I'm a recent SF graduate w a background in consulting. I'm really impressed and in love with everything about the DSI!
Organization and Structure: the organization of this program is insane. Every minute of every hour is designed with purpose. It's very structured and right from the beginning, you'll get a Google calendar detailing the agenda for every day of the program, and rarely do we ever deviate from the plan or even our punctuality. This struct...
I'm a recent SF graduate w a background in consulting. I'm really impressed and in love with everything about the DSI!
Organization and Structure: the organization of this program is insane. Every minute of every hour is designed with purpose. It's very structured and right from the beginning, you'll get a Google calendar detailing the agenda for every day of the program, and rarely do we ever deviate from the plan or even our punctuality. This structure and transparency really helped me as a student to know what to expect.
Teaching Model: At the same time, I strongly support the daily model of learning they promote --which is non-grade based. You have 2 lectures a day and 2 assignments a day (one solo and one with a partner), but neither are ever turned in or graded. Some may critique that that then takes away the external pressure to complete the assignment, and to some degree, that's true, but I benefitted strongly from it in being much more concerned about understanding the concepts and improving myself, than just working towards a grade or idea of "completion".
Quality of Instruction: Another huge positive was the quality of the instructors and the student to teacher ratio/staff to student ratio. Depending on which city you complete the DSI in, you may have a diff number of applicants/cohortmates. But the ratio of students to instructors are the same. For our cohort of 18 people, we had 2 instructors and 2 DSRs (like TA's). As for quality...you know in college when you sometimes get professors who are clearly geniuses, but unfortunately really can't teach? Yeah, that didn't happen here. Concepts, problems, and solutions are explained thoroughly, and we were always encouraged to ask questions. The depth of knowledge our instructors had within them became very quickly apparent. Out of curiosity, I did some good old fashioned LinkedIn stalking and also found out they all have impressive work experience and credible backgrounds. You'd never know otherwise given they are so humble and never toot their own horn.
How Students Get Help: I was initially really concerned about quality of instruction and help given the pandemic, we had to move everything online, but I was impressed by how adaptive the staff were in transitioning to an online format and develop an efficient way to offer help for each individual student when needed. Each day of the program, we typically have a solo assignment and a pair programming assignment. During pair programming, we shared our screen, one of us driving and one of us navigating. When we needed help, we messaged a helpdesk bot on slack, and usually within a few seconds to 2 min, a DSR or instructor would pop into our breakout room to assist. It was all a lot easier than I would have imagined it to be to get help. At the end of the assignments, we were given access to the solutions, which we’d typically go over on your own at the end of the day.
Curriculum: I think Galvanize does a really good job of pulling the curriculum together, from its free basic prep offering, to its premium prep offering, to its actual course. The course is extremely comprehensive and up to date with the current Data Science and Machine Learning industries, ranging from coding, to stats, to ML techniques, building models, and a little beyond. The last topics we learned even covered advanced topics that the industry as a whole is still just dipping into. When I showed what I was learning to other data scientists and machine learning professionals, they were impressed w the amount of topics covered. Built into the curriculum, too, is your own dedicated career coach who will break down the steps to building your resume, cover letter, income negotiating, interviewing, networking, tailored to fit the Data Scientist job entry experience. You have access to the same career coach up to 6 months (and access to the Galvanize workspace in your city) after you graduate, too.
Cost: Yea...it’s expensive. You have several options for payment. Here were the options I had:
- scholarship: You should apply for the scholarships they offer every cohort. During my time, they offered one full ride scholarship. Now I see they might be offering more? The process is: you fill out an application w an essay portion, pass the technical interview, and submit a video by a certain deadline. Ask staff for more info.
- early bird discount: during my cohort, they offered a 10% early bird discount off tuition if you completed passed the technical interview early. Not sure if it’s still ongoing, but again, check with staff.
- payment plans: I give Galvanize a lot of credit for being flexible. You can pay everything upfront, OR pay half first and then the other half when you’re halfway through the program, OR enroll in an Income Share Agreement where you either pay a percent of your income every month for x number of months and a CAP after you get a job, OR you can set up a sort of hybrid pay upfront/ISA hybrid payment plan, OR you go through some lending partners they have who will have a set interest amount.
Process:
- complete prep work
- pass the technical interview (TI)
- complete the precourse work
- start the program.
I would personally recommend taking the premium prep course ($495) which is deducted from the tuition of the program prior to taking the TI.
Difficulty: Was it hard...yea. I came from a non-coding background and even before the program started, I had to put in serious work to meet a baseline for what you are expected to know before the program even starts. DON’T think of this as a “in-3-simple-months-I-can-totally-change-my-career get-rich-quick” scheme. You’re going to learn an incredible amount in the 3 months, yes, and I for example went from not even knowing what a terminal was, to building my own object detection machine learning model. And I am confident now in my grasp of concepts and in applying for jobs. BUT I had to really put in time to prep and learn the basics of python, git, pandas, basic stats, on my own, even before the program started. Not to mention, the full time program is on a M-F 8:30am-5:30pm schedule, but my cohortmates and I often found ourselves staying up until at least 10pm-1am working and reviewing. For me, the experience was really hard, not so much because of the complexity of the concepts, but because 1) the speed at which the material comes at you. 2) the number of topics you need to not only know, but be/become fluent in. 3) I came from a non-coding background, so while I was learning new concepts, I had to also translate them into code. I remember, for example, that the first week was supposed to be “review” on topics we did in the precourse material, but I was completely exhausted by end of week 1 and I realized I didn’t know as much as I thought! This is all to say...yea, it’s hard, but preparation, persistence, and practice will get you through!
Critiques:There are some points I do wish we had more guided help from instructors. Galvanize does push us to be as independent as possible, given the necessity of independent problem solving and troubleshooting as Data Scientist. However I think the bootcamp experience and learning would benefit from a recorded demo of the case studies we completed every Friday. That way, after we attempt it ourselves, we can watch how an experienced Data Scientist would think through such an assignment.
Overall: I’m such a big fan, because I came in as a skeptic but saw the quality of the instructors, comprehensiveness of the curriculum, got to do my own projects which I felt really excited about, and I came out of it feeling confident I can get my first job in this new industry. Along the way, Galvanize has truly demonstrated their support of my learning, and me as a student. They’ve been receptive to criticism (they beg for it, really…) and they do actually try to implement suggestions from students the best they can. Their ISA shows investment in me as an individual, and they are truly focused on churning out capable, quality Data Scientists.
Opinion:If you have the flexibility, see if the winter time cohort is right for you. I like the winter cohort schedule because the holidays allows for more breaks that wouldn’t typically be in the other cohort timeframes. This gave me more time to self-study, review, digest what we learned and prep for projects!