12-Weeks In-Person/ Remote Live Data Science with Machine Learning Bootcamp

The motivation in attending a data science bootcamp generally starts with reading testimonials, which are the experiences and thoughts of past fellows. I believe that my experience as a remote participant in the Summer 2017 bootcamp is unique as the NYC Data Science Academy made it live stream to me throughout the bootcamp. This is totally different experience than doing bootcamp online as I was able to join lectures, industry expert speaker sessions, and workshops as the fellows did in person. This was indeed great opportunity for me as I was able to ask my questions during the lectures and seminars and participated actively with my comments. The crucial point here is that live streaming the bootcamp and communicating through Slack channel made it possible for the team at the Academy to track my progress in the assignments and projects daily and encouraged me to participate fully in the bootcamp. 

I can definitely say that attending the NYC Data Science Academy bootcamp was one of the greatest investments in my life. I learnt how to code efficiently in R and learnt coding in Python with real life projects. I am hundred percent sure that my attendance to the bootcamp let me to find my current data science related post doctoral position. I could suggest future candidates to take some initial online courses for machine learning and deep learning, where they will find themselves more comfortable while approaching highly-technical projects in the bootcamp. 

One of the strongest side of this bootcamp at the NYC Data Science Academy is the time and effort that the hiring team spends on. For remote participants, this is much more valuable than any other thing as you are receiving constant help in finding best job depending on your portfolio. And you feel that you are a member of an excellent group of data scientist. As an active learner of data science field, I recommend the NYC Data Science Academy with full of confidence.

I was previously a student in one of the early cohorts of for the 12-Week Data Science Boot Camp, and it was one of the best experiences for my career.

Before going into lengthy detail, I will break this up into three sections: Before, During, and After in the hope of conveying a more comprehensive experience. 

Before:

Before attending the NYCDSA, I had just finished my undergraduate degree in an engineering field.  After taking an Intro ML course my senior year, I grew increasingly interested in the applications of AI and Machine Learning in industry.  When I discovered the field of data science, I was instantly hooked, but I knew I needed to learn more. 

I had a decent amount of programming experience, mostly with low-level languages and systems, but lacked statistical intuition and experience with scripting langauges.  After some more digging, I knew that my skillset at the time would not be competitive for the actual job market.  While my undergraduate education was excellent, the curriculum was reasonably classic, most of my courses focused on theory and textbook problems and what I lacked was skills and experience for real-world applications.  While graduate school was an option, at the time I wasn't sure if Data Science was a field I wanted to commit more formal education to without experiencing it first hand.  So after applying to various boot camps, I chose the NYCDSA to jumpstart my career. 

The interviewing process for the NYCDSA was simple and effective and included phone screens, code exercises, and some Q&A.  I chose this program because the company was relatively new and small and felt more like a community rather than a factory to churn out data scientists like other boot camps I came across.  After making my decision, I promptly moved to NYC after graduating and began my journey into data science. 

During:

The company was relatively new at the time I attended, but the caliber and experience of their faculty were top-notch, and it was transparent how passionate the staff was about developing the best and most efficient curriculum.  Within another couple of years, they were able to scale up, expand, and further improve their curriculum and course content at an exponential rate.

It is important to note that your mileage may vary with any boot camp.  This program offers awesome resources, but ultimately it's up to the student to determine how much he or she will get out of it.  I spent an average of 60~90 hours a week on campus for the 12-week program.  The course is still manageable for people who have families or other commitments, and I have seen many successful part-time students graduate from my cohort.  I just wanted to note that this was particular to my experience.  

The curriculum covers various aspects of data science and offers a cutting-edge foundational overview of stats, machine learning, and programming.  The core languages are R and Python.  The philosophy of spreading the breadth of languages rather than just choosing one is reflective of the fact that the tools in Data Science are highly mutable.  Rather than memorizing a single programming paradigm, the goal is to expand your programmatic and discrete logic intuition so that you can pick up other languages if needed.

After class, the projects were engaging, and it was enjoyable to engage with other data enthusiasts and attend data science meetups and conferences together.  I still keep in touch with many of my cohort mates years after the program. 

Moreover, Vivian and company have a robust network of many NYC companies and quickly expanding to other areas of the country.  By the 9th week, I've already had a couple of interviews lined up at top firms in the area and was able to practice and refine my onsite interviewing skills.  

Three months after the boot camp, I landed my dream first job at a fast-growing tech-startup in Chicago after receiving multiple offers.

