| | Anonymous | I picked NYCDSA because it offered the most comprehensive curriculum. Surely, the amount of content introduced was overwhelming especially in the second half of the bootcamp but with unwavering dedication and commitment, which was pretty ubiquitous and contagious in the bootcamp, the workload was just about manageable. I would describe myself as having a relatively strong quantitative background as I have a PhD in physics (though my knowledge of statistics and coding in general is abysmal), the coursework was still challenging. I would also confidently say that the coursework is taught in a way to be conceptually accessible by all. The bootcamp teaches two popular languages (Python and R, some SQL) as well as the core concepts of machine learning. The machine learning topics are taught with a good mix of qualitative and quantitative reasoning. The students are required to do four intense projects along with many exams and homework. Overall, the bootcamp sets up the students well for future success-- it gives us the foundation necessary to continue to grow as data scientists. The job support is unexpectedly great. The bootcamp works with us personally to become competitive candidates for the job market and also actively makes work connections for us. The instructors are beyond amazing as they rise above expectations always. They make you feel that they are truly responsible for your understanding of the topics and they are very open to feedback (in fact, they actively look for it). In hindsight, as a more experienced data scientist and knowing that there is only 12 weeks, I wish that we had focused purely on Python and SQL, and spent more time on coding challenges, algorithms, case studies, AB testing and big data techniques. There were duplicate content taught as we switched between Python and R though for some this may be good because repetition of topics with a different spin can help with better understanding and retention. And despite saying I wish we focused purely on Python, working with R and having the knowledge to use R Shiny was what ultimately made me more attractive as a candidate since I was able to showcase my work as an app. All this back and forth rambling probably just means that 12 weeks is very short and given that the field of data science is growing so quickly, it's really hard to gauge what exactly and what amount to master. Given this situation, if I turned back time and had to decide all over again where to invest my 12 weeks of time, I would without any doubt pick NYCDSA. With their expertise, receptiveness, hustle and attitude of wanting to do what's best for us, I truly believe that any student there will be in good hands. |
