Data Analysis Projects

Once you’ve decided on a dataset you’d like to explore, the next step is actually figuring out what questions to answer and what to analyze.If you recall what I said earlier: the best data science personal projects are eye-catching and skimmable.

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Before getting into data science, I came from an economics research background — so I knew a ton about where to find and how to analyze U. I was able to explain this project during one of my interviews because the panel was impressed by the visualizations I constructed…Moral of the story: companies are impressed when you have a portfolio of projects.

And personal projects give you the chance to discuss work that you know a lot about and are passionate about. comedy fan; one of my favorite shows of all time is .

During a standard application process, you really have two opportunities to show and discuss your projects to the hiring team: a non-conversational opportunity (so either on your resume/CV or on your personal website — more on this later) as well as during an actual interview.

You need your project topic to work well in both capacities.

However, a project like this is in no way necessary for getting hired as a data scientist.

This may be a subject for another blog post, but in my experience, aspiring data scientists seem to immediately jump to fancy machine learning or deep learning tutorials — and forget about learning the basics and honing their problem solving, critical thinking, and presentation skills.If you’re still struggling for inspiration, a great strategy is finding a way to weave together data and pop culture. It’s fairly easy to take one of your favorite shows or movies, find the script online, scrape the show/movie dialogue, and do some basic text analysis.If you’re intrigued with blending data science and pop culture but need more inspiration, I highly recommend the website Once you have settled on how you will analyze your dataset, the next step is to start coding. Then I recommend you create a Git Hub account and read this introduction.What’s most important here is writing clean, easy to read, and well-commented code. Just pin the repos you want people to see and add clear and concise READMEs that explain what your project is about. Git Hub is a fantastic place to demonstrate your programming ability to hiring managers.Is it easy to digest and is it skimmable, so a recruiter or a hiring manager can quickly read it and understand it?Can you elaborate and discuss it at length to an interviewer?We all know the old catch-22 — you need a job to get job experience and job experience to get a job. You can use personal data science projects to demonstrate your skills to prospective employers — especially for landing your first data science job. It’s important to pick a project you can showcase effectively.And it’s just as important to know how to include it in your resume or CV.If you’d like to go for an in-depth machine learning project — that’s great.But if you don’t, rest assured that simply answering an interesting and insightful question with your dataset is more than enough.


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