Overall, I had a fantastic experience with Flatiron School. The program enabled me to get a data science job that ticked all my boxes. I was able to make a dramatic career transition (during a pandemic, no less!) from academia/non-profits to tech thanks... Read More
Background: When I was applying to bootcamps, I already had a PhD (in a humanities discipline) and lots of data science and programming tutorials under my belt. I chose Flatiron School because the curriculum looked good, the program was only 5 months when done full-time, and they offered an income share agreement as a payment option.
Getting in: First I had a call with a representative to assess my interest and experience in data science. Then I got access to some preparatory materials that included basic Python and a little bit of calculus. Once these were done, I had a technical interview with a Flatiron School instructor, consisting of a few basic programming questions and a conversation about some math/calculus concepts. The preparatory materials were enough to help me be ready for this interview, but the real point of the interview is to assess whether you can learn quickly and take feedback/correction well. After this, I was accepted to the program and chose my start date.
The program: For the duration of the program, I had one instructor, an "educational coach" (to help with motivation, study skills, etc.) and a cohort of fellow bootcampers. We spent four hours per week in live lecture/office hours, and I had two half-hour one-on-ones with my instructor each week, too. My cohort had its own Slack channel where we could ask the instructor or each other questions and do daily standups. Here's a list of topics we covered (not exhaustive):
General Python programming, with particular focus on NumPy, Pandas, and Matplotlib
Git, GitHub, and principles of version control
Basics of data visualization
Regression (linear, logistic, multiple linear, polynomial, etc.)
SQL and general principles of database design
Basics of object-oriented programming
APIs, web scraping, and working with JSON files
A broad survey of statistical topics, including combinatorics, permutations, distributions, central limit theorem, hypothesis testing, and Bayesian stats
Time series analysis
Basics of linear algebra
Survey of machine learning algorithms and techniques, including k nearest neighbors, decision trees, random forests and other ensemble methods, support vector machines, PCA, k means, and recommendation systems
A little introduction to Spark via PySpark
Basics of graph theory/network analysis
Basics of NLP
Survey of deep learning topics, including RNNs, CNNs, and transfer learning
A little intro to AWS and how to deploy a machine learning algorithm into production.
Near the end of the program, I also got access to a career prep curriculum, which contained readings and exercises relating to résumés, LinkedIn, networking, building a professional web presence, etc. Flatiron School recently added a post-bootcamp curriculum of extra stuff to study while job-searching, including more work with SQL, and intro to R, and some guidelines for making your projects look better on GitHub. I'm really glad they created these additional resources, since these are things I went out and studied on my own because they were important for my job search. Flatiron is constantly updating the curriculum, and students are welcome to submit suggestions or corrections to be addressed by the curriculum team.
An average day of bootcamp involved a lot of independent reading and work on the labs provided in the curriculum, interrupted by a lecture/group meeting with instructor and classmates to go over the material assigned for that day. This format was great for me because I learn better when I can focus on something on my own for a while and then have a chance to ask my questions and hear other people's questions, too. Each day's meeting covered a certain chunk of the curriculum, but there was a lot of flexibility to work ahead or take more time on something if I wanted. Although you could always spend more time on things, I found it was possible to complete each week's material to my own satisfaction in 40-45 hours on average.
Overall, the curriculum prepared me well for my job search. The projects I built during bootcamp were of a high enough quality that I could present them during job interviews. The capstone project is totally open-ended, so you can design it to show off skills relevant to the types of jobs that appeal to you. For each project, I had to do both a non-technical presentation and a code review with my instructor, and these experiences were extremely helpful once I started looking for a job. This is definitely one of the reasons to do a bootcamp rather than just studying on one's own. If someone I knew were looking for a bootcamp today, I would tell them to ask whether a program includes this kind of project work, since it's the best way to prepare for/show that you're prepared for real data science work.
Career services: My experience with career services at Flatiron definitely made the program worth the cost. As soon as I graduated, I started working with a career coach who helped me with my résumé, mock interviews, web presence, and general job search strategy. My coach was amazing, especially at keeping me motivated when the pandemic brought my job search to a screeching halt. It was really helpful to have someone to talk to each week about my job search, and she was super responsive to any questions I had about how to respond to e-mails, networking tactics, etc.
Highlights: The best parts of the bootcamp for me were my instructor, my career coach, and the projects I built.
Improvements: I would have liked to see a little more space in the curriculum given to ethics, maybe some case studies about practical applications of data ethics. There was a brief overview of data ethics in theory ("Hey, you should only make ethical use of people's personal information! There is bias in AI!"), but since this is an issue that affects all aspects of data science, it would be good to give it a little more weight.