Galvanize

Moved from SD to Bay Area to take this bootcamp. Probably one of the best decisions I have made. Was enrolled in the Data Science bootcamp and went through a lot of material. The idea is to get a working knowledge of the main concepts and a project at... Read More the end to showcase what you learned. If you already have a stats, math, or CS background, then you will fit right in. At the end of course, you will get help from career services in landing a job. The best thing about the bootcamp is the connections you will make throughout the course. Best of luck.

I loved my time at Galvanize, completely through and through. I have been through several vocational schools before arriving at Galvanize and I personally felt that this experience from the beginning was going to be different. Going to Galvanize as a... Read More student has changed my life and I am not in a place where 2 years ago I would've thought impossible. I can't say enough good things about Galvanize and I whole-heartedly recommend it to anyone who is curious about learning to code, or wanting to make a career change.

Galvanize was exactly the challenge I was looking for and more. I started back in 2017 after a couple years of on and off self-study. Tired with not being able to make the moves at work and needing the immersive environment and training to break into... Read More the field and be capable and competent, not just copying and pasting Tensorflow tutorials around and not understanding what a p-value is, which I see a lot of people doing these days. Galvanize taught me what it really means to be a Data Scientist. Galvanize taught me to be a healthy skeptic, a problem solver, a continuous learner, and an advocate for the Data Science community. I learned many of the fundamentals necessary to get into a job in the industry, but there is so much more to learn once you're there. Galvanize gave me the mental fortitude to know I can succeed if I was willing to put in the work. A growth mindset and a learn-to-learn teaching style, the instructors won't simply give the answers to anything, they will help you work towards the answer yourself. Which is immensely helpful. The community you are introduced to and encouraged to participate in is tremendous as well. One of the hardest things to do being new to an industry is networking and knowing what you need to know, the instructors, and Galvanize staff help guide you and open the doors to things you may not otherwise have been exposed to. That helps a lot in getting nudged in the direction that they know will help you in the long run of things. One thing that is always a "want to know" from prospective students is "what is the placement rate". Which is a good question, we want to get a job after all this effort and money. For example; I had to quit my job and live on savings for 3 months to do this, there were no guarantees, and there will never be. But I can tell you that the instructors and staff will do everything in their power to get you placed. It is up to you to develop the competencies and aptitude to get hired. So don't slack off on being in the course because of your pedigree or simply because "you're in", you will not survive with that attitude. You need to always stay hungry and continue learning, or you will quickly be left behind.

This course includes the most topics that would be used in the actual job as a Data Scientist. There are a lot of learning due to the short period of time and the large amount of information to be discussed. The instructors are very accessible to the... Read More students and the career service will prep the students step by step which I found very useful. There were not many projects in my cohort but I heard they have changed their curriculum to include more projects recently.

The Galvanize Data Science Immersive program is a great way to advance or pivot your career . Personally, I started with some knowledge of ML, but I was finding that self-learning and taking courses online wasn't quite getting me where I wanted.

The bootcamp... Read More is set up having two 90 minute lectures a day where you learn first about github and python, later expanding to ML topics. After each lecture you have a few hours to work on exercises generally to attempt to code the logic of each ML technique within your own Python classes. In the morning, you'll code individually and in the afternoon you'll be paired with a classmate.

I thought that the structure and curriculum of the program were excellent and I learned a lot of new techniques some of which I use at work today, and others that I can use for side projects. I ended up deciding to take an analyst job at a large tech company where I can continue to build upon what I've learned at Galvanize.

I wouldn't say that this is a program that can replace a PhD program as you don't have the time to get deep understandings of each of the ML techniques before moving onto the next. You won't learn most of the mathematics behind the ML techniques, but it will give you exposure to most of the things that are being used in the tech industry today. I had a great experience at Galvanize and I would recommend it to anyone that is curious about learning more about machine learning.

