Review of Springboard's Data Science Career Track

Recently, I enrolled in the Data Science Career Track at Springboard in order to publish a review of their platform and compare it to other online courses and bootcamps.

Full disclosure: This is a completely honest review of Springboard's Data Science Career Track, but please note that signing up with Springboard through links on this page will result in a commission that helps support LearnDataSci.

Overall, I've been impressed with the people involved and the organization of their curriculum. The mentor I was paired with was very knowledgeable and had a great deal of experience in the industry. If I was learning data science to start a career all over again, Springboard would have essentially everything required.

One thing that needs to be pointed out right away is that Springboard does not have custom in-house content (other than the career services/advice). The data science content is curated mostly from free online sources and then structured in a way that produces the best results for students. The main value of the career track is in their mentorship, custom assignments, capstone projects for your portfolio, career services, and job placement.

Also, Springboard's Career Track isn't like Coursera, edX or other MOOC courses in that you will actually need to submit an application and complete challenges to prove your understanding of some prerequisites before being accepted. So the first thing we should do is take a look the prerequisites you'll need before applying.

Experience and knowledge of programming and statistics are necessary to get into the DS Career Track. There will be a challenge for each before being accepted into the program, so if you're looking for something more fundamental with no prerequisites, they also have a beginner-friendly Data Science Career Track Prep course. This beginner course helps students master the foundational Python programming and stats skills needed to get started in data science, as well as preparing students to go on and take the Career Track.

From reviewing a lot of the curriculum, I've found that you really should know the basics of Python programming so you're not immediately lost in the material. The career track jumps straight into pandas, matplotlib, and the rest of the data science stack, so getting confused on Python basics while learning these libraries at the same time is going to be strenuous.

One of the best ways I've found for getting up to speed with the Python required for data science is to use the interactive environment at Dataquest. If reading is more your thing, check out the free Python books in our free book list.

Other than that, college-level statistics and linear algebra fundamentals are also necessary. The application challenges will obviously weed out a lot of applicants that lack the knowledge, but from the curriculum, there's really no time spent on how linear algebra works like in Week 1 of Andrew Ng's Machine Learning course on Coursera (link).

The application process is pretty simple overall. The general flow is:

1. Submit an application (10-15 minutes)
2. Pass the challenge (~3 hours)
3. If you passed the challenge, pick a date and payment plan to reserve your spot
4. Join and start learning

Now let's jump into what I found to be the overall pros and cons of the program.

The huge difference between Springboard and a data science MOOC, like Coursera or edX, is that Springboard offers private mentor calls with a data scientist each week. In those calls, you can ask them anything, such as personalized feedback for your recently submitted assignments and capstones, what it's like being a data scientist at their company, or anything you're stuck on in your learning path.

Although the cost is a lot higher than a MOOC, it's actually lower than every other online and offline data science bootcamp I could find (as of 2019). Compared to a university, the entire career track is priced very close to a single college course. And like college, Springboard offers deferred tuition.

Below is a breakdown of what Springboard's Data Science Career Track actually offers in terms of the curriculum structure, assignments and capstones, mentorship, and career services.

As stated at the beginning of this review, instead of creating custom content like many other platforms, Springboard's data scientists curated a curriculum from already well-made lectures and tutorials. These mostly come from free online sources, such as the CS109 Data Science Harvard course, PyCon talks, and Khan Academy. Additionally, the learning path that's presented to you is always changing and evolving based on feedback from students and mentors, new course offerings, and other factors.

Here's a breakdown of the curriculum and modules they've structured:

The statistics and machine learning coverage are great. They've included lectures to cover the following topics:

There's certainly a lot of material to cover, and they have a vested interest in making sure you understand all of it.

Since the largest part of that understanding will come from the completion of and feedback on assignments and capstones, let's jump to that next.

The assignments and capstones are the most important parts of actually learning data science since they let you solidify your understanding of the material. Unlike going at it on your own, in Springboard's career track you'll have mentors to answer any questions about your assignments each week.

The ability to actually speak to a data scientist via video chat about your issues is one of the biggest bonuses over open courses. MOOCs don't have the resources to speak with you 1-on-1, and instead rely on a written forum to answer your questions.

Essentially all of the modules have custom assignments (usually iPython notebooks) that explain more about the topic and walk you through the Python implementation. This is where you start getting your hands dirty with what you've learned in the lectures.

