Metis

Metis was overall an amazing experience. From the application through graduation the staff and instructors were supportive and responsive! The course content was well designed and mostly clear. The most valuable part of the Metis experience was the peer support and 1:1 instructor/TA meetings. My peers and I still keep in contact for job searching job and networking. Additionally, the 1:1 feedback from TAs and instructors was pivotal to my learning. I also really appreciated the post-bootcamp careers support. This was importance to get advice/feedback throughout the job search process. 

I chose Metis because of the career support, network, and job portal. The curriculum is good but because every module is 2 weeks some of the modules felt rushed and some felt dragged out. The instructional and career support are great. There are 2 instructors and 2 TA's for each module. They all have multiple time slots for one-on-one office hours. If you don't understand something or need help with your project, there is always someone available to support you. The instructors are familiar with the subject matter but it depends on their teaching style. I had one instructor that I really understood and learned a lot from. There were a few instructors that was easy to talk to. I did find that towards the end of the track, the slides had less text and the instructors would lecture on a topic using only one picture for 30 minutes. For my personal learning style, I find this a bit more difficult to understand the concept. 

The Metis DS bootcamp was life-transforming, in the best sense. Perhaps no other three-month period in my life was so distinctive. It was tons of hard work, which I always felt glad to do, all day, late into the night, and most of the weekends too. (If there were more than 7 days in the week or more than 24 hours in the day, I would have spent the additional ones on the bootcamp work.) Ok, so I’m already unusually inclined and motivated to learn (I had a PhD already) and I’ve been keenly focused on learning situations for years (taught college for a decade) but I believe my perspective is relevant to anyone contemplating diving into a Data Science bootcamp. 
I carefully chose Metis because of its curriculum, standards, and reputation, as well as the fact it has a careers team (to help with the job search). I was not disappointed in the least. First of all, the best thing about bootcamp generally—which probably can’t be replicated any other way—is the variety of events, activities, and tasks that center around and circle around the subject (actually each subtopic). This was ideal for learning. For instance, often in the morning there would be a relatively small-scale one-on-one problem to work on for 45-minutes or so to build up a toy version of an algorithm from scratch (these “pair-programming” exercises nearly every day were _great_—so much so that some of us continued doing it a few times a week even after bootcamp was over); then a couple of hours later the same day (or sometimes the day before) there would be a one- or two- hour lecture on that same Machine Learning approach, and then later a workshop-ish lecture to get your hands dirty using the scaled-up industrial-strength pre-packaged (not from scratch) version of that algorithm. This meant that we learned how the engine under the hood actually worked so we would become skilled enough to tune it.
The lectures themselves were of various styles by various instructors, which again was bonus. (A uniform or cookie-cutter approach, or having only one instructor, would have been disastrous.) Although doing a bootcamp online (via zoom) might seem like a disadvantage, we turned it into an advantage. by intermittently having on-topic side chats, sometimes with apropos hyperlinks and diagrams posted, questions asked and answered (by the instructor, the TA, or other boot-campers) and the occasional witty nonsequitur for levity to keep the mood light—given the intensity of the material! There were many times when it was surprisingly thrilling. One of the reasons for this also is that people are coming into the bootcamp with such varied backgrounds, so the kinds of questions asked were all over the map; and some bootcampers came with their own expertise (in math, stats, comp-sci, probability, etc.) so they could sometimes contribute added nuances to the instructor’s already expert explanations. The mood and tone was always respectful and collegial—which can’t be taken for granted.
The project-based structure of the course was also ideal because we were grasping to master the lecture/workshop material in order to be able to implement it right away in the individual project we were working on for the given two-three week period, with that deadline looming. I got a taste of all the steps, from locating data, to wrangling/cleaning it, modeling, testing, interpreting, and building up a presentation that would be scrutinized by my instructors and peers. The day-to-day flow of the Metis bootcamp meant becoming fairly fluent with professional tools like git and fluent in installing and learning unfamiliar python packages needed to complete your own particular project.
Learning DS and ML is complex enough that it entails the potential for obstacles at nearly every step. (You better be brave. LOL !) Nevertheless, it was completely possible to always push forward because of the Metis setup: an incredibly active Slack channel (for posing and answering questions and sharing code and hyperlinks), the availability of TAs (for help via screen sharing), and an increasing sense, as the bootcamp progressed, of where and how one could seek and find (or build) solutions on one’s own.
