experience, skills, and achievements in data science. You should also have a portfolio website or a GitHub repository that showcases your projects, code, and data. You should include a brief description of each project, the tools and methods you used, the results and insights you obtained, and the challenges and learnings you faced. You should also have a presentation deck or a notebook that summarizes your main projects and showcases your data storytelling skills. You should use clear and concise language, appealing and informative visuals, and relevant and convincing metrics.

3 Practice your presentation

Before the interview, you should practice your presentation and rehearse your answers to possible questions. You should aim for a 10-15 minute presentation that covers your background, your portfolio, and your goals. You should also anticipate questions that might arise from your projects, such as how you chose your data sources, how you validated your assumptions, how you evaluated your models, how you communicated your findings, and how you would improve your work. You should practice your presentation in front of a mirror, a friend, or a mentor, and ask for feedback on your content, delivery, and timing. You should also record yourself and watch your performance, and identify areas of improvement.

4 Adjust to the format

Depending on the interview format, you might need to adjust your presentation accordingly. For example, if the interview is online, you might need to share your screen or use a video conferencing tool to show your portfolio. You should test your equipment and connection beforehand, and make sure you have a backup plan in case of technical issues. You should also dress professionally, choose a quiet and well-lit environment, and look at the camera when speaking. If the interview is in person, you might need to bring your laptop or a flash drive to show your portfolio. You should also dress appropriately, arrive early, and maintain eye contact and body language when speaking.

5 Engage with the interviewer

During the interview, you should engage with the interviewer and show your enthusiasm and passion for data science. You should start with a brief introduction of yourself and your portfolio, and explain why you are interested in the role and the company. You should then present your projects in a logical and coherent order, and highlight your skills and achievements. You should also ask questions to the interviewer, such as what are the main data challenges they face, what are the tools and methods they use, and what are the expectations and goals for the role. You should also listen carefully to their feedback and questions, and answer them confidently and respectfully. You should also thank them for their time and attention, and follow up with a thank you note or email.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?