Springboard

I enrolled in Springboard's UX Career Track in April 2019. I was able to finish in eight months and still get the job guarantee. What I liked the most was the one on one weekly mentor calls and the community on Slack. With those tools I was able to have any of my questions answered. Springboard also really prepares you to work in the industry, since in order to be eligible for the job guarantee, you need to pass a behavioral mock interview and a portfolio walkthrough, which are the two types of interviews you have to go through when applying for UX jobs. I am now excited to start my job search and look forward to keeping in touch with everyone on the Slack community.

I am rewriting my review as it has been 6 months and still no job. The problem is that the career coaches are always booked up and you cant book a time that works for you. I booked 2 career coach calls within a 2 week period and canceled one of them because I had one booked already. Sometimes the career coach has to reschedule, thats why I did 2 schedules. Not a month later the school emails me that I am no longer eligble for the money back guarantee because I canceled one of my 2 calls. The problem with this Bootcamp school is they dont have enough resources for you and your set up for failure. Also you should try and stick to one career coach as multiple people have different ideas and it messes you up. Like, one said to do this on your resume and the other said to do this. Stick with one that you like. 

This course gives an extremely broad foundation for getting into ML/AI/DS careers. The weekly mentoring is perhaps the best part, and having weekly meetings with an experienced member in the field pays huge dividends, especially as you find yourself changing jobs or feeling like some impostor to the field.
You will become extremely knowledge about the math and proof of concept stage to getting machine learning-based projects off the ground. I think this is helpful, but could improve in many ways for 2020.
1. More emphasis on deploying systems and system design. The curriculum delves very little into cloud services (AWS, GCP, Azure), preferring a more open-source based model. I think this is good in general, but machine learning engineers are inevitably going to be using one of these services in their careers. The course gives you credits for AWS, but little attention is paid in the curriculum to its use cases. Given all the time spent in the course, reading other paid material like "Grokking the System Design Interview" shouldn't be necessary if the curriculum is better formed. 
2. Techniques other than machine learning. It's kind of ironic to ask a course like this to do something other than ML, but knowing when ML is useful and when other techniques are appropriate (from heuristics to Deep RL) does not really get much attention.
3. More work on data collection. It would be much more helpful if the course explained more further up the pipeline on how to collect, ETL, and store data to make ML possible. It might be an old school way of thinking, but many companies have older data  systems that need to be understood in order to get to the interesting ML portions.

This course covers a lot of info in a very detailed manner. You get a wide variety of topics, and you will get a nitty-gritty view of it, too. I appreciate the mentor they give you to get you through it. I had a very helpful one that I met with every week and was very good about answering my questions. You even get some career help, and you can't graduate from the course unless you do some practice interviews. This is were my negative comes in. It was very difficult to even schedule these interviews (and other career advisor meetings that are separate from the mentor ones). The slots were few and filled very quickly. For one practice interview, the person that I was supposed to do it with didn't even show up, so I had to reschedule that one. That was frustrating to me and a mar on an otherwise good course. Other than that, I would recommend it.

Springboard did a really good job in putting together all the coursework and materials together. The materials and topics were up to date and quite relevant to the latest trends in machine learning. Even though the coursework and videos might have come from different sources and might seem unstructured, the content was useful and pointed you in the right direction in case you wanted to delve into that subject using resources you found on your own. Hands-on mini-projects for certain topics were crucial in helping gain a practical understanding of the subject matter. The one-on-one weekly meetings with the mentor were an invaluable experience.

The coursework and videos will take up a lot of your time and sometimes it is hard staying on track, especially while trying to work on the capstone project in parallel.

Overall, it was well worth the time and effort!

After completing the program and securing a job, I wanted to come back and review Springboard again after experiencing their job search program.

In terms of curriculum, I overall enjoyed Springboard’s curriculum and learned a lot. The Harvard lectures are very informative and good for understanding machine learning theory. I do feel that their SQL curriculum is not very strong and could be improved, my SQL skills were poor when I finished the bootcamp. I also feel like there should be more information about basic Python coding - I had coding interviews that I was not prepared for.

The two capstone projects are definitely the strongest aspect of Springboard and the aspect that got me the furthest in the interview process. Having projects to talk about really helped me in my initial interviews. My mentor who helped me with my projects was really nice and helpful!

One thing that is a little sketchy that you should be aware of before you join: as part of the job guarantee, you are required to pass several mock interviews, and you only get two chances to pass each one. This did not impact me, but it is not something that is disclosed before you start the bootcamp.