One of the highlights of bootcamp was when the architect of Metis’s curriculum (the Metis mastermind herself) Sophie Searcy came in to guest-teach us on the topic of Singular Value Decomposition (a subtle topic of Linear Algebra that happens to be important for certain aspects of Data Science/Machine Learning). Another highlight was when Kimberly Fessel (who was _not_ one of my cohort’s instructors) came in at the end to give us (four at a time) a mock-technical interview, gently quizzing us on how to create from scratch a classification algorithm we had learned two months prior.
The variety of instructors had different emphases, different strengths, and sometimes didn’t completely agree, which is realistic, and gives a better sense of the actual kinds of divergence you’ll encounter out there in the DS world.
The instructors were great:: intense and serious about the subject matter, but also capable of being light-hearted at the right times. My main instructors were Vinny Sanguttuvan and Leon Johnson.  Leon’s lectures were clear, with a definite sense of priorities distinguishing the overarching point from the supporting detail.  Leon also provided excellent practical critique on bootcampers’ presentations, things that I still contemplate and consider vividly five months after bootcamp ended. Based on the particulars I had achieved with/through a given project, Leon was unique in suggesting next steps built on these and directions I could take that would further burnish my portfolio. Leon obviously has a firm overview of the DS landscape, is clear communicator, and encouraging mentor. Vinnie Sanguttuvan is one of the most dynamic and insightful teachers I’ve ever had (comparing to dozens of other great teachers I’ve had in college, grad school, as well as tech training courses). His teaching kept me and other bootcampers on our toes from start to finish. Nothing was ever routine or rote, but rather presented us challenges we had to carefully consider. Sometimes Vinny did this through Socratic-method, where he would pose a series of questions (or mini-problems) that would interactively lead to an insight, which therefore would stick. Other times he would relate mathematical/algorithmic/statistical principles to observations he had personally made about well-known tech industry products and services.  When Vinny was teaching, often you really had to think through mathematical or algorithmic concepts, and problem-solve on the fly. He is also very open minded about what types of problems might be tackled with algorithmic methods.  Vinny’s teaching presents a distinctive synthesis of creativity and precision that epitomizes what is most exciting about the Data Science field. 
I want to emphasize that while Metis’s excellent curriculum is one of the reason’s I’m glad I chose it, the most important learning events for me were the moments when the instructor cooked up his/her own problems or reflections for us to consider; these complemented the pre-cooked curricular material. That is to say, there was a mixture of very carefully planned out lessons and more spontaneous ones originating from the instructor’s creativity. It made for an exciting and engaging bootcamp, when you could sense that what and how you were learning was unique to that moment and context. (This was especially true with approaches to the pair-programming problems). I hope that Metis continues to allow seasoned instructors latitude in how they teach, including creating their own material from time to time.
Another instructor I want to mention is Dimitri Theoharatos, who taught python-and-math course I took a year before the bootcamp.  Dimitri moved quickly and efficiently, while also overflowing with a wealth of practical tips and laser-like precision. He peppered his teaching with astonishingly apropos industry insights of his own, giving me perspectives that sill condition my thinking a year later. I learned as much from carefully observing how Dimitri navigated the nuts-and-bolts of the python programming interface (jupyter notebook) as from what he verbally articulated. 
All the Metis instructors listened carefully to students’ questions.  They seemed to appreciate they were dealing with many intelligent and motivated bootcampers, so they calibrated their responses accordingly, never resorting to generalized rote answers (as sensitive teachers might be prone to do).
The camaraderie among bootcampers was inspiring and encouraging.  One thing that was incredibly clear after bootcamp ended:  Metis alumni help other Metis alumni, especially Metis alumni from prior cohorts. In the brutally frigid world out there of cold applications to jobs, the Metis alumni network is indispensable; it’s how I got most of my interviews, including the ones that led to me landing two simultaneous job offers.
Especially if you’re coming from an academic career, or this is your first job search, the Metis careers team (I worked mostly with Ashley) can be helpful in steering you what might be unfamiliar terrain of the corporate career landscape.
Before bootcamp: To make the most out of this experience, I recommend you prepare extensively.  Try to use Codecademy and Dataquest to learn Python and SQL before the bootcamp. Use Brilliant.org to learn basics of linear algebra, probability, stats, and calculus. (If you’re going to splurge on the bootcamp you might as well purchase memberships to these so that you begin the bootcamp with your best self. I used these further _after_ bootcamp as well.)  As many others mention in their reviews (of Metis and other bootcamps) the amount of material coming at you in the bootcamp is like a firehose to your mind, which you can absorb better to the extent you prepare in advance. If you get your feet wet with Brilliang.org, Codecademy, DataQuest, and the Metis python-and-math, you will be ready even if you don’t come from a thoroughly STEM background. During bootcamp, expect to spend all evening every weekday as well as a large chunk of weekends working on your bootcamp projects. Metis DS bootcamp is not a side activity; it’s a life-changing process. 