SwitchUp initially matched me up with Galvanize when I was researching bootcamps. I did online programs previously and visited a few bootcamps (attended sample courses when available). I immediately knew that Galvanize was the best match for my learning... Read More style. I find that the curricula of most data science prep places cover the same topics - Galvanize's curriculum was a great mix and match between rock solid theoretical foundations and hands-on implementation experience. Accordingly, the bootcamp was intense, required great math and technical foundations - 10 hours days plus a lot of extra reading coding (plus material you don't even get to cover and can dive into later). Now a data scientist, I still use the course materials as reference. Where Galvanize truly shined was after the bootcamp was over. If you are switching careers, you have to slug through many interviews - career services was there for me with unbelievable one-on-one support every step of the way. If you paid a career coach, you'd have paid easily the bootcamp's price in hourly rates for all the help and support I've received. Pros: outstanding curriculum and instruction, small class size, extraordinary career support. Cons: their motto covers it all: "This is not school." They will give you all the tools to succeed but they will not force you to use them. It's up to you how hard and how fast you push yourself to achieve your goals of becoming a data scientist.

As somebody that never finished my degree but had a few of the required tools to be a data scientist, being a part of the Galvanize program in NYC was the most incredible 3 months of my life. Before I went to Galvanize, I was managing a pool store. I... Read More loved it but the pay just wasn't there and I always found myself too far from the technology I desperately wanted to work with. In 3 months (and then another 1-2 months of practice and job searching), I was able to get a job at a fortune 50 company as a data analyst, and then was promptly poached to a startup to be the first data scientist on the payroll reporting directly to the head of analytics and CEO only a few months later. All because one of my best friends (someone who did a Galvanize program a few months before me) told me I had to do it and would not take no for an answer until I went and interviewed for the program. At this point, I'm a year and 3 months out of graduation and have also taken up being a teaching assistant at a similar bootcamp because I loved the experience at my bootcamp so much. Free time is hard to come by at this point, but I get to do what I love every single day and it's all because of 3 short months of hard work... My instructor was an incredible and patient woman with a bioinformatics background and my classmates (who are all data scientists or engineers now) were some of the best and brightest people I've ever had the opportunity to meet. The person who ran career services spent nearly 20 years in the recruiting business and knew all of the tricks to help us get hired. He was the first person I formally met on campus, told me to lighten up when I was visibly terrified before my on-campus interview, helped me navigate my exit from my first employer after Galvanize, and we still call each-other to check in sometimes. The teaching staff, (even instructors I had no classes with) were infinitely more approachable than the professors I came across during my years in undergrad, and the support staff kept our moods high even when stress levels were up. Everybody on that campus was there to help or listen, everybody understood how tough it was, and everybody knew that it was for the best and that you should just keep pushing. This program will give you everything you need to go out there and get a data science job if you make the best of it, no matter how scared you are on the way in, or how impossible of a task some things feel like when you're there. You will leave with every skill you need in your quiver to start a job as a data scientist or engineer and you will find yourself thinking about how much fun you had the entire way many months down the road. I know this because I think about my time at Galvanize NYC every day and am doing my absolute best to replicate that with the students I work with now. If you have any doubts about this, go to campus, meet these people, and never look back. I couldn't say enough about my time at Galvanize.

Yes, Galvanize taught me machine learning. Yes it taught me structured coding. Yes it helped me get a job. But more than anything it taught me confidence in my ability to learn. That last part alone makes this a great program. It took me about a month... Read More to get a job after. Is it a pricy program? Yeah. But it's worth it.

* Introduction: I had a tremendously positive experience with the Galvanize Data Science Immersive in Seattle. I feel very fortunate to have gone through the program at the time I did, with the cohort I had, and with our incredible instructors. The path... Read More that started largely with Galvanize has led to the present moment, where I am a data scientist at a major biomedical research organization, doing what I might consider a dream job, and feeling like I could end up staying here for a long time. I want to give as realistic a view as possible of different aspects of my experience at Galvanize. With the exception of my instructors (who I believe deserve to be named directly, see the instructors section below), I will avoid naming specific people, although there are many folks who contributed to the quality of my time at Galvanize.

