This is the third post in a series on my experiences attending the Metis immersive Data Science course in New York City. My previous posts covered the application process and pre-bootcamp work through Week 1. While I cannot guarantee anyone else’s bootcamp will be the same as mine, I hope you find these posts as useful as I found other blogs when I was researching data science programs.
Each week of bootcamp brings with it a host of new models, evaluation metrics, and visualization tools but maintains the consistent structure established in Week 1: pair programming followed by lectures and independent project time. There are four individual projects in addition to the first week’s group effort, each one concentrating on particular skills and analyses. Lectures are timed to introduce you to the knowledge and tools you’ll need to immediately apply to project work. Weekly seminars from the careers department add some variety to the normal lecture schedule.
| A Typical Day at Metis | |
|---|---|
| 9 am | Pair Programming |
| 10:30 am | Instructor Lecture |
| 12 pm | Lunch |
| 1 pm | Lecture or Careers Seminar |
| 3 pm | Project Work |
| 5 pm | End of Day |
Pair Programming
The typical day at Metis starts at 9am with a pair programming challenge focused on coding skills, a logic problem, or breaking down a machine learning algorithm. Problems can seem impossible, very easy, or anywhere in between, and the variety in the challenges means you’ll likely vacillate between feeling really smart and really stumped. Metis makes a big deal of the diversity of student backgrounds, which I certainly found to be true of my cohort. Pair problems cover enough different topics that you’re unlikley to be an expert in all of them, meaning you may encounter some you can’t solve alone. They aren’t checked or graded, so there’s no real pressure to solve them, but they’re certainly worth spending time on. Many of the problems are similar to (if not identical to) questions you’ll encounter on take-home tests and in whiteboard challenges during the job interview process.
Lectures
The number and length of lectures varies in a given day but you can expect at least one between pair programming and the noon lunch break. The standard lecture consists of an instructor walking the class through a Jupyter Notebook or slide deck to introduce or further explain a particular concept or technology. Examples from Week 2 include a Powerpoint presentation on the assumptions of linear regression and a notebook showing how to train linear regression models using the Scikit-learn and Statsmodels libraries. Lectures coincide with the focus of each project, so Weeks 2 and 3 are heavy on linear regression and data visualization to prepare for the regression modeling project due at the end of Week 3. Here’s a quick summary of lecture and project topics, but keep in mind the current curriculum may be different:
| Weeks | Topics | Project Duration (in weeks) |
|---|---|---|
| 1 | Exploratory Data Analysis, Git, Pandas | 1 |
| 2-3 | Linear Regression, Web Scraping | 2 |
| 4-6 | Classification Algorithms, Data Visualization, SQL, AWS | 2.5 |
| 6-8 | Natural Language Processing, Clustering Algorithms, NoSQL | 2.5 |
| 9-12 | Passion Project, Big Data, Neural Networks | 4 |
Lectures tend to be front-loaded within each project cycle and the course as a whole in order to quickly provide you the knowledge necessary to complete each project. Towards the end of the course many lectures are optional as they may not apply directly to your chosen final project. Information will likely come at you too quickly for you to absorb it all, let alone all of the provided supplemental lessons. A hurdle many of my classmates and I had to overcome early on in bootcamp was abandoning the idea of ‘doing it all.’ There isn’t enough time to dig deep into every concept introduced by the instructors so eventually everyone has to pick and choose on what they spend their time and effort. Prioritizing the specific topics and skills you want to learn is essential to making the intense coursework manageable. For every data science skill you rigorously study and apply you’ll likely have to settle for cursory knowledge of other topics, which is especially true for project work.
Projects
Everything at Metis revolves around the five projects at the core of the program, and for good reason – that work will eventually make up the portfolio you’ll use to get your first data science job. I’ve already written an in-depth post about the first EDA project; the remaining four projects cover regression, classification, natural language processing, and any topic, covered or uncovered in class, that you want to explore further. Students deliver 4-5 minute presentations on their projects to complete each assignment, with earlier work serving as practice for the final presentations. Although most projects have specific requirements, students choose their own topics and acquire their own data, which leads to a broad diversity of work. Of course, coming up with five ideas you like for which you can find sufficient data is easier said than done. Since you can only afford so much time for ideation, I recommend a strategy of giving yourself a hard cutoff day for when you’ll just commit to a project from your list of ideas. I was fortunate enough to find topics I liked for three of my individual projects, and those were a pleasure to work on; putting in extra hours is always easier if you have genuine passion for what you’re researching. The one solo project for which I failed to find a great topic is easily my least favorite, but I’m still happy with the results. After using up my allotted ideation time I still didn’t like any of my project proposals, so I ended up picking the one I disliked the least. I could have burned a few more days of work time trying to come up with a better idea, but instead was able to comfortably deliver a finished project and learn quite a bit in the process.