# Preparation

Before you apply I recommend you do as much of the following as you can:

* Take a college-level intro to matrix algebra course (and any pre-req's required to pass that course)
* Acquire introductory level python programming skills, but if you want to build web applications then learn OOP with Python and get some Flask under your belt. There are alternatives you'll learn in the class.
* If you can take a class on machine learning and deep learning before you enter the bootcamp, please do so. 
* Basic statistics and probability theory
* Basics of how to use git and github
* Basic paired programming skills

You can make it through with not all of this in place, but you may experience a lot of stress making up for any areas where you lack basic knowledge in any of the above areas. 

I wouldn't join this bootcamp if I had 0 python or 0 linear algebra knowledge even if admitted. The others maybe you can make do without them going in as you will get exposure during the bootcamp.

Metis provides a scholarship for traditionally under-represented people who are accepted into the bootcamp.

If you are admitted into the bootcamp you will be provided intro material. The more of that material you complete before the bootcamp the better your experience will be during bootcamp.

# While in Bootcamp

Our bootcamp was 100% online over Zoom. This was great, as I was in the bootcamp when shelter-in-place orders were in effect. Glad they were flexible! The bootcamp was only 3 months long. Even though it was really tough, it beat a 2 year $50,000 full-time masters program from a time/money perspective. 

Time is of the essence. This bootcamp requires at least 12 hours per day of effort (6 of it is mandatory lecture the rest is the time you'll need to execute your projects). It's not a good idea to do this bootcamp and hold a full-time job. Working on Metis material during weekends is a good idea, especially if you have a project that goes sideways and you need time to recover it.

The instructors are very patient. They're willing to repeat concepts in lecture, hold extra lectures, along with once-per week scheduled office hours. Instructors also do technical interview prep and offer quizzes at the end of bootcamp to help gear you up for job interviews, which will be technical. 

The course material is really good: It gives you the gist of a lot of very large complex libraries (like matplotlib) which are good introductions to these things which can serve as a jumping-off points when you want to do more complex stuff with them. Also weaves knowledge of effective  ways to use these libraries to do data science that may otherwise may be hard to discover.

There is career development material provided to help you to effectively network and traverse the interview process. The career development personnel will meet with you 1-2 during bootcamp for career development training.

Projects involve delivering strictly time-bound presentations. If you go 1 second over you can lose points. So prepare,prepare, prepare for your presentations.

There are plenty of extra lectures like how to setup a GCP instance for data science, and introduction to complex machine learning models like BERT and GPT-X. 