Aside from the module assignments, there are two larger capstone projects that encompass everything you've learned up until that point. Each capstone is designed to challenge you on all parts of the data science pipeline and is what your data science portfolio will eventually be comprised of.

According to Springboard, when employers were interviewed, they stated they wished applicants had portfolios and more experience with real datasets. It's Springboard's ultimate goal to have their students hired, so Springboard has created its platform in a way that gives employers exactly what they want to see in future applicants.

As of 2020, Springboard is one of two online data science platforms that offers a job placement guarantee (see also Thinkful). I expect there will be more platforms (universities hopefully) that'll offer a similar guarantee to stay competitive.

To be eligible for the job guarantee, you need to:

• Be authorized to work in the US within 1 year following graduation from Springboard
• Be proficient in both written and spoken English
• Be at least 18 years old
• Have a Bachelors degree from any university in any major
• Be willing to live and work in one of the eleven US metro areas
• Be actively searching for a job and committed to your professional success

I think the biggest key point here, for me at least, is the “willing to live and work in one of eleven US metro areas”, namely Atlanta, Austin, Boston, Chicago, Houston, Los Angeles, New York City, San Diego, San Francisco Bay Area, Seattle, or Washington D.C.

Personally, I don't think I would actually want to move from where I am now, so that would be the biggest issue for me.

Rajiv Shah Data Scientist and AI Researcher at DataRobot

My mentor was Rajiv, a data scientist at DataRobot in Illinois. Rajiv has Bachelor's in Engineering, a Ph.D. in Communications, and was a professor at Illinois State University. He started learning data science before MOOCs were a thing, so he spent a lot of time learning from books, websites, and doing personal projects.

Rajiv got his first data scientist position at Statefarm, where he built a data science team to about 25 people, before joining Caterpillar where he built another team. His data science team building has given him a ton of experience on what to look for in new hires, and he's a wealth of knowledge on what to learn and do to become an excellent data scientist.

Speaking with Rajiv was super interesting, and you can tell he's extremely passionate about teaching what he's learned over the years to help students become data scientists.

Rajiv's biggest tip for aspiring data scientists is to make sure you're comfortable programming, to be used to thinking about numbers, and to get your hands dirty with projects, preferably ones that haven't been done before. He couldn't stress enough about the importance of doing real projects, preferably ones that haven't been done before, as soon as possible.

Learn more about Rajiv via his LinkedIn or YouTube channel.

The one section Springboard does have in-house content for is in regards to career advice and personalized strategies for your job search.

In addition to that content, you also get a personal career coach that'll help you with everything you need to get a job in the data science field. Since Springboard is risking a several thousand dollar refund from you, it's not surprising they put a ton of resources towards getting you into a data science career.

Overall, there's a lot of value in their career advice and there's a large accountability aspect to it as well. Since you'll have to meet with your career coach and mentors each week, you'll be expected to report on your portfolio, job search, and interview progress.

Yes, and Springboard has spent a lot of time figuring out what it is that makes great data science candidates. Raj Bandyopadhyay, Director of Data Science Education at Springboard, has spent a great deal of time interviewing data science candidates for his own team and has found most applicants lack in two areas:

1. Experience with real-world datasets
2. Communication skills

It's Raj's goal to provide solutions to those two problems through Springboard's capstones, which utilize real-world data and result in solid projects, as well as mentorship, which strengthens communication skills as students need to explain their work.

It depends.

The curriculum structure, custom assignments, mentorship, and personalized career coaching make Springboard a great fully online platform for getting ready for and becoming a data scientist, but the price tag is a major inhibitor.

If you're one to get major value from personal feedback, guidance on topics you don't understand, projects you're working on, and eventually getting through interviews, then Springboard is a good option. They have a job placement guarantee, and a 7-day money-back guarantee if you don't like the platform for any reason. If your new job in the data science field would earn more than the cost of the program, then it's essentially a risk-free investment.

Alternatively, you could achieve close to the same outcome by:

• Working through Dataquest's material
• Read through top books and take Data Science MOOCs on topics you need to learn more about
• and most importantly start working on projects you're interested in to build a portfolio

Otherwise, if you need a full-service, flexible online platform, and desire the extra 1-on-1 guidance and personalization, then Springboard's Data Science Career Track is a great choice.