Galvanize

Note: I took the "in-person" Austin DSI. I jumped into this program as a potential shift in my career and shortly after starting the "Free Prep" material I realized they wanted you to really grasp a great amount of knowledge in math and coding before... Read More you could even take the interview. Fortunately I was only in my half way point in my Engineering degree before taking on a career change, due to COVID, into data science (remote), this helped me in understanding the math portion a lot easier. For someone who does not come from a technical background take it from one of my classmates who said "This is not easy for someone who comes from a non-technical background but I am surprised I am now able to accomplish what I have in a short period of time..." So it is very crucial you set yourself up for success. The course and structure is only made in a way so you get a great deal of understanding in the field and so you get a structured learning for what you paid for but all the knowledge you acquire will be solely dependent on your hard work and if you are struggling you will have an amazing community, ready to help at anytime, as long as you reach out. For the Job Search it is very much the same. You will be steered in the right direction but landing your first job will also solely be dependent on the hard work you put in to reach out, get interviews scheduled and follow through with a job! Overall amazing program and great community!

I came into the program with a technical background and had previously done some software development - so please understand this review comes from that point of view.

I believe the data science immersive (DSI) is absolutely a worthwhile program if the... Read More fit is right. There simply is not enough time in three months to go from beginner-programmer to well-rounded data scientist. However, Galvanize does a great job at giving you as much academic load as one can handle, fantastic career services, and sufficient skills to be able to get an entry- level data science job.

I thought the staff was amazing and the content presented was usually the right balance of breadth and depth.

That said, the program caters to self-starters and problem solvers with a decent handle on Python. The students who seemed to have less success exhibited at least a couple of common traits: they struggled to write clean code quickly, they struggled with moving on before concepts were thoroughly understood or assignments were completed fully, they didn't enjoy collaborating with their peers, or they struggled with the math.

I think Galvanize tries to assess incoming students to make sure they are properly prepared and a good fit- but am not sure if this process couldn't be improved to ensure that the students in the class are the students who will get the most out of it.

If you are confident in your ability to solve problems in Python AND feel prepared to take on the math involved AND are willing to put in a lot of work both in and out of class AND are prepared to make peace with moving on from a topic before you are completely ready AND enjoy working and learning as part of a group- I am sure the program will give you a 5-star experience. The less of those things that are true, the less you will enjoy what Galvanize has to offer.

Note: review is based on my experience in the Data Science Immersive on the Denver-Platte campus.

If you have prior work experience, are self-motivated, and ready to work before/ during/ after the course, this program will be for you. Be advised this... Read More program is rigorous, but that comes with the territory of a 12-week course.

To those with no work experience, current undergraduate students, or those aspiring to enter the field of data science: I’d strongly suggest gaining some relevant experience before considering enrollment in the program. This includes business intelligence, data analysis, data engineering, databases, or software engineering, or some form of programming skills (Python & SQL being preferable). You'll be better prepared coming into the program, and more qualified than others coming out.

For the job search: be prepared to be on your own when searching for positions. Some of this pain can be attributed to having no prior work experience in data analysis, data engineering, or relevant fields. If you don’t have prior work experience, it’s more difficult for companies to consider you for a role. I do wish admissions screened for prior experience before admitting candidates, as this makes the student experience that much stronger.

Galvanize advertised SQL & Python at the top of their copy, and tested us weekly.

We had two lectures on SQL. "Passing" on the tests was 30%.

Instructors had "industry" experience, it just wasn't data science industry experience- they had engineering industry... Read More experience.

Galvanize advertised twice monthly industry networking events with local businesses. What we actually got (& they argued it was the same in a legal document) was study meetups with other job seekers.

Class was supposed to be 830-530 m-fri to get the (very) expensive hours we paid for. Most of the time it was 9-5, and they tried to short is another two full days until I pointed it out.

One of our instructors was completely new and regularly showed up not knowing what we were doing that day.

Both instructors only lectured, didn't ask questions or engage in any meaningful ways, despite the fact that our small class -5 - was perfect for interactive learning.