* Background: I was a high school and middle school teacher for 12 years, teaching a variety of subjects mostly around science and languages. I studied chemistry in undergraduate school and had a strong background in math and analytical/logical thinking, but I had minimal coding experience. I started learning python in earnest a few months before the start of the program.

* Admissions and Preparation: After deciding that I wanted to move into data science as a field, I had initially applied and been accepted to a different immersive program (General Assembly). I decided to visit two other DS immersives in Seattle in the interest of due diligence. The first (Metis) seemed really positive, but not so much better than the program where I already had a seat. After my first visits to Galvanize, however, I felt strongly that it was the program where I wanted to go.

From the start, I had the impression that everyone involved (instructors, admissions people, program director) was interest primarily in helping me evaluate what was best for me. Yes, Galvanize is a for-profit institution, they naturally need students in their courses if they are going to be able to overcome the costs of their education programs, but I never felt like they were trying to fill seats. With me, and later with others whose process I saw, the emphasis seemed to be on assembling a cohort of qualified students whose goals and previous experience were a good match for the program. On a number of occasions, I knew of people being encouraged to take more time to prepare before applying, aiming for a later cohort. I was encouraged to consider multiple programs and choose the one that felt best for me. I was also able to observe class sessions and to meet with instructors, the program director, and current students.

Once I sat down with a specific admissions person, she was helpful and thorough. She listened carefully and was responsive to my questions and thoughts and helped me plan the timing of my application and the different pieces involved, including program costs, discounts, and ways of paying. I got some well-structured study materials leading to a take-home assessment. The next steps were two (remote) technical interviews focusing on statistics and python. I prepared carefully for each and found the interviewer to be extremely helpful. He gave me excellent feedback at the end of each interview and made clear his willingness to correspond further if I had other questions. When I actually wrote him asking more questions, his replies were timely and showed a real desire to help. I have written him a few times since and gotten similarly helpful replies, even though we are way past the point where he was expected to be helpful.

After I was accepted, I began the pre-work for the course. It consisted of a variety of modules aimed at solidifying different concepts in programming, statistics, calculus, linear algebra, and some other pieces around growth mindset, impostor syndrome, and related ideas. I found it to be generally well-ordered and helpful.

I think that there are now additional data science prep materials available online, with different options for how to work through them. I felt well prepared for the course, but definitely would have taken advantage of those had they existed when I was getting ready. I think they are worth a look for anyone considering a data science immersive program.

There was also a week 0 program provided to accepted students at no additional charge, focusing just on python skills. It wasn't mandatory, but most of my cohort was there and I think that everyone except very experienced python programmers should plan to attend.

* Cohort: I found my classmates to be a remarkable group of people. I think there were 19 of us to start, with one person dropping after a week (I think maybe feeling underprepared/overwhelmed), another leaving halfway through (due to a personal situation and finishing with the next cohort), and one student who left the program early to take a job. My cohort was about half women. My peers came from a broad array of backgrounds, with PhDs sitting next to MDs, software engineers working with former teachers, and recent college grads collaborating with people in the workforce for decades. It was a wonderful group to learn with and many of us are still close and in regular contact.

* Program Structure: Having spend more than a decade designing curricula and teaching, I was eager to see how they were going to structure the overall program and materials. Most days consisted of a morning lesson leading to a solo programming exercise through lunch with a second lesson and pair programming exercise in the afternoon. This routine was interrupted by a number of case-studies (some a single day, others several days) oriented around groups of 3-4 students working toward a goal together. There were also periodic visits from people in industry, including previous grads of the program and people from organizations who work out of the Galvanize co-working space. We also had regular lessons with our career services person on different aspects of the job seeking process (I'll include more about that in its own section below).

*Capstones: The program was formally 12 weeks long, although a much needed break week in the middle and the week 0 offering made for an overall length of 14 weeks. The last 3 or so weeks were devoted mostly to our capstone projects. I think the structure of the capstones is different now (with 3 spaced iterations, perhaps), but ours involved a series of proposals starting halfway through the course. We refined our ideas and got feedback from instructors and other students on different datasets and how we might explore them. With each subsequent proposal, we focused our ideas, eventually creating scrum groups and work tables, and culminating in a tightly scheduled delivery of an MVP, a more fully formed project, lots of practice presentations, and a capstone showcase. In the showcase, we were arranged in stations with computers and screens and got to present our work a number of times to people walking through. While some of those people were friends and family coming to support us, many were hiring partners invited by Galvanize, others from industry who were aware of the Galvanize program, and people from the businesses housed in the co-working space elsewhere in the building.