After:

After landing my role as a Data Scientist, I still maintained a close relationship with the NYCDSA.

The support of the NYCDSA does not terminate at the end of the cohort, and they do a phenomenal job of providing ongoing support for their alumni and students.  After the boot camp, I've continued to receive ongoing help (practice interviews, coding exercises, access to review course content, network events, resume editing) which were crucial to my future employment.

Not only do they offer support for students seeking employment but they provide support for education as well.  I was actively interested in an advanced degree in a field that leverages machine learning, and my experience at the boot camp only affirmed this for me.    Many of the faculty come from incredible backgrounds and are very willing to share their experiences.  Their advice and transparency were constructive in my choice of graduate program.  Currently, I am a part-time Masters candidate at a top data science program with a focus on Machine Learning and Data Ethics while working full-time as a data scientist at my current company. 

The skills that I learned from the NYCDSA and later refined at my current company significantly prepared me for my graduate courses, allowing me to test out of basic classes and attend advanced topics in machine learning and statistics.   The skills from the boot camp carry over very well for most data science roles or education programs.

In short, what I wish to convey is that a career in data science is a journey of continual learning and NYCDSA does everything they can to optimize the learning experience for their students.

However, please note that the Data Science Bootcamp is tailored to provide balanced curriculum among programming, stats, and machine learning and is meant to be a program to strengthen core skillsets rather than a masterclass for already experienced data science veterans.  Although the company does offer more specialized classes in more targeted areas. 

If you are looking for a program that will teach you everything there is to know about data science within a mere few months, then I'm afraid that those may be in general hard to find since data science itself is both a hybrid of multiple subjects and growing as a professional field. 

On the other hand, if you are serious about transitioning into data science as a career and if you are seeking a program that offers competitive curriculum, a strong alumni network, and a community of data enthusiasts to engage with, I would strongly recommend looking into this program to jumpstart your career.  I cannot emphasize enough how much this program has benefitted my professional development both during and after the boot camp. 

Attending the NYCDSA 12-week bootcamp is the best career decision I've ever made. I came to it after finishing my PhD in math and working for a year in academia. I wanted to get into data science for lots of reasons (leveraging my background, interesting problems, ample job opportunities, interest in coding, good pay, etc.) but after spending six years in graduate school, the last thing I wanted to do was go back to school for two years to learn basic data science skills.

This program allowed me to do this career change quickly without compromising the quality of the material I learned. The program emphasizes sound foundations in coding in R and Python and in both the theory and application of machine learning. The pace is very fast but you learn and do an incredible amount in those 12 weeks. Even when full absorption of the material isn't possible, the bootcamp does a great job of exposing you to tools and concepts you'll need to be familiar with later. 

The projects are incredibly valuable for building skills and exposing future employers to your work. You would be spending your money wisely just to come and complete the projects.

The bootcamp students and the TAs/instructors have varied backgrounds, which also makes for a great environment because you can learn from everyone. The entire company - from fellow students to TAs to instructors to the CTO and COO - is filled with smart, hardworking people who are pushing to help you reach your potential. 

TL;DR: The NYCDSA full-time bootcamp is a great way to start a career pivot into data science. The curriculum covers the most important topics in the space; the instructors are accessible and actually care; and you'll get out what you put in.

The first four weeks are python and R basics. These can get a little tedious and heavy on programming, but are important so that you can fly around when doing the more advanced stuff. The next couple are on web scraping, then a few more on machine learning, then some on more advanced topics like distributed computing, time series analysis, and deep learning. 

The 3-hour morning lectures are helpful and touch on the most important issues from a high level. The homework assignments (usually every other day) and projects (4 total) are how you actually retain the information. If you stay on top of these assignments and aren't afraid to ask for help, you'll learn so so much! I thought the first ~8 weeks were thorough and well-paced. I thought the last 4 weeks rushed through a number of advanced topics without enough time to retin the information through projects, homeworks, and other "real world" practice. I'd recommend choosing a few of these topics to really focus on in your projects rather than trying to understand everything from the last 4 weeks just from a high level.

This particular bootcamp has a lot of industry connections. After graduating, the hiring partner cocktail event will lead to a number of interviews with companies looking for entry level people.

Taking this bootcamp (or any other, frankly) provides a stamp of legitimacy on your pivot to data science that you probably couldn't get from just teaching yourself python and doing online coursera courses.

You'll get out what you put in with NYCDSA. Sometimes this means 60 hour weeks right before projects are due.

