In today's highly-competitive job market, it’s not uncommon for job applicants to insert themselves into a large group of applicants – sometimes hundreds of others. People seeking a data scientist role are experiencing this even though several data scientist jobs are available in the marketplace today.

As data scientist Matt Chapman put it:

It’s hard to get a Data Scientist job.

While pursuing a job is hard, Matt created a robust data science portfolio that was “easy” to create, and it helped him land a data scientist job.

A Data Science Portfolio is Your Opportunity to Stand Out

Matt made sure to feature things that helped him stand out and differentiate himself from other candidates. That includes avoiding generic projects several other candidates will have on their portfolios and resumes.

He points out that if your portfolio features a lot of the same type of things everyone else has in their portfolio, you can’t stand out from the crowd.

Jeremie Harris, co-founder of Gladstone AI, agrees with this sentiment and says companies will not be interested in data scientist job candidates that feature “trivial proof-of-concept datasets” as part of their list of personal projects.

He shared these as examples:

• Survival classification on the Titanic dataset.

• Hand-written digit classification on the MNIST dataset.

• Flower species classification using the iris dataset.

Mark says that means focusing on things you’re genuinely passionate about in data science. In his case, he loves Natural Language Processing and social sciences, so he featured projects related to that passion.

Ultimately, you have to show why it’s adding value.

On the other side of this, AI and Machine Learning EngineerGuglielmo Cerri shared some examples of data science projects that will boost your career, and they are:

• Sentiment Analysis on Social Media
• Image Recognition with Deep Learning
• Predicting House Prices
• Web Traffic Analysis
• Sales Forecasting

While this is a small sample of ideas, Guglielmo also encourages data scientists to choose projects that align with their interests.

Here are some other ideas for a powerful data science portfolio.

Showcase Your Practical Skills

While resumes and CVs provide a snapshot of your academic and professional background, it’s harder for them to really prove practical skills.
An online portfolio allows you to showcase real-world projects, illustrating your ability to apply theoretical knowledge to solve complex problems.

Prove Your Industry Expertise:

Tailoring your portfolio to a specific domain (e.g., healthcare, finance, e-commerce) can demonstrate your understanding of industry-specific challenges and your ability to provide data-driven solutions.

Highlighting Collaboration and Communication Skills:

Many data science projects involve working in cross-functional teams and communicating results to non-technical stakeholders. An online portfolio can include detailed project descriptions emphasizing your teamwork and communication abilities.

Tableau sees communication as a skill that definitely stands out in a field full of data scientists applying for jobs. They say:

They say to thrive in business, data scientists need to be proficient at analyzing data, and be able to clearly communicate explain findings to everyone – including the non-technical staff.

With everyone on the same page and understanding each situation, it not only provides unified literacy, but increases the value of the data scientist’s role.
It will help to communicate your philosophy and processes in your portfolio. For example:

  1. Use Clear Problem Statements: For each project, provide a concise problem statement that explains the context and challenges. This helps employers understand the significance of your work.
  2. Use Detailed Methodology: Describe the steps you took to tackle the problem, including data collection, data cleaning, feature engineering, model selection, and evaluation metrics.
  3. Use Code Samples: Include snippets of your code to showcase your programming skills. Make sure the code is well-structured, commented, and readable.
  4. Use Visualizations: Visualize your results using clear and insightful graphs, charts, and dashboards. Visualizations help convey complex information in a more understandable manner.
  5. Use Interpretation and Insights: Alongside the technical details, explain the implications of your findings and how they could drive actionable insights for businesses.
  6. Use Documentation: Provide clear documentation for your projects, including explanations of the dataset, libraries used, and any challenges you encountered.

It’s equally important to put your style to these examples and communications.

Continue to Learn and Update Your Data Science Portfolio

Remember, in the world of data science, actions truly speak louder than words. Along with demonstrating your passions, projects and style, it’s a huge advantage to show an employer you’re always learning.

By participating in open-source projects, networking events, freelance jobs, coding bootcamps and more, you can stay on top of trends and expand your versatility as a data scientist.

Here are some data scientist portfolios to help inspire yours:

David Venturi

Donne Martin

Julia Nikulski

Arjun Bhaybhang

Hanna Yan Han

If you’d like to start building or advancing your data science skills, take a look at our data science bootcamp, and let us know if you have any questions.

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