Examples of Data Scientists’ Portfolios

While searching for employment as Data Scientist it is important to show your skills with well prepared portfolio, just as Developers show their github accounts to show their programming skills. Your portfolio should show how you use your skills stated in the Resume. I think that the most important thing is to tell a story about chosen data, show what you can do with openly available data, how insightful you are when it comes to asking questions based on the data and whether you can represent the results clearly (but also aesthetically and beautifully).

Let’s look at some examples of projects representation:

1.Projects – scientific way

http://timdettmers.com/data-science-portfolio/

I like this portfolio because I am scientist (but probably it’s not ideal for recruiters). Each project is described with abstract, methods and results with discussion (with accompanying figure). It’s quite simple with no graphical fireworks, but it’s clear.

2.Projects – more advertising way

http://binnie869.github.io/

Projects are represented by title, short comment and a image that redirects to the github project (code).

3.Analyses – tools used

http://gemelli.spacescience.org/~hahnjm/data_science/data_science.html

As the author stated, not exactly projects but activities are shown. Each activity is represented by a graph and short description of statistical method/tool used. It for sure shows skills of the author.

4. Projects – very advertising

http://davidventuri.com/portfolio

For sure the author knows how to make nice website ;). Again, projects are represented by title, short comment (however here in the caption also technologies are included) and a image that redirects to the extended project description or website presenting results.

5. Projects – story telling

http://dsal1951-portfolio-v1.businesscatalyst.com/portfolio.html

I really like this portfolio as it is really ‘story telling’ portfolio. When you enter a project, story about data and various approaches to analyse it are presented.

As you can see, each Data Scientist has different way for showing their expertise. Which is best? Hard to say, depends what you want to do with data science and what kind of a company you want to work in.

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