The quality of the program depends on the instructors. In my program, the instructors were all Galvanize alum and had less than 2 years of actual industry experience combined. This limited the experience to domains or topics you could gain depth in to... Read More only those the instructors were familiar with. For example, my instructors were not business people so it was rare to have discussions on how a predictive model or an evaluation metric would affect the bottom line of a business. Also, in many lectures, the examples and visualizations were directly copied and pasted from textbooks - which was troubling considering the cost of the program (~$17k for 11 weeks). The career services is also poor - it's on par with a typical college career workshop. A simple Google search for career advice and you'll find everything you need on transferrable skills, STAR method, and resume/cover letter review/LinkedIn profile review. Granted, I was committed then when I decided to transition into data science and I am committed now as I continue to learn on my own and look for a job. But these are just the facts. There's so much hype around technical bootcamps these days, and these programs are businesses that sell hard with their $95k salaries at a 90% placement rate. Make sure to do your research and model out your cost-benefit scenarios (including the opportunity cost of being unemployed for the duration of the program + 3-6 more months it may take to secure) before deciding if this program is the right one for you.

Every experience in your life will always be what you make of it. Your time at Galvanize will be no different. If you devote yourself to breathing in Data Science and Python you'll be incredibly prepared for most data scientist, analyst and your coding... Read More knowledge would be transferable to other IT related fields such as data engineer or full stack developer. Although this is not a course geared towards the later two fields, you can leverage the problem solving skills you'll develop in the program to tackle these challenges.

I have friends across different master data analytics / data science programs as Ivy league schools such as Columbia in NY. You will learn more in 13 weeks than my friends learned in a 1 year data science program. You'll be more confident in your programming, development, analytics, and possibly statistics knowledge than most of these graduates from masters programs. I graduated with a master degree in statistics and still learned a lot in the statistics portion because of code focused approach they use for it.

What you won't get, brand name recognition of Galvanize compared to Columbia or other top tier programs is no comparison at all even if your skills will be much farther ahead than peers at those masters programs. It will still be up to you to sell yourself, and prove to recruiters that you are ready for a professional life as a data scientist. The great thing is Arthur will be there to guide you through this process of teaching you how to sell yourself and meet with you weekly after and during the program to help you get your job.

Overall I'm glad I choose Galvanize.

I'll start with the good.

The lead instructor has a great mindset when it comes to focusing on the students and how they learn and grow individually. I dropped the program early since the second half just felt like a prison and all the work is self guided... Read More anyway, but career services seemed pretty good from the bit that I went through. Good advice on setting up LinkedIn and resume building, but maybe don't expect much more than that. Most of my gripes are from the curriculum and environment.

The curriculum and environment...

The curriculum is almost completely taught by videos and self-guided time. You will be in class for 60 hours a week (so that they retatin accreditation), if you're five minutes late at any point in the day, expect a third of an absence, three absences you will be removed from the program. You will be pulled out of class or asked to change locations several times a day, often right as your just making progress/settling in. The building presents a hip, techy workplace at first glance, but the more time you spend there it quickly turns into the lifeless building it is.

The amount of effort that goes into presenting that environment would be better used actually building it. Every office around the building has giant glass walls that you can expect to feel like a zoo exhibit. They put on regular workshops in the middle of the space so they can make sure you see them being techy, or entrepreneurial, or whatever their angle is. It's an exhibit more than an incubator or school.

Overall it's a very disruptive environment. Also, not sure if this one is me being jaded by prior experience, but only expect to really walk into a job using the exact tools they teach the curriculum on. Instructors will not be able to provide advice unless you adhere exactly to what they have used and is part of the curriculum. Engineering is honestly a misnomer for what they selling here.

Data structures are very loosely touched on and never in the course is the application of engineering principles really taught. Expect to be handed a project with some test cases and told to go have fun. Assignments and tests are done by bullet points or following a list of instructions. It doesn't inspire free thought, or how to solve a problem using engineering.

The first six weeks of the course is definitely where the majority of the course lies. It does a good job of familiarizing you with javascript, but the second half where you're learning how to build actual applications. You're handed a business document and a rendering of a website and told to go make it. It's easy to make something show on the screen through code, but the means in which you accomplish this is where the term engineer comes from, and this is what is not touched on a single time through the course.

My advice coming out of this.. if you need the in classroom setting, do the part time course. You're interaction is probably the same even if you're there full time. But at least you have you're own schedule and can have a less distracting environment. The price tag is high. If like online learning, do online, maybe look at some of the paid courses available elsewhere. You will get a lot more material for your dollar.

So the real question. What are you paying for at Galvanize?

The space (not great), the curriculum (has value early on, but definitely not 20k), the instructors (hardly any instruction or mentorship), career services (resume, linkedin, cover letter, interview advice, pay negotiations, overall valuable stuff, but again not 20k), oh, and the name Galvanize/Hack Reactor, which does carry value. So long as they don't tarnish it.

