My experience with Springboard’s Data Science Career Track Bootcamp
I had a great full-time job, but I found myself trying to squeeze in an hour here and there every day for learning more and more about data science. I was spending pretty much all my free time listening and reading about how companies and governments were employing data driven strategies. I was trying to think of projects I could do that used data science in day-to-day life. This is when I knew I definitely wanted to take my career on a different trajectory, and I started researching data science courses, bootcamps, and university programs.
There is a lot of different types of bootcamps out there, ranging in prices and time commitments. The choices get even harder to make when you are working fulltime as a teacher, and no one seems to know how long work from home will continue for. On top of that, you don’t want to decide on a learning path that comes at the expense of your own students.
I first used SwitchUp to narrow down my choices to General Assembly and Springboard. I then dug deeper into the courses and the two schools. Their courses were quite similar in structure, with the biggest difference being Springboard ran a self-paced course whilst General Assembly did not. Honestly, I would have preferred having a more rigorous and structured program, but problems with scheduling during the Covid-19 pandemic etc. led to my General Assembly course not working out, so I went with Springboard and enrolled in the Data Science Career Track.
So, what do you do in a Data Science Bootcamp?
With Springboard, they start you off with the Data Science Method from problem identification to documentation with plenty of programming practice with Python on Data Camp and LinkedIn Learning for data wrangling, data visualisation, pre-processing, and modelling.
They quickly help you build up your ‘data science stack’: I learnt introductory SQL, did practice exercise with R, and listened to a lot of lectures and read papers and articles on the maths behind various machine learning algorithms. I learnt a lot! I come from a Physics and Engineering background and have been teaching Maths, Physics, and Statistics for the better part of a decade. And, even with my maths background, I still learnt a lot.
I’ll be honest, I found the bootcamp to be exhausting. I know it’s self-paced, but I was on a pay-as-you-go scheme, and I took 4 months finish the course rather than their recommended 6 months. The Udemy courses I’d mentioned in a previous post helped me go through the Springboard material quickly, but it was still absolutely tiring. It is worth noting that if you wanted to, it’s quite easy to skim through the lectures, or you can spend some time going through the material for deeper learning. Thinking back on it, I may have spent a little more time than you really need to in some of the programming practice sections. I think the course is designed so people can decide for themselves how deeply they want to go into certain areas. Now that I’ve finished the course, I do find myself going back through a lot of the material to remind myself of the theory and to keep my skills up. As for DataCamp and LinkedIn Learning, Springboard selects the most suitable chapters/sections of courses where appropriate but gives you access to everything, and they pay for your DataCamp subscription for a whole year. For sections where I was less confident, I found myself doing more than the recommended subtopics as this helped me gain confidence and brush up on some topics from now and then.
Another major part of the bootcamp was focused on building up soft skills like communication and presentation- storytelling is a major part of being a Data Scientist after all! There are lots of opportunities to present your work to mentors, who are technical data science experts, and other students. As I’m based out of Hong Kong, so the time difference meant I didn’t attend many of these, but they were scheduled regularly. There are quite a few social events organised by the Springboard, however, the time-difference between the US and HK didn’t make these ideal either, particularly as I was also working full time. Apart from getting a chance to present capstone projects, the virtual meet ups were designed to help with networking with many of them featuring guest speakers. To help practice for interviews and networking, there were also opportunities to meet career advisors individually and in groups. There are also quite a few practice interviews as part of their curriculum which were excellent yet terrifying. The interviewers had a lot of advice on what to do after the course, what skills to focus on, and gave lots of friendly nudges towards resources such as Leetcode, HackerRank, and SQLZoo.
Careers service and job guarantee
There’s quite a few bootcamps out there with job guarantee clauses, and Springboard is one of them. This is only offered for the US/ those who have the right to work in the US so didn’t apply to me. In terms of the career service, I found their careers advisors to be very helpful but many of them said outright they lacked experience with international placements and job markets. I did get a lot of advice on where to look for jobs and on various international part-time job sites which was helpful. I particularly found their advice on resumes, LinkedIn profiles, and networking to be encouraging but also insightful. Overall, the team was great to speak to, but it wasn’t ideal for someone in Hong Kong or requiring visas for working outside of Hong Kong in terms of careers services. I did make friends on the course who had the right to work in the US, and they found the service much more helpful. At the end of the day, they specifically say they can only make this guarantee for the US so if job guarantee is something you want from your bootcamp just make sure you have the right to work in the US if you go with Springboard.
Was it worth it?
Well, I’ve now finished the Data Science Career Track bootcamp and am looking for opportunities in data science. I am slowly getting over the imposter syndrome, and I know I can make a meaningful contribution as a Data Scientist. So, was it worth it? Definitely. Maybe, I could have come up with the same set of resources if I’d looked harder, spent more time searching for what I should be doing/reading/watching next. I’ve not done the calculations for how much LinkedIn Learning or DataCamp would have cost me where I to pay for it independently. Moreover, they give you a lot of resources to help you with interviews such as lists of questions and practice answering them in mock interviews. As I mentioned earlier, I was on the pay-as-you-go structure. Even so, Springboard is not cheap, but I think the resources were definitely worth the money. Whilst those four months sped past at light speed, the learning curve was steep, I definitely came out the other side feeling more confident about changing careers.
