How to Use Micro-Credentials to Get Your Foot in the Door as a New Data Scientist
Nowadays, you can get micro-credentials in anything from animation to basket weaving and everything in between.
Micro-credentials have taken the learning world by storm over the last 7 years, allowing people to earn badges for acquiring and demonstrating a specific skill. Some of the first micro-credentials were for programming skills, but have since grown to include skills in a variety of industries thanks to the rapid development and deployment of micro-credentials by post-secondary institutions to help fill skill gaps in their region.
Micro-credentials for Data Science are no different, with a simple Google search returning hundreds of results for related micro-credentials from post-secondaries, private companies, and learning platforms all around the world.
But what value do micro-credentials really hold in the data science job market? Can they be used to help you get a foot in the door? Are they a good idea for new data scientists or just those already in the industry who are looking to remain relevant? All of these questions will be answered here with a look at how you can use data science micro-credentials to bolster your resume and help you land that first job in the industry as a new, up-and-coming data scientist.
But first, what are micro-credentials? A short history.
Micro-credentials are digital badges awarded to students who learn, demonstrate, and exhibit a very specific skill – typically something that can be taught or learned in 8–12 weeks, or less than a typical university-length course (about 16 weeks depending on the course).
Micro-credentials are offered by post-secondary institutions, private companies or not-for-profits, and learning platforms, such as Udemy or Coursera. Micro-credentials have come a long way in the last several years, with many now produced through the involvement of governments and industry leaders with universities to create credentials that target specific skill gaps.
According to Forbes, the talent shortage the western world is presently experiencing due to the vast retirement of baby boomers is affecting nearly every industry and business in a major way. Research suggests that a great majority of executives are finding that, while there is no shortage of people willing to work, it's becoming difficult to find employees with appropriate skills and talents.
The benefits of micro-credentials (when designed correctly) include scalable and cost-effective training (bite-sized learning opportunities at a fraction of the cost of university degrees), on-demand and individualized learning (micro-credentials can be completed in a short period and usually at your own pace), not to mention that micro-credentials are designed to align business needs with career aspirations.
What value do micro-credentials have in the data science job market?
A simple Google search for "micro-credentials for data science" returns hundreds of results for courses from post-secondaries, private companies, and learning platforms. However, the question remains: even though everyone is producing micro-credentials, do they hold any value in the data science job market?
According to a research study conducted in 2020 by Thomas Gauthier, Associate Dean for the Department of Trade and Industry at Palm Beach State College, "Microcredentials offer merit to an applicant's transcript while highlighting skills gained in an authentic setting." Gauthier goes on to say that the validity provided by micro-credentials from accredited institutions can give employers a "clear understanding of a candidate's abilities before extending an employment offer."
Luckily, data science, being a part of the tech sphere, is already at the forefront of accepting alternative education from potential employees thanks to the phenomenon that many data scientists are self-taught without a direct university education in the area. Research conducted suggests that more organizations are moving towards skill-based hiring, something that has been going on in tech for decades.
I've experienced this firsthand with the people I've worked with in the tech industry. For example, one of the people I worked with had a formal educational background in computer science but had gained data science skills through micro-credentials and online courses. Their ability to demonstrate the data science skills we needed was enough to allow her to expand her job description to include data science tasks. It didn't matter that their education was from a micro-credential and not a university degree because they could do everything we asked of them, and then some.
Can micro-credentials be used by new data scientists to get their first job in the industry?
While having an undergraduate or graduate degree in data science still seems to have a hold on industry hiring standards, micro-credentials are quickly building a reputation for being able to bridge the skill gap we're currently encountering.
It's encouraging to see that, for example, many employers are beginning to see Coursera certificates as valuable and valid representations of a candidate's skill set. This shift in hiring policy, along with increased industry involvement in developing micro-credentials, suggests that micro-credentials are becoming a gateway for new data scientists to break into the industry. While micro-credentials likely won't be enough to get you that six-figure salary so many data scientists covet right from the get-go, it will be enough to get you that first job.
