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.Tags: Mckinsey Business PlanWriting The Synthesis Essay John BrassilHemingway Essay QuestionsComputer Science Term PaperBuddhism Essay TopicsWorking On A Golf CoursePope An Essay On Criticism AnalysisSteps To Solving Word Problems
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.
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.
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.(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.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.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.So you might be thinking — skim my data science project? The reality is that (at least during the early stages of the job application process) your application will be skimmed. Now, if a project catches their eye, a recruiter or hiring manager will spend more time reviewing your work.Which brings me to my next point: pick a project topic that will make potential recruiters and hiring managers say, Lastly: how many projects do you really need?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.