I came to New York City Data Science Academy because I wanted to become a better coder, to  become more knowledgeable about machine learning, and to get a better job. Having completed the bootcamp in the Spring of 2017, I can say that through the Data Science Academy, I was able to accomplish all three.

Before the bootcamp

Previous to the bootcamp, I had a job as a data analyst which gave me the exposure SQL, Linux, Hadoop, and some Python - all tools that are taught in the academy. I knew I wanted to improve my overall problem solving approach, specifically using Python and R. After a few years as an analyst, and many months of debating if enrolling in a machine learning bootcamp was worth the time and money, I decided to go for it. Although I do not have a masters degree like many of my fellow cohort members, I knew that I could use my work experience to my advantage in preparing for the bootcamp. Like many others have stated, giving yourself enough time to go over the 100+ hours of prep work before the bootcamp is highly advised - being able to perform the basics of Python and R will set you up for success.

Preparing before the bootcamp is also crucial in another way. As you spend more time studying, you spend less time doing all the other normal things you’re used to doing in your life. In order to make the most out of the bootcamp, sacrifices must be made, from your social life, to your eating and sleeping habits, and to the amount of coffee you normally drink. If you don’t get used to it before, adjusting to these changes midway through the bootcamp can be a challenge.

During the bootcamp.

If you spend enough time preparing before the start of the bootcamp, then the first month or so should not be too challenging (but still very useful). Many of my fellow cohorts actually became nervous, thinking that our investment in the bootcamp might not have been worth it. Don’t fret. After going over the basics again, the fun truly begins.

After the first month, you will spend every day learning machine learning concepts, applications, statistics, and then applying these techniques in both Python and R. This is no easy task in a few short months, which is why the instructors, teaching assistants, and Vivian, deserve so much credit in churning out so many qualified data scientists in such short time. The instructors are always, at all times, helping and guiding you in the right direction. On top of that, you have the additional resource of working with your fellow cohort members, all who have unique backgrounds and always willing to help.

In the end, the journey would not be worth it without days of extreme struggle and frustration. Some days I felt really confident in the material, other days I did not think I had what it takes to be successful. What I believe the instructors are best at is instilling the confidence in each and every student, spending as much time with you as needed to successfully complete the projects.

The last few weeks are spent on tidying up your resume, github, blog posts, and interview skills. Aside from learning both R and Python in the bootcamp, one of the reasons why I chose the Data Science Academy was because of the strong professional connections that Vivian and the team have developed over time. The final day is dedicated to a networking event, where the ratio of companies to students is almost 1 to 1. Although it can be a bit nerve wracking, Vivian and the team do a good job of preparing you on what to expect.

After the bootcamp

I was lucky enough to land an internship at a startup as a data science intern from one of the participating companies at our networking event. I have to give my experience to the bootcamp all the credit for this. Had I not had relevant experience and projects to speak of, I would not have been able to land the job. As my internship was coming to an end, I spent more time with Vivian and the team doing mock interviews, going over practice questions, asking for help on take home assignments, and constantly reviewing. Without a doubt, I can say that the three months of the bootcamp was the second hardest thing I’ve ever done - the first hardest thing was getting a job afterwards.

Vivian and the instructors have the uncanny ability of knowing what specific skills you need to improve on, based on constant back and forth communication based off of past interviews, as well as the interviews you eventually take. You will fail, and fail a lot. Most data science interviews are designed to test you on the very limit of your knowledge on data science subjects. With practice, you will answer the questions confidently, and even if you are unsure of a question, you will be able to communicate a thorough data science process on how you think the question could be answered. If you fail an interview, it’s another lesson on how to improve for your next interview, which Vivian will most likely have helped you set up already.

After months and months of dedicating my life to all data science related activities, I have landed a job as a data lead at a media company, and have the entire NYC Data Science Academy program to thank for it. If you are seriously considering a future career in data science, then I can 100% vouch for the academy, so long as you are ready to work harder than you have ever worked in your entire life. At the end of the day, it’s all worth it.

I came to NYCDSA immediately after graduating from college with a bachelors in math and finance. I had been struggling with passing quantitative interviews due to my lack of programming skills. The New York City Data Science Academy helped out sooo much on that end by constantly keeping us busy with lectures, homework, and projects. I was very surprised at how quickly I was able to learn programming in Python and pass assessments and interviews for a number of top hedge funds. There is no way I would've been able to learn as much as I did in as short amount of a time anywhere else. On top of that, having instructors, TAs, and a team devoted to helping me develop the skils and connections I needed was invaluable. After graduating from the bootcamp, I completed a project in Python for a quantitative investment firm, impressed them, and got the job. I'm so glad I came to NYCDSA to build the skills I needed for a career I'm truly passionate about. 