As I mentioned previously about my co-worker, micro-credentials are more than enough to begin doing data science work. What most employers care about is whether or not you can do the job, not how you came about learning the skills required.
When in doubt about whether a micro-credential can give you the career boost you need, try taking a micro-credential designed by a post-secondary institution, such as these offered by some of the top universities in Canada:
- Microcredential in Data Analytics with Python – McGill University
- UBC Data Science Certificate – University of British Columbia
These types of micro-credentials give you the best of both worlds: a short-term, skill-building experience backed by the name of a respected post-secondary institution.
How to use data science micro-credentials to get your first job in the industry as a new data scientist
Like other alternative forms of data science education and experience, micro-credentials just have to be paired with a strong portfolio and resume and marketed properly to the right clientele (read: employers).
However, before we get ahead of ourselves, let's start at the beginning, with which micro-credentials you should be focusing on.
The micro-credentials that will give you the most bang for your buck are those developed in post-secondary institutions with input from industry and the government. These types of micro-credentials will hold the most weight from potential employers because not only are they from a post-secondary institution (which, let's face it, is still important in today's society) but they've also been signed off on by industry leaders and government officials who are working to fill a skill gap. Additionally, you should focus on completing micro-credentials that have a capstone project. Capstone projects not only provide evidence of learning but can also be added to your professional portfolio.
After completing your micro-credential, you want to immediately begin putting to use the skills you learned. The pace of micro-credentials can often be quick, meaning you may not retain as much information as you might when working on a course over 16 weeks in a typical university setting. Therefore, now is the time to begin building your professional portfolio, building data science products to sell, sharing your data science knowledge on blogs, and even doing pro-bono data science work for local businesses.
The next step (which may be completed in tandem with the previously discussed step) is to begin developing your resume and learning how you're going to market your skills. Because your learning path is slightly different than what other job candidates will have, you want to demonstrate how you've leveraged your micro-credential into personal projects, pro-bono work, and a track record of authority in the area of data science. These items, the physical work experience (whether paid or unpaid), are what employers are looking for in someone who they will need to hit the ground running.
For example, my co-worker demonstrated their data science expertise by posting informational content on LinkedIn. Marketing your skills this way is a great way to show employers that you learned something immediately applicable from your micro-credential, while also showing that you can teach others to do the same thing after receiving your training.
One of the keys to leveraging data science micro-credentials is selling employers on the fact that you learned data science techniques in a short period which makes you immediately able to hit the floor running if they hire you. Not only does this demonstrate your teachability and ability to self-start, but it also shows that you won't back down from a challenge and can get things done. My co-worker began speaking up about taking on small data science-related aspects of projects after they completed their micro-credential, which gave our employer the chance to see what they were capable of. This led my co-worker to further data science-related work on aspects of company projects.
5 Things in Your Resume That Are Keeping You from Getting Your First Job in Data Science
How to Create a Professional Portfolio on GitHub That Will Help Land Your First Job in Data Science
How to Effectively Showcase Personal Projects on Your Data Science Resume
Finally, you need to begin preparing for your interview and how you plan on wowing potential employers. Standing out from candidates who have university degrees in the subject doesn't have to be difficult if you've adequately prepared for each interview format you'll likely encounter throughout the process. In short, you'll want to be engaging, professional, and knowledgeable in your non-technical interviews, and communicative, creative, and transparent in your technical interviews.
Final thoughts
Data science micro-credentials show employers that you've taken the time and money to complete structured education in the area, something that you just need to show is as good or even better as those who have spent 4 years learning data science in university.
The real key to getting your first job as a data scientist with a micro-credential is to expand your learning in relevant ways beyond what you've been taught in the course. This means combining relevant self-learning with your structured course material to make you as relevant as possible for future employers. Recognize that relevance is the key term here and is what will help set you apart from other data science candidates out there.
Subscribe to get my stories sent directly to your inbox: Story Subscription
Please become a member to get unlimited access to Medium using my referral link (I will receive a small commission at no extra cost to you): Medium Membership
Support my writing by donating to fund the creation of more stories like this one: Donate