They are open to feedback and are willing to adjust the program when students experience excessive pain due to a "bug" in the program and are willing to provide honest feedback in one-on-one conversations. They're not here to grab tuition money and socially-promote you, they're serious about making you into a competent data scientist.

# After bootcamp

You will continue to receive support from career development folks. They will help you write your first resume that includes your bootcamp experience, and directions on how to update your LinkedIn profile.

Guest speakers who work in data science drop in to talk about work, offer career advice, and may be pathways to a job.

There are a lot of companies that recruit graduates out of this program, and that's how I got my first data science job. You can apply through a Metis alumni web site to opportunities from these companies or you may just get a referral.

My cohort was very cohesive, which really helps take the edge off this challenging program. Be in the mode of giving and not just getting and you will grow from the experience, IMHO. 

# Places Where the Program Could Improve

They should incorporate code review into the program as you will have to go through that in the real world and it may be a big learning curve to jump over on first exposure. Udacity MLND had code review which was helpful in improving my python programming skills. I understand this may make the program longer, and may be a trade-off.

An introduction to causality would be helpful. A lot of companies are looking for that skillset.

I hope they can regain accreditation (If this hasn't already happened). 

Metis was a great experience. The instructors are wonderful and you’ll make great connections and friends with the other students. You’ll have to work a lot over the 3 or so months of the course but you’ll learn a ton along the way. Metis’ career support is also fantastic. That with access to the alumni network can go a long way to helping you find a job. With that being said though you should plan on spending several months after the completion of Metis searching for jobs before realistically expecting to land one. It’s competitive out there and while Metis will give you a leg up it’s not a replacement for either some previous technical background or domain knowledge/experience in your industry of interest (technical or not). If you have one of those two things, are willing to put in the work during and after the program, and you can afford going 6+ months without a job, then I would definitely recommend it.

The course structure was great. I had some data science exposure in university (~40 hours total, 1 project).  The Metis bootcamp was around ~400 hours over the 10- weeks with 5 2-week sections and one project for each section.  The instructors were really great and had high expectations of students (we graduated with about 50-60% of the original program participants, although the ones that didn't pass got to retake sections).  I would recommend taking the Data Science and Engineering bootcamp instead of the Data science bootcamp (four of the five sections are the same but the one section that they don't share seemed more relevant with Data Science Engineering based on talking to other students).  Really would recommend this program, but I would caution those of you with very little python exposure or technical backgrounds.

