An Updated Review of The Data Incubator Data Science Bootcamp

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A while ago, I wrote a review of The Data Incubator based on my experience in the program. Since then, it’s been the most common reason people reach out to me. I’ve had people reach out to tell me how the program went for them, to ask me questions about the program, or to ask advice. Since this happens so frequently, and my review is a bit out of date now, I figured I would write an updated version, taking into account what has changed (and what hasn’t) according to those who have been through the program after me. I've also written a collection of the most common questions I get asked about the program, with my answers.

Curriculum

The curriculum is the right level of breadth. A lot of topics are covered, but with enough depth that you get a reasonable introduction to them. The topics covered are pretty standard and there are no major omissions, though arguably it is a bit weak on statistics (it focuses much more on coding skills). It’s a good overview of the broad strokes of data science, which I think is the benefit of bootcamps like this.

Lectures

While the topics covered are good, the lectures themselves are lacking. Many of the lectures are taught remotely with very poor A/V equipment, making it difficult and annoying to interact with the instructor. The lecture quality varies a lot, but tends to not be very useful. I and many others tended to use lecture time for eating lunch so it wasn’t a total waste.

Assignments

The assignments are possibly the greatest strength of the program. While there are annoyances with the automated marking, the assignments have the great benefit of forcing you to get your hands dirty with each topic and get some experience. This, in my opinion, is the benefit of The Data Incubator over it’s main competitor, Insight, which essentially just gives a list of topics and resources and leaves it to the fellows to learn and get experience.

Coding exercises

Every morning in the program starts with coding exercises, programming problems that you might get as a technical exercise at a job interview. I found these extremely useful as they made me much more confident in Python. Opinions differ though, some people found them frustrating and discouraging. As these challenges are not unlike a lot of the ones you’ll see as the technical part of a job interview, I tend to think they are valuable.

Job hunt support

In general, I thought there was good written content and advice on how to interview and build a data science resume. However, the lectures on these topics were significantly less useful. The alumni network is very weak, giving few ‘ins’ and networking opportunities from the program. In comparison, Insight ends up being very good at this, and fellows in that program end up with a strong network and community after completing the program. The hiring partners, the companies that hire from the bootcamp, are fewer than you would expect from the slick advertising, and are much less diverse than you would think (fewer industries and locations). This is a major issue, as some people from my cohort dropped out after seeing how few opportunities there were in the area they wanted to work in.

Organizational culture

I’ve heard mixed opinions on this, but the majority of people I’ve talked to agree that the culture of the organization feels a bit off. The demeanor of some career advisement staff (to name names, Alyssa Thomas, director of program experience and career advising) can be very condescending. In addition to this, fellows are punished for not completing their assignments by being cut off from the portal to interact with employers, and this punishment is wielded pretty arbitrarily to punish fellow behavior they don’t like (such as turning down a bad job offer). A number of other factors make the program feel much less welcoming an environment, which is too bad. I would love to love The Data Incubator, but instead was left with mixed feelings about it, largely because the culture led to a mixed experience.

Final thoughts

Overall the program is good for what it is. The curriculum and assignments are solid, and I learned a lot from the program as well as the other fellows. The program has problems, but all programs do. I wouldn’t full-heartedly endorse the program, but I think it is a good way to go for a lot of people looking to break into data science, especially those coming from academia.





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