Metis Flex: A New Way to Bootcamp
As I was contemplating how I would enter the data science field, I read countless blogs and articles about the experiences of those who chose to go the bootcamp route. Some were entertaining, others draining, and most were somewhat informative. It’s tough to figure out whether a data science bootcamp is going to be the right choice for you and it boiled down, for me anyway, to three very important considerations:
- Value (which is, arguably, the product of 1 and 2)
I am just finishing up Week 3 of my Metis Flex Bootcamp in Machine Learning, which will last seven months, and I’ll do my best to address these three considerations at this time point (and future ones). Yes, I said seven.
The Metis Flex Bootcamp, first of its kind, will last seven months, starting on July 26, 2021 and ending on February 21st, 2022. The curriculum and content that would usually be taught within a 14 week period has been diffused into 28, and in my opinion, is all the better for it.
Each incremental step of learning data science is held within its own module, with the first one, EDA, lasting 3.5 weeks. Each module is broken down into weekly segments with lessons, assessments, assignments, and project milestones. You are encouraged to follow the timeline, but are welcome to view the lessons at your own pace. Assignments, assessments, and project milestones, however, are due at the end of every week.
Lessons are taught in 5ish minute increments through recorded video with slide presentations, with optional Jupyter notebook worksheets for practice at every step and multiple choice questions to test for integration.
Content is accessed through Canvas online, which you’ll be familiar with if you’ve taken any online classes in the last decade, and is quite intuitive.
As mentioned, I think the longer timeframe suits this material best, though I am speaking as someone who has never taken a full-time bootcamp. However, blogposts I have read in the past have all mentioned the desire to have more time with the material and projects, and I feel I have a spacious amount of time to sit with the lessons, do more research on topics I don’t quite grasp yet, and work, re-work, and work again on my EDA project.
While I do have a desire to be done with the coursework and start my career in data science now, I am also thoroughly enjoying the process of learning how to code hands-on, and know this time is invaluable to my future aspirations.
I’m a lucky little guinea pig.
I started my journey in data science by a not inconsequential amount of research, hand wrangling, and signing up and dropping Edx Nano courses. I read all about how you can learn all this material on your own and just need a stiff enough backbone to get through it, and while I know, ultimately, I could do this myself, there’s nothing like having one-on-one mentoring, a community of students to commiserate with and add on LinkedIn, and weekly encouragement talks from past alumni.
My big hangup, of course, was how expensive these bootcamp programs can be.
Luckily I am in the first class of the first flex bootcamp Metis has ever run, and for that reason I got an introductory (guinea pig) rate for signing up.
If I ask myself right now whether I would sign up if I had to pay the full price (minus my scholarship as a Queer Woman of Color student) I would say it would be a tougher choice between Flex and Full-time bootcamp, though I would definitely sign up for one or the other.
My comfortability with Python, Numpy, Pandas, Seaborn, Jupyter Notebooks, and SQL has shot up immensely in the past three weeks of the program, and I have discovered a voracious appetite for coding that had been lying dormant beneath years of believing it the domain of everyone who is not like me.
Is value = time * money or money / time? Perhaps its the sqrt(time*money). I’ve put a lot of time and money into this program already, but the value it has returned on my investment has already surpassed my expectations for growth.
I didn’t think coding was for me.
I remember now, back in middle school, being taught how to code image files using ASCII and saving my work onto giant five inch floppies 💾 that we could quickly swap out in order to copy each others’ code. (yes, I am that old)
But I also remember going through one of the worst moments in my life as and 8th grader, when both teacher and classmates in Science 101 laughed as someone called me a “dyke” while we reviewed the geology section for the final exam.
I wrote off science. Just as I had written off math when I realized I did not (and do not) have a head for memorization.
I went into graduate school with a curiosity for the human mind and left with the discovery of a latent talent: statistical analysis.
Oh how I love to look at the data and understand when someone’s trying to pull my leg with carefully chosen values that are not representative of the whole.
Now as I enter the Metis program, that love has only been fostered, supported, and continues to grow through one-on-one mentoring with data scientists who quickly redirect a lost presumption back into the fold, active and engaged peers who challenge and support each others’ projects, and a team of support that reminds you at every step that everyone thinks they’re the imposter- until they’re not.
Yes, the parameters for value have been met.
Originally published at https://celiasagastume.com.