Resume Tips! Do's & Dont's

In this post, we'll be aiming for building better resume's for anyone applying to Data Science positions. When it comes to this field, there are certain Resume differences that need to be taken into consideration; i.e. the resume format will not be the same as fields. 

1. Your Resume should most definitely fit on 1 page. Unless you have enough information to fit on 1.8-2 full pages, stick to 1! Also, keep it simple because most major companies dont have the time to skim through thousands of resume's, so they utilize ATS readers; An applicant tracking system - or ATS, for short - is a type of software used by recruiters and employers during the hiring process to collect, sort, scan, and rank the job applications they receive for their open positions. Single Column resume's are easier to read. Remove your "objective/summary" section as well.
 

2. List relevant courses first! Be sure to adjust your resume to match the position's job details section. List in order of Relevance! Hiring managers literally only spend a few seconds on each resume, so formatting is key!

3. Do NOT rate the skills you list! For some reason, a lot of applicants measure their own abilities and put their rating near each skill (i.e. Python 8/10). Not only is your opinion highly subjective, it's best to leave it up to the company to decide how good you really are. Focus on getting your foot in the door!

4. Since Python/R are the biggest languages when it comes to data science, listing them first makes the most sense. Be sure to list all of the relevant modules/libraries you're knowledgeable with.

5. For all of my fellow self-taught programmers, try your best not to list your MOOC coursework as your portfolio. Although some of them may seem impressive to you, try to imagine the amount of applicants who've taken the same online classes as you on whichever site you use. HR recruiters will certainly notice that every applicant has the basic/simple learning projects. Instead, try to find a data set/project on your own and dissect it.

6. Instead of listing MOOC projects, you can certainly decide to post your Kaggle competition results to demonstrate your skills further. This shows that you are involved in the data science community, and are able to work with other people as you will on competitions.

7. If you have any Amazing projects or papers that you've published, that can certainly go a long way with whoever is viewing your paper. This shows your involvement in the community as well.

8. Be sure to include all relevant websites such as LinkedIn, Github, or any blog posts you run. The current job market is nearly completely digital and although you can't provide the hiring manager with a sense of who you are prior to meeting, having an online presence and good profile will certainly suffice.

9. Lastly, you can use the rest of the resume space to list relevant work experience and education. This may cause concern if you lack any official data science experience, but having any real world experience is better than none. Also, having a degree in any quantitative field will never hinder your chances.

Those are all my tips for creating a resume that stands out. Good luck!