Machine Learning is transforming the world at an incredible pace and I felt it was imperative for me to acquire new skills in order to be professionally relevant. The summer bootcamp fit my schedule perfectly and I decided to enroll to better understand this exciting new field. With a PhD in Engineering from a top-five ranked school and significant background in Computational Materials Science, I felt that I had sufficient background to be successful in the bootcamp. Due to my hectic schedule, I was unable to complete all the pre-work prior to the bootcamp and I believe that this had an impact in my learning experience at the later stages of the bootcamp. In my opinion, pre-work is analogous to binary decision trees—you are trained to be independent weak learners ahead of the bootcamp. The actual bootcamp is more like a random forest where individual students work alongside other students as well as Teaching Assistants and Course Instructors to significantly contribute on a variety of real world projects related to Data Visualization, Web Scraping, Machine Learning and Big Data. The course is fast paced and students are exposed to a variety of technologies relevant to Data Science. The instructors are knowledgeable and fellow students in the cohort are sharp. It is not surprising that NYC Data Science Academy is one of SwitchUp’s Top Bootcamps of 2017. I strongly recommend this bootcamp to individuals who are seriously interested to pursue a full-time career in Data Science.

I came into NYC Data Science with experience working as a data analyst at several companies and active data consulting work.

At NYDSA, I wanted to spend three months solidifying my existing skillset as data analyst and learning areas that I did not know much about like machine learning and big data.  In my jobs, I had previously worked with both Python and R, and appreciated the value of both languages. I also liked that NYDSA wasn't part of some big cookie cutter data science bootcamp chain. 

For the first month of NYCDSA when we covered topics like data wrangling, visualization, shiny, and web scraping,  it was mostly review for me. That being said, I learned some new tools and tricks, and was able to work on some interesting projects with some help with from our great teachers. I also got to meet and learn from a lot of interesting classmates. The students at NYDSA come from a wide variety of backgrounds, from PHDs to straight out of bachelors programs, and are definitiely part of the value of the program. 

After watching me instruct classmates on web scraping, which I had a lot of experience with before the bootcamp, NYCDSA asked me if I wanted to record web scraping lectures for their online data science bootcamp. I agreed to record them and got to have some experience teaching while I was still a student in the bootcamp, which both solidied my skills and helped me earn back some of tuition. 

During the machine learning and big data portion of the bootcamp, I was exposed to lots of new material and learned a lot. While I still have a lot to learn, I now have a good understanding of different machine learning models and techniques, and some of the big data technologies. 

One month after the bootcamp ended, I started working full-time at a consulting company that I was consulting for during the bootcamp. With my experience in the program, I was able to negotiate better terms on my contract with the company. In addition, I have been doing some additional data science consulting on the side (one project referred by NYCDSA) and the skills learned at NYDSA have benefited me in all my work. 

If you invest your time in the program, you will get three months of data science learning with awesome teachers who know their stuff and are willing to help you through any learning hurdles. As someone who has and continues to learn most of this stuff on my own, three months with expert teachers definitely accelerated my learning pace. So if you want to spend three months learning a lot of data science, I would recommend you sign up 

Going to NYCDSA is one of the best decisions I’ve ever made. Data science was completely new to me and I didn’t have a vey good programming background.  At NYCDSA, i was able to master both my data science and programming skills with the help of ever-present instructors, TA’s and friendly classmates. The curriculum was well balanced with all important data science topics, lab practices and mandatory individual projects included. In addition, they also had weekly coding challenges and professional development courses which teaches you how to  deal with interviews and present your self in the real world. The three months program was intense but is doable if you put in effort and dedication.  After the course, the academy also help you with your resumes and get interviews with companies within their connection. I highly recommend NYCDSA to all aspiring Data Scientist as this program helped me achieve my dream of becoming Data Scientist within 3 months after graduating.

Cheers NYCDSA

I am a recent NYCDSA graduate. Before the retrospect. The outcomes first:  Know myself better, made great friends, landed a great job within 3 months, came back to the dream land……

My story is a little different. The pre-DSA career is not bad at all.  I have had worked for a couple of top companies in the pharma and health industry for several years after getting my PhD. For some reasons, I am always thinking of expanding and tuning my fields. Life is quite dynamic that I recently moved to the east coast and happened to get stresses from various parts of the life. Rather than hanging there, I decided to challenge myself and make changes. I have thought of schools but it is not realistic for my situation.  I have studied some bootcamps and visited NYCDSA last year. The people there were very kind and down to the earth. I was not too confident at first but I would like to give a try. Therefore I gave up what I was doing, which was pretty risky and got a lot of doubts from people around me.