Since the Metis curriculum is available online and may change in the future, I won’t spend many words rehashing it. Instead I’ll quickly describe each project, link to what I did for the assignment, and provide a bit of insight into the particular challenges associated with it. The first project is the only one of the five that is a mandated collaborative effort, and you can read more about it in this blog post. My second project at Metis required us to scrape our own data and use it to fit a linear regression model. This project is the first real gauge of skill and understanding amongst the class, which can make presentation day intimidating. The early weeks of the course heavily favor students who enter the program with a lot of coding experience, and if that’s not you it can be easy to feel behind. As both a student and teaching assistant, I’ve seen a number of novice programmers struggle through the second project only to show marked improvement on the next one. For our third project, my class was tasked with utilizing a cloud computing service (AWS, google cloud, etc) to host a large amount of tabular data in a SQL database and then using it to fit a classification model. Whereas the regression project offers limited variety in applicable algorithms and software libraries, the classification project starts to open the data science floodgates. I really enjoyed the increase in creativity and diversity of topics and approaches in student presentations from projects two to three, and that trend continued steadily through the rest of the course. The fourth project is very open-ended and focuses on Natural Language Processing (NLP for short). The two previous solo projects are both exercises in supervised learning, or training models using actual values you want them to accurately predict. The NLP project marks students’ first foray into unsupervised learning, meaning output consists of things like document groupings and word relationships instead of a set of predictions. This assignment can seem directionless and the results difficult to interpret, which can be difficult for students who are used to clear goals and structure. The minimal requirements and sundry applicable techniques and technologies make the NLP presentations quite diverse. Students at this stage also tend to start applying more advanced visualizations using software like Tableau and deploying models in app prototypes with Flask. Of course, the cornerstone of most Metis portfolios is the final project, which is what students present to a crowd of employers and alumni on Career Day. This project tends to be more ambitious and polished than its predecessors because students have amassed more knowledge by the end of the course and they have a full four weeks to work on it instead of two. Topics and technology used tend to be more diverse than in any other project, but many students trend towards training neural networks since they are introduced in the final weeks of the course. If you’re interested in a software library or algorithm not covered in bootcamp, the final project is an ideal opportunity to learn and apply it. Career Day presentations are pretty much like those for any other project except there’s an unfamiliar audience and students spend more time practicing and fine-tuning their presentations beforehand. Showing your work to a group of people interested in hiring data scientists can be intimidating but it’s important to remember that while Career Day is a great opportunity to connect with employers, it’s not your only route to a job. Ultimately the event is a platform to show off what you’ve learned and to celebrate that progress with your classmates. I’ll write more about the job hunting process coming out of bootcamp in a future post.
Career Seminars
Your most important interactions with the Metis careers department will likely occur during your post-graduation job search, but advisors also check in regularly during the bootcamp. Various seminars aim to deliver tips on effective networking, optimizing LinkedIn profiles, and writing resumes to attract data science recruiters. Working data scientist guest speakers describe their professional responsibilities and experiences navigating the field. Some even give insights into what recruiters and hiring managers are looking for out of new hires at their companies. Much of the information provided by Metis career advisors is useful even if you are a seasoned job seeker, but it can often come at inopportune times, when students are focused on finishing projects. It can be easy to ignore careers seminars in lieu of more pressing concerns, but paying attention and taking notes is still worth your while, since only some of the presentation materials are available online. Once you leave bootcamp for full-time job hunting, you’ll appreciate all the leads and guidance you’ve collected.
Closing Thoughts
The Metis Data Science bootcamp was exactly what I hoped it would be: an intense, collaborative learning experience designed to push you to create the best work you can in a short period of time. It was well worth the cost of tuition ($17,000 when I attended) and helped me get hired as a data scientist within three months of completing the course. I enjoyed learning from knowledgable instructors and teaching assistants as well as fellow classmates from my close-knit cohort. Above all I appreciated how professional Metis was at every step of my bootcamp experience. Having said all that, I don’t think Metis, or bootcamps in general, are right for everyone. I was able to make the most of the program due in large part to my financial stability and lack of competing time commitments going into it. You can certainly succeed with a less advantageous situation, but the class will be even more of a challenge. The trite adage ‘you get out what you put in’ also applies to Metis, making time management and focus paramount if you want to get the most out of the short, fast-paced curriculum. I recommend visiting Metis and other bootcamps for a private tour or open house event to get a better feel for how you might fit in there. I also found the personal experiences detailed in alumni blogs very helpful in deciding on the bootcamp route; I hope that you get as much out of my posts as I did reading those of others.
TL;DR
- You won’t have time to learn everything, so you’ll want to prioritize what you’re most interested in learning
- Don’t spend so much time coming up with the perfect project idea that you don’t have enough time to execute it
- Students with prior programming experience tend to have an easier time early in the program, but the rest of the class catches up fairly quickly
- Career Day is a good opportunity, but not your only route to a data science job