Word from HRDive.com is that employers have a preference for work experience over a Master’s degree for the most desirable data scientist hires. Data and analytics firm Qlik found 60% of respondents to their Data Literacy Survey ranked previous job experience or a skills test as a key measure of a job seeker’s data literacy. Only 18% said a college degree in STEM or data science was the major consideration in hiring.
This relaxation in hiring requirements seems to be coming about due to the ongoing struggle to find skilled candidates. It may also be impacted by the availability of open source software and the ease with which that software can be learned and implemented. Data Science libraries in Python like NumPy and Panda make it easy to use many data analysis tools.
As recruiters in the data science space we don’t see our clients “settling” for candidates without a Master’s degree. They question whether boot camp graduates have the depth of statistical knowledge needed to catch bias or underlying artifacts that may lurk in complex analyses done on enormous data sets. A lack of mathematical or statistical rigor or computational scalability means embracing the wrong solution.
There are positions available where having two of the four skill sets required for a data scientist is adequate. Those with data management and programming skills could be a data analyst or data engineer and may be able to get these skills from a boot camp. You might naturally be a great communicator fulfilling the third out of four skill sets. Advanced statistical skills will be difficult to acquire exclusively on the job or in a boot camp. Look for training through the new one year Master’s Degree in Data Science cropping up all over the country.
For either a one year master’s degree or an intense boot camp here are four main questions to answer before making your choice:
Does this particular education venue offer training in the area you are deficient in. Always great to supplement your skills but if you don’t have one of the four basic needs, adding on to existing skills is not what you should be targeting.
Is networking with current practitioners built into this program? Finding out how what you’re learning can be applied in real time makes the learning process “stick” better.
Will you have the opportunity to do projects that will build your portfolio as part of the program? Interesting and informative projects on GitHub are getting to be a minimum requirement in the data scientist application process.
What do former graduates of this program do, and how quickly did they come to do it? If 80% of the students in this program are already working full-time in data science before completing the program, this program probably won’t get you a NEW job. Were graduates immediately employable or did they have to search on their own?