When you’re just starting to look into putting together your own data science project, you might feel a bit overwhelmed.
In this post, I’ll guide you through the data science personal project process — from how to pick a good project topic to how to actually utilize your data science projects in your application. He is a former economics researcher turned data scientist with stints at Tune In, the University of California, Berkeley, the United States Bureau of Labor Statistics, and the Census Bureau.
And the easiest way to make them that way is to create an awesome visualization.
No matter what you analyze, what question you try to answer, or what methodology you use, you need to think about how you will visualize your results.
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.He is currently building marketing analytics and automation tools at an early-stage start-up.Before you start brainstorming topics, it’s important to think about the point of these projects: to show prospective employers you have strong technical skills and a knack for presenting data science results.With that in mind, let’s revisit my Parks and Recreation example and I’ll show you how I’d present this project on my resume/CV: Parks and Recreation Dialogue–Visualized This project is an analysis of my favorite T. show, “Parks and Recreation.” I used R’s ggplot2 to construct the visualizations and Python’s Beautiful Soup to scrape the show dialogue.Okay, so a couple of things to notice: one, yes, this is short. You have your job experience, skills, education, and contact information taking up space.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.(This is good practice in general–but especially important for your data science projects.) Once your code is written, the best way to display your code (and demonstrate to prospective employers that you can code) is to set up a Git Hub account. Just make sure that in addition to having clean and well-commented code, you also include a README file explaining your motivation and what your project is about.Let me just mention this one more time: the point of these projects is to show prospective employers you have strong technical skills and a knack for presenting data science results.