How to Effectively Showcase Personal Projects on Your Data Science Resume

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Components of a project description for your data science resume

Objective/Goal

Recruiters need to have a clear idea of the objective or goal of your project.

What were you trying to accomplish with your project? What was your project attempting to resolve? What sort of answers were you looking for? What had to be done within the project? Why were you attempting this project?

Now is the time to give a brief overview of your project, including a description of the project objective, timeline, and scope. Keeping in mind that your resume shouldn’t cover more than one page, this section should be succinct with a focus on clearly describing the items listed above in 1–2 short sentences.

Example: Conducted an analysis to determine air pollution levels in Beijing from July 1st to 15th and how they correlated with hospitalization rates during the same time frame.

Role

The projects you list on your resume don’t necessarily have to be personal ones — in fact, you could list any projects that you were a part of.

For example, you could list projects from when you were in college, from previous employment, or from competitions or hackathons.

The main goal is to give the recruiter an idea of what you did and how long it took you to do it. For a project description where you worked as part of a team, you’ll want to go a step further and indicate your role within the project and your responsibilities. Additionally, for a group project description, now could be a time where you list the technologies or tools you used to fulfill your role within the team.

This section shouldn’t be any longer than 1 sentence.

Example for a personal project: Worked independently for 3 weeks to extract, clean, analyze, and visualize Beijing air pollution-hospitalization rate data.

Example for a group project: Worked 10 hours a week as a data analyst within a team of 4 to produce visualizations of Beijing air pollution-hospitalization rate data using Tableau.

Data

Now is the time to give the recruiter an idea of the approximate data set size and skew. This could involve detailing the exact data set used and providing a link (if it’s available freely online) or describing how and where the data was obtained from.

This part of the description could also involve a discussion on the tools and techniques used to obtain, extract, and clean the data.

This section should be 1–2 sentences long.

Example: OpenRefine was used to clean data obtained from Open Dataset A (Beijing air pollution level data between July 1st and July 15th — 350 values) and Open Dataset B (Beijing hospitalization rates between July 1st and July 15th — 50,000 values).

Models and Tools Used

Arguably the most important section in the project description is your discussion on the models and tools used throughout your project.

This is where you indicate its relevance to the job you’re applying for by focusing on listing the models and technologies used. By listing the technologies that appear in the job ad, you indicate to the recruiter that you’ve successfully used them to complete an analysis.

This section should be no more than 1 sentence long.

Example: This project was completed using SQL, Python, Tableau, and a stepwise regression model.

Code

Now is the time to provide a link to the code you’ve written for your project.

The old adage always tells us to “show, don’t tell”. If recruiters are intrigued by what you’ve told them about your project, they’ll then want you to show them exactly what the bones of your project look like.

Therefore, this is the right time to provide a link to your Github or another code-sharing repository.

This section should only be 1 sentence long.

Example: Code for this project can be found here: link

Results

The relevance of a data scientist is always determined by the impact they have on their company.

This means that you need to tell a recruiter how your project yielded vital results. The results of your analysis will prove that you can be given a problem to solve and come back with a solution.

Focus on what your analysis determined, the relevancy of your results, and if you feel so inclined, discuss how your results can be expanded upon in the future.

This section should be 1–2 sentences.

Example: Results from the analysis found that hospitalizations in Beijing spiked during periods of extreme air pollution, which indicates that hospitals in the city should be prepared for an influx of patients at this time. This study could be improved upon by also taking into account extreme temperatures that may also exacerbate pollution and health issues within the city.

How to put it all together to produce a project description

Here is what our final project description would look like once you put together all of the individual parts:

Conducted an analysis to determine air pollution levels in Beijing from July 1st to 15th and how they correlated with hospitalization rates during the same time frame. Worked independently for 3 weeks to extract, clean, analyze, and visualize Beijing air pollution-hospitalization rate data. OpenRefine was used to clean data obtained from Open Dataset A (Beijing air pollution level data between July 1st and July 15th — 350 values) and Open Dataset B (Beijing hospitalization rates between July 1st and July 15th — 50,000 values). This project was completed using SQL, Python, Tableau, and a stepwise regression model. Results from the analysis found that hospitalizations in Beijing spiked during periods of extreme air pollution, which indicates that hospitals in the city should be prepared for an influx of patients at this time. This study could be improved upon by also taking into account extreme temperatures that may also exacerbate pollution and health issues within the city. Code for this project can be found here: link

Once you’ve built this basic description, you can refine it to better relate to the job you’re applying for and trim the fat to produce a smaller description if necessary.

An alternative if you’re running out of space on your resume is to include just the objective, tools, and results in your description, along with a link to where the code repository can be found. Then, the README file in your repository can be used to include more details. This saves space on your resume and also gives you the chance to provide additional details without being restricted by space.

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