Metis delivered on all my expectations. I decided to explore making a career transition to Data Science after working as a postdoc for 3 years after completing my PhD in Political Science at Cornell and feeling less than satisfied with my academic career prospects. After teaching myself some basic Python and brushing up on my linear algebra, probability and statistics (I had done a minor in Math in undergrad just for fun but that was over a decade ago and all my Poli Sci research was super qualitative) I decided to enroll in the Intro to Data Science course in June 2020 to get a better sense of my aptitude and whether or not I would like this kind of work. The Intro to DS course provided me with a nice overview of the field and the opportunity to complete an end to end project which gave me the confidence to apply to the full-time 12 week bootcamp starting September 2020. 
Metis was the only program I applied to as I was most impressed by the fact that they are the only accredited DS bootcamp, have very high quality instructors, a robust alumni network, and career support until you land your first job-and even after!
I found the program both incredibly rigorous and wonderfully supportive. Honestly, the coursework was as demanding as my busiest times in grad school, however, two things were really different about bootcamp. One was the number of opportunities I had for 1:1 support. I had at least one weekly meeting with an instructor (often twice a week!) plus another with a TA. This definitely exceeded my expectations and left me reassured that even if I felt in over my head at the deep end of the swimming pool, there was always someone there to help me refine my project ideas, better understand a new library or just reassure me that I was on the right track. The second was that the breakneck pace actually protected my from going into perfectionist mode. Metis really drives home the concept of the MVP (minimum viable product) which was something I had not previously been familiar with and totally revolutionized the way I thought about my work. Yes, I still spent many a late night and weekend trying to make each of my projects the best it could be, but with only two weeks to complete each one, I was so focused on making sure that I had something complete that there was no time to waste worrying about the ways that it wasn’t perfect and in the end I am incredibly proud of the portfolio I built. 
I would absolutely recommend this program to anyone interested in transitioning to Data Science but you absolutely must be ready to put in the work and take ownership over your own success. Enrolling in Metis makes an incredible breadth of resources available to you, but its up to you to actually use them, sign up for 1:1’s, ask for help etc. Trust in the program! Also, the time investment is no joke, I worked about 3-4 additional hours a day after the day formally ended and put in time on the weekends too so do make sure that you have a good support system in place to keep on top of ‘life stuff’, making dinner etc. That said, after the 12 weeks are over you do get your life back :)    
In my cohort I was among a small handful of people who hadn’t previously worked in *some* form of data/analytics role or STEM background. However, given Metis’s rigorous admissions standards, I knew that I had what it would take to succeed. To be sure, there was a steep learning curve, moments of imposter syndrome and being so new to coding I never felt totally comfortable with pair programming (which is something Metis could do better to manage in the online format tbh) but I did come to better appreciate strengths of mine that I had previously taken for granted like knowing how to give a good presentation or not being afraid to ask a question during lecture. Bootcamp is often described as ‘drinking from a fire hose’ and this is apt, but, the great thing about Metis is that you still have access lecture recordings for 6 months after bootcamp if you want to review material that you didn’t fully grasp the first time around (which has been super helpful while gearing up for technical interviews) as well as lifetime access to all the course material that is on GitHub. The instructors provide such a wealth of required and supplemental materials that I have had the opportunity to further dive into as needed in the months since bootcamp and feel like I am continuing to get extra bang for my buck! 
My fellow students were also an incredible asset. Despite doing everything virtually, we still had the chance to really bond and after bootcamp ended I’ve stayed in touch with a number of classmates to give each other support on the job search. Likewise, I have found Metis alum to be incredibly generous, willing to take informational interviews and provide referrals for a number of job leads.
I also want to give a *big* shout out to the careers team. In my first meeting with my advisor, she asked me what I wanted to get out of working with her and I said, honestly, I’m committed to doing whatever work I need to do to get a job but I’ve never applied to a non-academic job, I feel so out of my comfort zone and really just need someone to hold my hand through the process. And boy did she ever! After graduating in December and taking a little time off, once I was ready to begin searching for jobs full time we had weekly checkins over Zoom where I could ask all of my questions about reaching out to recruiters, talking to hiring managers, preparing for technical interviews and felt so supported and encouraged through the inevitable ups and downs. Additionally the other careers counselor was also available to for guidance while my assigned counselor was away and she would even periodically check in to see how I was doing. Big, big gold star to the careers team! 
Now I can proudly report that only 12 months after I first started learning Python and about 4 months of job searching post-bootcamp I landed an incredible job as a Senior Data Analyst making a salary 3 times higher that I did as a postdoc. There is no way that I could achieved these results on my own, and probably not at another bootcamp program either. The investment I made in myself enrolling in the Metis bootcamp has paid for itself many times over. If you're ready to do the work, I cannot recommend this program enough! 

Metis is on point. They are constantly updating their curriculum based on industry standards. They offer as much 1-1 support as one could desire. They support big ideas and help them come to life. I found a Data Scientist job within 30 days of graduating (it was with a company I'd worked for in the past, but never in data science/technical roles). They also help tailor resumes, and part of the daily routine is preparing students for technical interviews. The people I met at Metis - both instructors and fellow students, were outstanding and I now am part of an entire network of graduates in the Data Science world.

