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The Navid Mashinchi Guide To A Data Science Portfolio

Navid Mashinchi has been in the data science industry for long enough to understand what works and what doesn’t. So, when he recommends having a portfolio, it’s certainly wise to take that on board. Branding yourself has become a necessary step in entering the workforce with both cylinders, and for those who have never attempted a portfolio before, you’re in luck.

This will be covering the Navid Mashinchi method of portfolio building, including an outline of why they’re so important and what goes into a successful one.

Getting Started

First things first, you’ll be needing a platform. For those who have some web development experience, it’s not out of the ordinary to code a portfolio website from scratch, though time consuming, it has some weighted advantages for those who want to customise fully.

For those without the necessary experience, or just need something made in a pinch, there are a myriad of sites that offer very clean-cut and easy to use templates (Wix, WordPress etc.) that you can utilise and show off your portfolio.

There’s no wrong answer here, it’s really up to how much time you have and the skills you currently carry.

The Ingredients

So, now we know how to get one started, but what actually goes into a good portfolio.

We can break it down into these 5 essential pages:

  • Home Page
  • About Me Page
  • Skills Page
  • Work/Projects Page
  • Contact Page

Home Page

The home page is the first port of call for your prospective employers to see you and your style. Having an inviting and beautifully appealing home page is vital for keeping people engaged in your portfolio.

 About Me Page

Fairly obvious but a few additional points should be marked here. As the portfolio is to show off your data science chops, it is very helpful to add some pizzazz about what excites you in the industry. Having a resume attached is a tremendous help as well.

 Skills Page

This page should highlight the proficiencies and particulars that you are well versed in. Think of it as your skills section of a CV but with a little more style and expansion. Having relevant data science skillsets listed here is a big help.

Work/Projects Page

The moneymaker page, all employers want to see your skillsets put into practice, so showcasing your current projects or proudest moments is highly recommended here. Any good data scientist should be able to communicate their findings in a clear and concise manner, employers have a special eye for this particular ability and this is your chance to show it off.

 Contact Page

Having the standard number or email should be sufficient. However, following the Navid Mashinchi method of going the extra mile comes into play here. Looking professional with a contact form and a link to calendars for appointments adds that extra spark.