* Curriculum: There is no such thing as a perfect curriculum, but everything else can cover a pretty broad range in terms of quality. I think the curriculum offered to us showed significant, consistent effort by the instructors to convey the most important materials in the best ways. While there were some shortcomings (not enough practice with SQL, odd sequencing around pipelines, feature unions, etc.) and the frequent revisions made for the occasional typo (frustrating if you assumed something wasn't working because of a mistake on your part), I think a big part of the strength of the Galvanize DSI lies in its curriculum. Working through things today, I have several times revisited old lessons from Galvanize to look more closely at aspects of what we learned. With topic after topic, I have found that the curriculum presented at Galvanize helped me create a good foundation suitable for future learning.

* Job Support/Career Services: Starting early in the course, we met regularly with our career services person, both in full-class session and individually. We worked carefully on how to craft a technical resume, how to portray yourself well on LinkedIn and similar sites, how to approach networking at events, and many related skills. Throughout, there was recognition of the fact that the skills involved in being a data scientist were not identical to the skills involved in getting a job. If you wanted to get a job in data science, you had better become good at both. With our instructors, we practiced white-board coding challenges often and conducted mock interviews with them and with each other. Our career services person helped each person plan a job seeking strategy (including the obnoxious but necessary idea of personal branding) and checked-in frequently on how different portions of the process were going. After the completion of program, she held regular job club meetings where we would gather to discuss our efforts, celebrate people who had found exciting jobs, and commiserate when things weren't going easily. I don't know exactly, but at last check nearly everyone from my cohort has managed to land a job that is either very exciting or is a solid step toward that kind of job.

* Instructors: Saving the best for last, our instructors were incredible. They consisted of Matt Drury, Miles Erickson, and Jack Bennetto, forming a remarkable dream team who had taught the course together for years and whose different areas of expertise seemed perfectly matched. Great teaching can overcome mediocre curriculum, but the greatest curriculum cannot overcome mediocre teaching. In our case, we got to reap the rewards of what happens when a solid curriculum meets exceptional instructors. Matt, Miles, and Jack were fully committed to our learning and our success. They poured themselves into the program, guiding us through lessons together, working with us in smaller groups, and sitting with us individually as we tried to work through whatever whatever challenge was present. Each of them was constantly working to perfect his craft, and the results showed those efforts. While I think they deserve to take pride in their work, no lesson or module was sacred and they were always trying to find better ways and open to feedback. I brought a lot of things to the course, I worked very hard, I am proud of where I've landed, but I am confident in saying that I would not have been able to end up where I am without such incredible help. It's something I think about frequently and leaves me feeling a great deal of gratitude.

* Conclusion: As I said at the beginning, my experience with the Galvanize Data Science Immersive was positive and fulfilling. It led me to an exciting job and a lot of lasting connections with wonderful people. I can only speak to the experience I had during my time, but I hope this review will be helpful to some in evaluating different data science immersives and trying to figure out if Galvanize could be a good fit for them. As a final thought, I'll channel the former president of my college in encouraging people to avoid the question of which is the best school. Instead, consider which is the best school for whom. I wish you luck finding the best school for you.

Pros: The curriculum is built with a strong sense of what skills a Data Scientist will need. The staff and faculty are sincerely invested in your success. The fellow students are committed, diverse, and talented. The location and environment is a plus.... Read More Frequent events are held in the space that expose students to industry professionals and companies. Cons: It's not free, but hey, what is? There is a fair amount of turnover in the faculty, but the curriculum is strong enough that learning is minimally impacted. It's really hard (one could consider this a plus). Note: This form forced me to choose GalvanizeU or Software Engineering as my "course". I did the Data Science Immersive.