Spoiler alert: It was surprisingly easy (and free) to make
Getting a Data Scientist job is tough.
This isn’t 2015 anymore: it’s not enough to know a number of pandas functions and put the words “Big Data” in your résumé. Competition for the highest jobs is fierce. On a recent trawl through the LinkedIn jobs board, I struggled to search out a London-based Data Scientist role with lower than 100 applicants.
The excellent news is that this competition will not be resulting from an absence of jobs. Even in 2023, Data Science stays a fast-growing field, and the U.S. Bureau of Labour Statistics has estimated that the variety of Data Scientist jobs will grow by 36% between 2021 and 2031 [1].
The challenge, then, will not be that there are not any Data Science jobs — there are tons! Fairly, it’s that a large variety of persons are attempting to break into the industry, making it increasingly difficult to face out from the gang and land that lucrative first role.
In the present job market, I’m convinced that probably the greatest ways to distinguish yourself from the competition is thru constructing a private portfolio of non-public Data Science projects to showcase your skills and experience. This is particularly essential in case you don’t have loads of economic experience working in Data Science roles. To borrow the words of Data Scientist Will Stanton [2]:
If you happen to don’t have any experience as a knowledge scientist, you then absolutely have to do independent projects.
In this text, I’ll walk you thru the method I followed to create my portfolio of non-public projects, and attempt to present you a little bit of inspiration for the way you possibly can create one for yourself.
If the thought of making a portfolio feels a bit daunting, don’t worry: it’s surprisingly easy (and free) to get something up and running, and also you don’t need any knowledge of web development to begin.
My journey into Data Science only began relatively recently. My first jobs fresh out of faculty/university were in fields like Project Management, Sales and Marketing. When I made a decision to make the switch to the Data Science in 2020, I had little or no hands-on experience working with data and didn’t have a technical undergraduate degree in a field like Computer Science or Statistics.
Yikes. That’s not a really strong place to begin.
In my previous post, I shared how I addressed this by taking a bunch of online courses, studying for a Master’s degree in Data Science, and doing a few Data Science internships. Along the way in which, I worked on many alternative projects and built a broad repertoire of data-related skills.
Nonetheless, for the primary few jobs I applied to, I discovered that I used to be struggling to get my foot within the door: it felt like recruiters weren’t even taking a re-examination at my application, despite me meeting all the minimum requirements. On the time, this was incredibly frustrating. I believed that I had rather a lot to supply, however it appeared like the conversations were being shut down before they even began.
To make matters worse, I learned that recruiters often spend as little as 6–8 seconds reviewing a CV for an over-subscribed job posting [3]. That convinced me that I needed a quicker way of communicating my skills and standing out, especially provided that I didn’t have reams of experience as a Data Scientist.
To tackle the this problem, I made a decision to create a web based portfolio as a way of showcasing my experience in a visually compelling and quickly digestible format.
Then, once I’d built my portfolio, I included a link at the highest of my résumé, to make it super easy for recruiters to search out:
When creating my portfolio, there have been a number of basic principles that I followed:
- Include projects which have something genuinely unique and interesting about them
- Keep it short and straightforward
- Make it pretty
- Don’t waste time on the “web development” side of things
In the remaining of this text I’ll briefly discuss each of those and explain how they helped me create a winning portfolio.
Include projects which have something genuinely unique and interesting about them
This might sound obvious, however it is such a crucial point that it bears repeating:
Don’t include content that everyone else includes.
If the entire point of your portfolio is to distinguish yourself, why would you hassle including generic projects which have already been done to death by others? This can be a point echoed by Jeremie Harris in The 4 fastest ways to not get hired as a knowledge scientist [4], where he said:
It’s hard to think about a faster technique to have your resume thrown into the ‘definite no’ pile than featuring work you probably did on trivial proof-of-concept datasets amongst your highlighted personal projects.
When unsure, listed below are some projects that hurt you greater than they show you how to:
* Survival classification on the Titanic dataset.
* Hand-written digit classification on the MNIST dataset.
* Flower species classification using the iris dataset.
What this implies is that in case you’re really interested by Computer Vision, include a few projects referring to that. Or, in case you just can’t get enough of Time Series problems, chuck in something on that theme. Truthfully, it doesn’t matter whether you’re interested by Bayesian Optimisation or Basket Weaving: the purpose is, just ensure it’s something that you just genuinely find interesting and think will add value to others. Personally, I’m really interested by problems related to Natural Language Processing and the social sciences, so I attempted to incorporate projects related to those. But truthfully, it doesn’t matter a lot what you include so long as you possibly can convincingly show why it’s adding value.
Keep it short and straightforward
Nobody desires to spend time on a boring or lengthy webpage, and this is particularly true relating to online portfolios. You’re giving a snapshot, not writing a PhD thesis, and I truthfully consider that you just’ll be wasting your time in case you spend too long writing out lengthy project descriptions and results sections.
To understand this point, we’ve got to return to the unique purpose of a portfolio, and at the guts of that’s one easy idea:
The aim of your portfolio is to get your foot within the door, to not get you the job.
In other words, you’re never going to get a job offer solely off the back of your portfolio. The truth is that, even when recruiters just like the look of your portfolio, you’re still going to need to undergo their interviews and assessments. The aim of the portfolio is just to offer a fast snapshot of your skills and show ’em what you possibly can do.