My experience at Hack Reactor @ Galvanize will be one that I will never forget. From the instructors to the course material, supporting staff members to the campus, every aspect of this program came together to offer a supportive, learning environvment... Read More essential to building the skills needed to code and become future developers.

On top of learning how to build apps, I've made life-long friendships/mentors, and developed soft skills that will help me in my journey. I am very grateful I was offered the opportunity to learn thru this program.

Summary: I quit my comfy job to do the Data Science Immersive (DSI) at Galvanize and I am so happy I did.

Background: Prior to the DSI I was a senior analyst (with an MS in epidemiology) for a research consulting firm doing quantitative research primarily... Read More in SAS. I had a strong foundation in study design and stats but never programmed in Python or had familiarity with big data tools or advanced machine learning algorithms. I thought being a quantitative researcher in this day and age without knowing Python was not good for my career so I started looking into programs. I went with Galvanize because it had the best reviews and a friend vouched for it. I saw this as more of an evolution than a career switch.

Pros:
- Purely doing the DSI allows you to focus solely on your own learning and devote yourself to applying for jobs. This is huge in itself. I never had the time or energy when working full-time. Interviewing these days, especially in tech, is a super involved process with multiple interviews, technical screens, and presentations.
- You get exposed to so many marketable tools (like AWS, Docker, Github, etc.) that you can add to your resume. Very important to get those keywords on the resume!
- You learn all of the important and relevant data science methods that are so cool like gradient boosting, neural nets and recommendation systems.
- You work on a capstone project that you can use on job talks. This is huge because it is sometimes hard to talk about a project from a previous employer because of IP or NDAs.
- You learn from you classmates and feel the buzz of being at Galvanize with the software engineering students. Everyone is working hard all day to try to better themselves. The atmosphere is motivating.
- Career services integration is pretty good although I felt like we could have been pushed more during the bootcamp.

Cons:
- It goes without saying but if the instructors aren’t good then the whole thing falls apart. Luckily the instructors I had in SF were great. Go to an open house and talk to previous students to get a feel.
- Everyone struggles but if you are weak in math, stats or coding you will definitely struggle and continue to do so if you aren’t putting in the time. It is ok to be weak in one area but if you are weak in all three then DSI may not be the best fit for you.
- This also goes without saying but the people that do the best already have some good experience. Probably because they are ambitious and quick learners, and also have some good stuff on their resume already.
- Some of the lectures were too fast to comprehend and some of the assignments were incredibly challenging or nonsensical (we have two assignments per day). Luckily the staff are constantly improving and updating the materials based on feedback. Galvanize was certainly better at responding to feedback than any of my college courses.
- The biggest con is probably the financial cost and the opportunity cost if you don’t find a well-paying job after the bootcamp. This is a calculated risk that you have to make for yourself. I suggest modeling it out. How much do you need to make and in what time frame? It is not unreasonable to be unemployed for 5 months after the bootcamp while you are looking for the right job.

Tips:
- This is not like college. You will be learning at least 10 hours a day non-stop, likely more if you are struggling with some topics. It’s not about studying for a test but always thinking about applying skills in real life and for a job interview. There is a lot of self-motivation and diligence needed to just plow through and not get bogged down.
- Be realistic: If you have no previous relevant experience it’ll be hard to land a data scientist job but I think the DSI might still be worth it since you’ll be well set up for an analyst role. My cohort had people with no experience to super skilled software engineers/math majors. What was great was that we were all looking to get something different out of our experiences and carved our own paths while helping each other. My suggestion is to think of a specific company and role you want and tailor your learning to landing that job.
- Schedule: The pre-course took me a month and a half to get through since I was still working full-time. Then interviews took two weeks. After the interviews there is some pre-course work that took a week to complete. I worked until the day before the program started so needless to say things were really busy for the two months before the bootcamp even started! Plan your schedule and timeline accordingly. I’d give yourself 4 months to do all of the pre-work (if you have no prior Python or stats experience), apply for any scholarships they have available, and get your life in order to go into the DSI head-on.

Overall: I had a great cohort and great instructors. I also think I had a good story and background and was strategic in my learning. All of this helped me land a job within 1.5 months of graduating. Three others in my cohort found jobs before me, a couple became teaching assistants, and the others are still actively looking two months out. There are so many factors that go into finding a job like previous experience, interview skills, role you are looking for (i.e. more senior). My suggestion is to just focus on yourself, be realistic and be diligent. If you do all of that then the DSI will be worth it.