I enrolled in Metis after a few years of working in analytics as a way to expand my technical toolkit and go deeper into data science. I’d done a lot of research on bootcamps (and taken a somewhat disappointing part time course at one of its competitors) beforehand. I can say with confidence that Metis has the most intensive curriculum in the field and the highest bar for admission and success. The technical assessment you have to pass before you’re even admitted and the amount of pre-work you do before Day 1 should clue you in. The instructors, TAs, and career team are all extremely gifted and do so much to help the students. The curriculum is well planned and constantly updated. Because every cohort is only 3 months, there’s a lot of room for them to innovate and they take full advantage: you’ll be exposed to the most cutting edge data science tools--things you won’t get exposure to as quickly in your job or at a more traditional program. The team also does a lot behind the scenes to personalize guidance and make sure every student is on track. 
However, because of the high bar and intensive curriculum, you have to be fully committed to not only get through the bootcamp, but get the most out of it. Creating a new project from scratch every 2 weeks while learning new material daily is intense. You need passion and curiosity to fuel you. You need to really love learning and doing the work. It is not for the faint of heart. You also have to be willing to ask for help. It’s easy to grab time with instructors and TAs as long as you’re proactive about reaching out. All the other students are extremely helpful. There's a real sense of community and that lasts way beyond graduation. The most successful projects I saw were from peers who combined pure passion with technical learnings and took full ownership of their projects (from ideation, to documenting their repo, to blogging/marketing their portfolio). Throughout the program, you’re able to explore different avenues of data science work, which is invaluable in helping you understand what type of role you want next, especially as the industry continues to grow. 
The careers team is helpful during the job hunt, but I think people often have lofty expectations. Careers will give you the tools to succeed, but that’s the most they can do. Networking, applying for jobs, etc. are things only you can do for yourself. I know of many peers in the cohort who were very proactive, brushed off setbacks, and landed jobs they really wanted within 1-2 months (some of them pivoted from completely different industries). In my case, Metis directly led me to my post-bootcamp Data Scientist role. Every cohort, the career team shares graduate profiles with all prospective employers via email, and before I’d had a chance to even apply, a company I’d wanted to join for over a year reached out after having seen my profile. Several of the other companies I interviewed for also recognized Metis and the strength of its program when I spoke about my experience. Upon accepting the offer from my top choice and current company, the career team was also very helpful in guiding me through the offer process. 
Metis opened the door to my next chapter and is one of the best decisions I’ve ever made for my career. I highly recommend this program. First, it gave me the knowledge, tools, and guidance to level up to a new version of myself and the room to explore what kind of data scientist I wanted to be. Then it quite literally opened the door to one of my favorite companies and a role that aligned with exactly what I was looking for. It also gifted me a lasting network of friends and data science peers: our shared passion for the field got me through long bootcamp nights and still energizes me every time we reconnect.
If you’re passionate about data, willing to do the work, and be proactive about your career, then look no further!! May Metis be a game-changer for you as it was for me!  

I attended Metis Data Science Summer 2020 Cohort after working at United Airlines as a data analyst. Wanting to make the jump from data analyst to data scientist, I decided Metis was the right choice for me. The coursework is excellent and expansive, covering important data science concepts ranging from natural language processing to deep learning. The coursework is regularly updated to stay up to date with the most recent concepts and algorithms to keep up in a very fast moving industry. The instructors are excellent and patient. It is fairly easy to find time for 1 on 1 interaction with an instructor or TA, despite being in a cohort of about 30. I liked the project based curriculum, as it allows you to apply concepts learned in class to areas of your own interest. Presentation days are great as you get to see how the same data science concepts are applied in countless different settings. Furthermore, the projects are a great talking point in the interview process, which leads me to my next point.

After graduating Metis, I didn't utilize the career support much. 2 companies did look through the Metis directory and reach out to me, but other than that, most of my success came from LinkedIn or other job boards. I was fairly lucky that a recruiter reached out to me on LinkedIn about a position that was somewhat similar to my previous work experience. My previous work experience, plus my newly learned knowledge of basic data science concepts helped me pass the interviews and land a data scientist contractor job. However, I consider myself lucky, as this position happened to overlap with my previous work. I believe most people in my Cohort are still looking for employment opportunities. 

