Data Science Portfolio Project Ideas That Can Get You Hired (Or Not)


What are some data science portfolio project ideas that can get you the job?

When employers hire a data scientist, they often look for someone who has the skills to generate revenue and opportunities for their business. Knowledge of programming, machine learning, statistics, etc. is not enough to get a data science job. You also need a portfolio to showcase your data science skills. A well-rounded data science portfolio can show off all of your skills that make you eligible for that position. A well-explained data science portfolio should show off your ability to communicate, collaborate, reason about data, take the initiative, and technical skills.

 

The Importance of a Data Science Portfolio

 

  • A data science portfolio is more valuable than a resume, as you can use a portfolio to keep a record of your projects, codes, and datasets.
  • Through a portfolio, you can showcase your data science skills.
  • A portfolio enables you to network with other professionals.
  • It also increases your chances of getting a data science job.

The most important part of building a data science portfolio is to figure out what to add to your portfolio. In your data science portfolio, you need to have some projects on GitHub or your website or blog. Each project should be well structured, so a hiring manager can quickly evaluate your skills. In this blog, we will walk through a few data science project ideas that should be in your portfolio.

 

Data Science Portfolio Project Ideas

 

Before adding projects to your portfolio, you need to understand what data science projects you should add and what you have to avoid. This is what we’ll cover now in data science portfolio project ideas.

You should add those projects that align with your role. For example, if you’re going to apply for an analyst position, building projects that use data cleaning and storytelling could be useful for you.

Your data science should consist of 3-5 projects that demonstrate your ability to:

  1. communicate
  2. collaborate with stakeholders and team members
  3. have technical competence
  4. reason about data
  5. take initiative
  6. have domain expertise

Data Cleaning Projects

You should add projects that will demonstrate your skills in data cleaning. Find a messy data set and then clean the data and perform basic analysis. Try to find and work on some unstructured data to your skills. You can also collect your own data via APIs or web scraping.

Data Visualization and Storytelling Projects

Include projects in your data science portfolio that will show your skills of:

  1. Telling stories
  2. Offering real insight
  3. Convincing to take action

Here you have to demonstrate and explain what your code is doing, so data visualization and good communication skills are extremely useful.

Building End-to-End projects

Building end-to-end projects is the best way to show your hiring manager that you have the skills to extract insights and present them to others. It shows that you know how to take in and process data and then generate some output.

Real Data and Web Scraping

You can perform your analysis with real data rather than pre-cleaned data. Data collecting, cleaning, prep, and transformation is the real part of a data science job. Web scraping is also a great way to get some interesting data.

Try to Pick an Interesting Analysis

Picking interesting data regardless of what you find could be a great idea. The best portfolio projects are more about working with interesting data and less about showing fancy modeling.

Projects Ideas NOT to Include in a Data Science Portfolio

It’s suggested not to have common projects in your portfolio. You need to stay away from the most common project ideas when building a portfolio. Try to come up with something that will truly set you apart from the others.

Here are a few most common projects that can hurt you if you include them in your data science portfolio:

  1. Survival classification on the Titanic dataset.
  2. Digit classification on the MNIST dataset.
  3. Flower species classification using the iris dataset.

These are the most common projects that can hurt you more than they help you. You can’t find ways to distinguish yourself from others using these datasets. You have to make sure to list novel projects to stand out from the rest.

Specific Data Sources Ideas

A few great sources for data are – Reddit, Tumblr, Sports, Wikipedia, Nonprofits, University websites, etc. Besides some difficult ideas as they have restrictive API policies – Facebook, Yelp, Foursquare, LinkedIn, and Craigslist.

Once you have a few interesting projects to add to your data science portfolio, your next step will be setting your work in the best possible manner. To give more weight to your data science projects, you can use GitHub URL, write blogs about your achievements, and create dashboards using BI tools.

 

Original. Reposted with permission.

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