Currently, there are tons of bootcamp training on Data Science or Artificial Intelligence, depending on the buzzword of the day. Why is that so? Capitalism is the culprit here because there is strong "demand" (notice I put them in quotes?) for talents.
I feel particularly offended when some bootcamps says that participants can be a data scientist within X months (where X is moving towards zero). Why? Because it trivialise the work done by data scientists. Come on, data science work is never simple. Besides the data challenges (data collection & quality), we still have to look at the scoping, training, implementation, strategy and each phase has its set of unique challenges. I dare to say most of the challenges are not something the bootcamps teaches you to solve. But...a true data scientist thrive on such challenges!
A friend of mine shared with me the following tweet and I can identify with it.
The underlying issue with bootcamps charging $30,000 for a 9-month curriculum patched together from free online sources and delivered by inexperienced instructors is just how dystopian "the opportunity" really is. It's capitalizing on a failed state approach to higher education.
— DHH (@dhh) February 11, 2020
Bootcamps are expensive in my opinion especially when most of them are just teaching Machine Learning as the dominant topic. And as stated in the tweet, the curriculum is patched together with free online resources. You can find them online and it is not too difficult. Check out your local data science community, I am sure they can share TONS of FREE resources with you. If you are in Singapore, can come to the community I set up with my friends, DataScience SG.
Still Bootcamp?
Ok before people start rampaging at me, let me put it here first. You can still go for data science bootcamp but it is NOT ENOUGH to get you the data science role. Companies are looking for data scientist who can provide solutions through data. Going to a bootcamp is akin to learning about what is available in the data science toolkit. You get taught on the usage of different tools like hammer is for knocking in nails, nails is for hanging pictures and stuff, screw driver is for screws, pliers is for holding or bending etc.
After the bootcamp, you are akin to being a carpenter who is now familiar with the tools of trade...but you have not demonstrated any capability on building beautiful & comfortable furniture for your clients or in our data science world, you have not demonstrate any capability on solving business problem with data YET!
You still need a project portfolio and for how to build it, here is my blog post. :)
Bootcamp No Advantages?
If you are looking at bootcamp by itself, you can see them as curators and they do provide a structured approach to learning and if that suits your learning style, you can consider but the cost will be a pain. Suggestion is ask around for reviews, show the outline to practitioners and seek their opinion.
Another thing you can do is ask to speak to their top students...but NOT one student ONLY! One data point does not form a trend and you are more keen on the trend/consistency! Speak to a few. Asking to speak to the students helps, because you can uncover many signals on how good the bootcamp is. For instance, how fast they can find the top students to speak to you, how many top students can they find, where the top students are currently, how long did the top students take to find the next job after the bootcamp.
Another important factor to your decision making is the instructor. Ask to look at the credentials of the instructor. How many years of PRACTICAL experience does he/she have, how many projects has he/she worked on and which phase of the data science project is he/she involved heavily in. Think about it, the curriculum are made up of free resources, so the premium that the bootcamps can command are the curation and the experience of the instructor. If the bootcamps are so expensive, in the five digits, the instructor (unique to the bootcamp) need to command that premium! Get to know who the instructor is and their working experience. Assess it, scrutinize it, ask yourself if this is the person you want to learn from.
In summary, to select the 'best' bootcamp do the following steps.
1) Get the outline and ask for practitioners' opinion about them, what are the strengths and shortfall.
2) Ask to speak to a few top students for the bootcamp and find out if the their career has benefited from the bootcamp.
3) Check out the experience of the instructor. Ask yourself if the instructor has a lot of relevant experience and a lot of projects under the belt. My opinion is the instructor needs to have at least 5 years of experience to gain enough exposure and has worked on a variety of projects (say around 7 projects).
Conclusion...
Bootcamp is not a sure-fire way of getting into Data Science, you will still need to work on a project portfolio. If you are paying so much money for a bootcamp, there better be a big enough carrot at the end and the instructors better be worth its salt. Education is an expensive investment so gather more information first before deciding whether a bootcamp is suitable for you and which bootcamp will best meet your needs.
If you will like to start your learning immediately, I have mapped out one that you can use to kick start your Data Science Learning Journey! Do check it out and if this post has been useful, please feel free to share it. :)
Regardless, have fun in your learning and all the best! If you want to stay connected, we do so on LinkedIn or Twitter. Consider subscribing to my newsletter to find out what I am thinking, doing or learning. :)