I would not have passed the technical interviews and landed my current job without the basic data science concepts I learned at this bootcamp. With that said, I believe that previous work experience is crucial in how your recruiting process goes. If you have a few years or experience under your belt, then this bootcamp is for you. If not, it could be trickier for you to find employment opportunities, despite the high quality of the bootcamp.

This bootcamp gives students everything they need to start a career in Data Science.

The curriculum itself is very strong, starting off with basic topics and continually building until each student has an understanding of all aspects of data science. Also, the five portfolio-building projects are perfectly spaced out to really drill the concepts from lecture into the student's brain. That being said, the common simile you hear throughout the bootcamp is that it is like drinking from a water-hose. Metis provides you with so much information that it is slightly necessary to gain a light understanding of every topic, only diving deeper into the topics that will help you finish your project. After the bootcamp is over, it's encouraged to continue reviewing material and further solidifying your understanding. Metis will provide more concrete details of the curriculum if requested through their website. I personally found that further education after Metis was completely necessary to land my current job, specifically knowledge in SQL and statistics.

The prerequisites for Metis are very important, in my opinion. You should have a basic understanding of stats, linear algebra, and Python. Of those three, having knowledge in Python is the most important. During the first week, you jump right into a team project due Friday, and if you're stuck on some basic Python issue, it's just going to weigh you down throughout the whole course. Luckily, if you chose to do the Metis intro to programming course, they deduct the price of that course from the DS Bootcamp tuition, which is a no-brainer (unless you've already taken a course in Python and feel comfortable). As for linear algebra and stats, you can kind of get by, but you probably won't fully understand the more math heavy lectures. Prior to Metis, I took a course from MITx to learn Python, which set me up very well to feel comfortable during Metis.

The team at Metis is very amazing. The careers team is very helpful and responsive. I was assigned to an individual careers advisor, but it never deterred me from getting insight from the other advisor, who was always eager to answer my questions. Also, the Metis alumni are very responsive as well, which was great during the job searching process. In the end, landing a job is on you, so Metis does what it can to help, but if you aren't motivated, there isn't much the careers team can do to get you a job. As for the instructors, I would like to say they are all highly knowledgable and experts in their field. The only reason I did not give them a 5/5 is due to the structure of certain lectures. Lectures had the tendency to derail and either run over time to get through all of the material, or just get cut short and the student would have to review the end of the slides or notebooks on their own. However, it couldn't be easier to schedule extra time with instructors or TAs to clarify any questions or get some one-on-one time to go over a topic.

In the end, I'd highly recommend this program to anyone who has experience in Python and wants to start on the path to becoming a true Data Scientist.

Metis was an experience equal to drinking from a firehose, covering ETL and EDA, basic modeling, regression, classification, visualization, and deep learning. With the onset of COVID-19, Metis has shut all of its physical campuses, moving to an online format. Although I did not get to take advantage of its physical facilities, I was pleasantly surprised with how we, the student body, were able to maintain a good sense of community, in part facing deadlines for projects and presentations as a group.

Most instructors were very well qualified and super helpful. Some, one might say, were even over-qualified. Some had taken the boot camp themselves, while others had not, often working at other boot camps beforehand.

The student class was diverse, with some students having a computer science background, while others did not. Some had MS and PhD degrees, while others did not. Generally, all students brought a unique perspective and unique interests, which helped the student body stay coherent.

I think learning the essentials of numpy, pandas, scikit-learn, and matplotlib/D3/Tableau is essential for any worker in the modern economy. Even though I think Stanford is fairly forward-thinking in its curriculum, we had one student in our cohort who was a current undergraduate: Metis provides the tools that traditional universities still omit or relegate to one-credit or non-credit courses. Basically, traditional education systems often leave out data science as an afterthought. Metis puts it front and center.