I was pretty satisfied with Springboard’s curriculum, but what I was completely unprepared for was the competitiveness of the job market. It’s tough to get interviews with no experience, and when you do get interviews, you have to be perfect or they will just find someone more qualified than you. I struggled with technical interviews, particularly coding interviews and business interviews (business is not my background). The mentors that they have to help you prepare for technical interviews are not super helpful.

The job guarantee makes it so that they have a reason to help you get a job, but I honestly didn’t find them that helpful - my career coach was nice and was good emotional support but didn’t help me get referrals and didn’t help me with technical interviews. Part of the issue is that I feel like there’s only so much they can do - the data science interview process is really like the Wild West, every company does the interview process differently and it’s tough to prepare. It felt like every company was looking for something different and it was impossible to be prepared for everything because there is just so much to know.

About four months into my job search process the pandemic hit, and Springboard made a couple of major improvements to the job search program. The most important improvement they made is they created a Placements team that spend their time specifically looking for referrals and “ins” at companies for you. This was extremely helpful for me, and though the job I accepted was one that I found on my own and got through networking on my own, I got a few good referrals from this team.

The second major improvement that they made was getting new mentors to help you with technical interviews. I got a job soon after I started speaking with my mentor, but I felt like he was very good and better than the mentor I had worked with in the past.

Overall, a mixed experience with Springboard. I appreciate that they have the job guarantee program because they do try to help you, but I wish there had been more help sooner and not just because of the pandemic.

Pros:
Nice and organized curriculum, with mini hands-on projects.
Continuous help from the mentor (real Data Scientist working in industry)
Remote and part-time. So you can maintain your present job while working on this.
Opportunity to work on two capstone projects with the help and support from your mentor.
Career coaches are there to help with your resume/LinkedIn profile, networking, and soft skills.
Mock interviews to prepare.

Cons:
No in house lectures/ they are tied up with Data camp/Khan Academy/ LinkedIn learning mostly for all lectures.
Everything is remote, so you might lose motivation sometimes, where in-person boot camps are like a real school.
Not all the mentors are equally caring and helpful.

They are one of the crooked so-called educational institutions. I took their course which ensures money back guarantee if I am unable to secure a job within a time frame. I did everything according to their guideline, and now, they are making false accusations to grab my money. I will not let this organization cheat students like this, and I will take every legal actions against them. All the good reviews on internet are simply generated by their robots. They even pay some employers, so that the employers can do fake advertisement of recruiting their students after the completion of the Springboard career-track program. This is such a deceptive and fishy institution ripping off many innocent students in North America. I would never recommend them.

A little background: I'm currently about 25% through Springboard's Data Science Career Track program. My professional background is in higher education. I recently completed a PhD in philosophy and have experience teaching undergraduate philosophy courses. I have minimal background in math beyond the high school level, but my philosophy degrees have prepared me for the sort of technical and critical thinking skills used in data science. I'm doing the Springboard program to make a career change. I completed most of Springboard's Data Science Prep Course, which I found helpful, before being accepted into the Career Track program.
Review: Overall, my experience in the Career Track program has been pretty good so far. My impression is that the quality of one's experience with the program depends largely on the quality of one's mentor. My mentor was initially pretty poor, and I had a difficult time understanding his accent. But I reached out to Springboard early in the program, and they helped reassign me to a new mentor. Thankfully, my new mentor is excellent and has dramatically improved my experience. I'm in a study group with other students, and I've gotten the impression that their mentors are so-so. I have learned a lot so far in the program, and I've been especially happy with Springboard's "customer support" (very friendly and helpful) and career support (they get you started on job stuff early on in the program). A fair portion of the data science curriculum (so far) has consisted of Datacamp exercises. Springboard partners with Datacamp, and students get free access to Datacamp courses. Honestly, I haven't been that impressed with the Datacamp courses. They are helpful for gaining superficial exposure to tools and skills used in data science, but they don't cultivate a deep understanding. I will say that the time estimates Springboard gives (completion in 6 months, working 15-20 hrs a week) are pretty unrealistic. Most of my study group (including me) is working on the course full time, and we are all on track to finish in 5 months (at the earliest), and probably more like 6 months. Unless you already have significant data science experience, don't expect to finish the program in under 5 months, and possibly even under 6 months.
Overall, given my experience so far, I would probably recommend (pretty highly) Springboard's program to people (depending on their circumstances). I'm happy to discuss my experience with people if anyone would like to know more, especially people with an academic background in the humanities. You can find me on LinkedIn and around the web if you look (e.g. philpeople.org). Feel free to reach out.