In my portfolio, as an illustration, I discussed seven projects that I’d previously worked on. For six out of the seven projects, I wrote not more than two short sentences of description.
Yep, you read that right: two sentences.
Here’s an example of a project description in my portfolio:
As you possibly can see, I didn’t waste time setting the context and explaining the nuances of the outcomes. Sure, that stuff is super essential. But when the possible employer is interested, they’ll ask you within the interview. At this stage, you only need a brief description which may pique their interest and prompt further discussions afterward down the road. If you happen to like, you possibly can even include a link to more description elsewhere; that’s sort of what I’ve done by including a link to the code underneath the project description. But on the primary page of your portfolio, you only need the high-level details. Anything is wasting time.
Make it pretty
My next tip is to make your portfolio visually appealing.
Put yourself within the shoes of a prospective recruiter: you’ve read a whole bunch (and even hundreds) of bland résumés and are struggling to recollect what the color green looks like since it’s been so long because you saw something that wasn’t black-and-white. Then, along comes a résumé/portfolio which — hold the front door — is definitely quite nice to have a look at.
You don’t need to be a rocket scientist (or perhaps a Data Scientist) to work out that the recruiter goes to enjoy your portfolio.
To make your portfolio pretty, my top tip is to incorporate numerous interesting graphs/plots. For instance, here is the outline of one other project I included in my portfolio, which has some graphs about Covid-19 restrictions and population movements during lockdowns:
The graphs show prospective employers that I do know how one can communicate ideas visually and make pretty pictures. This may appear trivial, however it’s really not: as a Data Scientist, you’ll steadily need to communicate your findings to non-technical stakeholders. By including some graphs in your portfolio, you’re showing your readers that you just recognise the importance of communication and have the talents needed to place it into practice. In other words, it’s a improbable technique to exhibit your skills and differentiate yourself in a crowded field.
Don’t spend unnecessary time on web development
My final tip is to avoid spending an excessive amount of time actually “constructing” the web site which can contain your portfolio. Remember: you’re applying for a job as a Data Scientist, not a Web Developer. You won’t be evaluated in your HTML and CSS skills; you’ll be evaluated in your Data Science skills.
Consequently, unless you might have extensive prior skills in web development, I’d recommend going for low-code/no-code option to construct your website, for instance using a tool like WordPress or Webflow which permits you to “drag-and-drop” sections of your portfolio and select from a variety of pre-made templates.
In my case, I opted for something a little bit different, and followed this excellent guide written by Ivanna Kacewica, who used the Minimal Jekyll theme for GitHub Pages to construct the barebones of the web site [5]. The primary reasons I went for this feature were (a) it’s free, and (b) it doesn’t require you to incorporate ads or the word “wordpress” within the URL.
An added bonus of Ivanna’s approach is that it permits you to host your portfolio at no cost on GitHub Pages on the address username.github.io, where username is your username (or organization name) on GitHub. Since your GitHub username is included within the URL, it’s super easy for people to search out your GitHub account, which is beneficial in case you’re got additional projects stored on GitHub.
I won’t go into the small print of how one can use Ivanna’s theme (as she does a superb job of explaining things), but the essential idea of this approach is that you just add text and pictures using Jekyll and markdown. If you happen to’re not acquainted with Jekyll, it’s a straightforward framework for constructing web sites that requires no coding or knowledge of HTML/web development.
Using Jekyll, you construct the essential structure of the web site, after which using markdown you populate the positioning together with your text and pictures. If you happen to’ve not used markdown before, don’t worry — markdown is just a extremely easy way of formatting text. And after I say “easy”, I mean really easy: to put in writing a heading, for instance, you only put a hashtag in front of the text you would like to be in your heading. To make a bullet point, you only put an asterisk in front of the text you would like to be in your bulleted list. It’s that easy.
If you would like to see the complete details of how I customised the theme, take a take a look at the index.md file in my GitHub repository.
Constructing a web based portfolio is an amazing technique to stand out within the crowded Data Science job market. In this text, I’ve given some insight into how I went about constructing my personal portfolio and my top suggestions for constructing your individual.
Let me know the way you get on, and in case you’d like all feedback or advice in your portfolio, drop a link within the comments and I’ll make sure you have a look.
[1] U.S. Bureau of Labor Statistics. Occupational Outlooks Handbook: Data Scientists. (September 8 2022). https://www.bls.gov/ooh/math/data-scientists.htm
[2] Will Stanton. Creating an amazing data science blog. (July 15 2015). https://will-stanton.com/creating-a-great-data-science-resume/
[3] Standout CV. How long do recruiters spend your CV? (November 2022). https://standout-cv.com/how-long-recruiters-spend-looking-at-cv#:~:text=Research%20shows%20that%20recruiters%20spend,15%20minutes%20reviewing%20a%20CV.
[4] Jeremie Harris. The 4 fastest ways to not get hired as a knowledge scientist. (June 12 2018). https://towardsdatascience.com/the-4-fastest-ways-not-to-get-hired-as-a-data-scientist-565b42bd011e
[5] Ivanna Kacewica. Set Up Your Portfolio Website in Less Than 10 Minutes with Github Pages. (March 1 2019). https://medium.com/@evanca/set-up-your-portfolio-website-in-less-than-10-minutes-with-github-pages-d0efa8ff56fd
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