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Galvanize is an education company that offers software engineering bootcamps under its Hack Reactor by Galvanize brand name. Galvanize offers programs for rapid career transformation, designed so that anyone with motivation can succeed, regardless of education, experience, or background. Please visit the Hack Reactor page on Course Report here to learn more about these bootcamps.
Galvanize also offers Enterprise training designed to rapidly upskill and reskill employees at any level, helping companies develop internal tech capabilities, broadening workforce representation, and empowering the workforce of the future.
Galvanize has long been committed to upskilling and reskilling the U.S. military. They equip active duty service members and Veterans with in-demand technical skills, empowering them to succeed in competitive, high-paying civilian careers in software development, data analytics, and cybersecurity.
The first thing that impressed me about Galvanize was how thorough their enrollment exam was. It took me two tries to get accepted, even though I have degrees in math and AI and a decade-spanning career of programming and database support. Talking to the other students in my cohort, I was far from the only one who'd had to apply multiple times. But the entire cohort successfully completed the program, with no one failing or dropping out, which proved the validity of the application process...
The first thing that impressed me about Galvanize was how thorough their enrollment exam was. It took me two tries to get accepted, even though I have degrees in math and AI and a decade-spanning career of programming and database support. Talking to the other students in my cohort, I was far from the only one who'd had to apply multiple times. But the entire cohort successfully completed the program, with no one failing or dropping out, which proved the validity of the application process.
In my case, I was new to the Python programming language, so I took the one-week Python pre-course (free for Data Science Immersive students). That really helped me get up to speed and become fluent in the language, instead of stumbling my way through it and constantly checking reference manuals. The goal was to be able to focus on the data science we would be learning, not the programming, and it succeeded. I also felt more confident by the end of that week. After all, I'd been out of school for nearly 30 years at that point, and I was a little concerned about how quickly I would be able to learn things, compared to the much-younger members of my cohort.
The lessons were clear and straight-forward, with lots of real-world examples from the careers of the instructors. The concepts built on each other in a sensible way, so that after spending a week on different elements of statistics, it was easy to understand the algorithms incorporating those statistics. Frequently, we would code our own versions of algorithms, really understanding how all the pieces went together, before we applied the commonly available versions of the algorithms. This made us far more knowledgable than simple analysis monkeys that could only drop data into an algorithm and turn the crank.
I also want to commend the knowledge, patience, and care of the teaching staff. They made what could have been complex and confusing, easily understandable and entertaining. Also, they did a great job of shifting their teaching styles to match the personalities of the cohort. Apparently, the cohort prior to ours had a lot of students that wanted the instructors to give them the answers when things got difficult, so the instructors would basically just show them how to Google the right question. Our cohort was fiercely independent, so the instructors had to almost beg us to ask them for help, rather than wasting our time trying to figure things out ourselves. They still wouldn't give us answers, but they'd point us in the right direction, or tell us where we'd made mistakes. And when they noticed a number of students having similar issues, they'd put together a special session to go over that one specific concept.
We did solo work in the morning, pair programming in the afternoon, and towards the end of the course, broke into teams on Fridays to work on bigger projects. Then, of course, we had to do our capstone projects. The instructors were very helpful with giving pointers for things to look at as we developed our capstone ideas. One of the members of the cohort started his capstone work the first week of class. I was able to spend most of our Spring Break working on mine, which helped take the pressure off to produce something from scratch in the final few weeks. (I'd definitely suggest to students to start developing an idea as soon as possible.)
There were additional reading assignments for each day, to be done before class started in the morning, as well as solutions to assignments given out at the end of the day to be compared to what we'd done. There were also "extra credit" assignments on most days, that we could do if we finished early. After we complained about the massive workload, they explained that the idea was no one should ever feel like they had "nothing to do". There was always more to learn. But we weren't expected to learn it all or do it all every day. That took a lot of the pressure off.
We had multiple practice sessions for our capstone presentations, and I was impressed by everyone's work at the Hiring Day presentations. There were an equal number of hiring companies and students at Hiring Day, which was nice, but it did not seem that they were very well vetted. Some were early stage companies that did not yet have funding (one basically offered to pay people with IOUs, convertable to money when their funding came in) or did not have any current openings (one was a recruiting firm that seemed to just want to add names to their roster). I was encouraged by the instructors to turn my capstone into an hour-long presentation at the Denver Datapalooza conference, which was an additional feather in my cap proving my abilities for potential employers, and got me some great contacts within the industry.
I had an additional hurdle that most of my cohort did not have. Because of my age, many of the companies -- even those that requested that I apply for their open positions!!! -- never contacted me. I've since been told by someone who works in this arena that the HR departments in large firms protect themselves from age-discrimination lawsuits by refusing to interview older candidates. It wasn't until I removed any mention of age (dropping graduation years, removing the first 10 years of my experience, and not stating how many years I'd worked in the industry) from my resume that I began getting calls. This really should have been addressed during the resume preparation workshop we had, as well as during the resume reviews.
The Outcomes group did not do a great job preparing us for the realities of a job search. We were under the impression that 1/3 of our class would get offers from Hiring Day, and the rest would get jobs within a month -- two at the outside! -- of graduation. So when I couldn't even get anyone to return my calls within that timeframe, I was distraught and felt like a complete failure. It took me nearly 2 months just to figure out which sorts of companies I should be applying to, that would reward my job history and experience instead of wanting the lowest cost provider of data science skills. I would have liked an acknowledgment that one path toward success is to take short-term data scientist contract positions, building up your resume, while you search for a full-time position, and to get some help finding those. Instead, the only options that were covered were joining a start-up or working full-time for a well established firm. Since their success is measured by how much money the students get when they sign their employment contracts, their motivation is skewed in a way that is not necessarily helpful for the students. Also, the official job prospect software was a joke. They stepped up to the task after graduation, and began sending out job notices to the students who were looking for positions. But it felt like a fumble and a recovery, not like it had been the plan all along.
Galvanize graduates have a very good reputation, and companies that have hired one often want to hire more. There are also lots of networking opportunities, both during the course and in the months following. There's even one specifically for women who have gone through any of the Galvanize programs, and who sometimes feel that the tech industry can be a "boy's club", to help you find female mentors in the industry.
Overall, I really enjoyed the three months that I spent in the data science immersive. If I'd known how long it would take to get a job, I would have signed up to be a TA for the following cohort, so that I could stay in that wonderful environment. I highly recommend the Galvanize program for anyone interested in Data Science.
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How much does Galvanize cost?
Galvanize costs around $19,480.
What courses does Galvanize teach?
Galvanize offers courses like 12-Week Software Engineering Online Immersive, 19-Week Software Engineering Immersive with JavaScript & Python.
Where does Galvanize have campuses?
Galvanize teaches students Online in a remote classroom.
Is Galvanize worth it?
The data says yes! In 2022, Galvanize reported a 100% graduation rate, a median salary of $95,000, and N/A of Galvanize alumni are employed.
Is Galvanize legit?
We let alumni answer that question. 216 Galvanize alumni, students, and applicants have reviewed Galvanize and rate their overall experience a 4.5 out of 5.
Does Galvanize offer scholarships or accept the GI Bill?
Yes, Galvanize accepts the GI Bill!
Can I read Galvanize reviews?
You can read 216 reviews of Galvanize on Course Report! Galvanize alumni, students, and applicants have reviewed Galvanize and rate their overall experience a 4.5 out of 5.
Is Galvanize accredited?
While bootcamps must be approved to operate, accreditation is relatively rare. Galvanize doesn't yet share information about their accreditation status.
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