But I always know very well and anyone should keep in mind that 3 months can hardly make one totally expert in data science, otherwise I will be willing to pay 10 times more. We should always keep learning; the effort will pay back.

The course designs are up to date. They focus on practical data science skills.  The first month is about coding and analysis in R and python, which I found helpful in my case. After that, machine learnings have been emphasized on both python and R, then the big data part. Most of us are not directly from cs or math backgrounds. We had to work very hard for the classes in the morning, the homework at night and most importantly, the 4 projects with different emphases. We were pushed by the deadlines and harsh schedules, like the real world, have to come out the results before knowing everything.  I was the humblest guy during the 3 months in my entire life J. The instructors are very kind, helpful and always there to help. During the process, I have made some great friends, knowing people from such diverse backgrounds and also know myself better. Although I got my job on my own search according to my specialized interests and desires. The NYCDSA tried its very best to connect alumni and companies for the hiring. They have great connections. There are many alumni get jobs from the network.

Suggestions and lessons: Once you have made the decision to attend the bootcamp, forget the past and dedicate yourself.  Always focus, do not doubt your decision for one second or look back.

Skill is very important, but that’s not everything. Try to communicate, make friends and find out your strength well during the bootcamp. Design at least 2 of your 4 projects well, you know yourself the best, interact with the instructors frequently but do not solely rely on their ideas.  Do not live far, I burned myself out on the long trip every day. For the projects, try to team up with members most accountable, fair and those with high integrity and work ethics. If you share the common interest, that’s even better.  Do not judge others only by the technical or coding skills, it’s a teamwork, cultural fit is critical.  Last but not the least, we do pay a lot and might give up what we already have for the bootcamp. But when you get the quick reward in life, these pains with hope are abosultely worth it.

I came to the NYCDSA after having spent several fruitless years on the academic job market with a Ph.D. in English.  I was depressed and didn't really know where to redirect my career.  I had a background in math and a strong enough interest in data, but I knew that I needed to skill up, because that no one was going to take me seriously without another credential. 

Despite my unusual background, the NYCDSA was very welcoming and helped me get unstuck.  I'm in a new, fulfilling career, and I couldn't have done it without this program.  By the end of the first year in my current job, I'll recoup the investment with respect to my prior earning power.

The Good:

1. The cohort my term (and, I suspect, for most) is a really excellent mix of people.  You have ex-academics, mid-career folks who are trying to reskill, fresh B.A.s who need to weaponize their math/CS skills; you have people from the sciences, from finance, from advertising; you have representatives of over half a dozen countries.  They bring a variety of talents, and you learn a lot from seeing what problems they want to approach and how to approach them.

2. I've been in my current job about ten weeks, and I've already used most of the curriculum.  I use R every day, Python frequently, and a fair amount of SQL and Mongo. Good BI visualization can really set you apart, so the early part of the course has been most valuable. I've already done a fair amount of scraping, too, and occasionally contributed on some machine learning.  The only thing that would be useful for my job that the program didn't cover was JavaScript, but that's really more relevant to my field (advertising) than data science.

The Solid:

3. The instructors are all very committed.  There's a lot to learn, and they work hard to see that you get it absorbed.  I'm not wild about the setup pedagogically--three-hour lecture blocks make it easy to lose focus--and sometimes the instructors are not the easiest to follow as lecturers, but they put tons of work in, and it's appreciated.

Could Be Improved:

4. I know they try on job assistance--there's some work with resumes and interview prep, and they set up some interiews for you with their assorted hiring partners--but they don't seem to have the staff they need (at least as of my job run) to supervise it as well as they could.  To give an example, they didn't have a dedicated placement officer when I was there.  Job hunting is always terrible and unpredictable, but my impression is that some of the other camps (e.g., Insight) do a better job of minimizing the aimlessness and frustration it can incur. 

Still, after three months in the program and three and half on the market, I got a job, and I wouldn't have done it without the academy.  It was an important stage in my life, and I'm happy I made the decision to go in.  It's let me move on with my life.

I have had a very positive experience during and after completion of the NYC Data Science Academy's Full-Time Data Science BootCamp. I chose this particular school because they were the most transparent (the full - and very rigirous ciriculum is available on their website) and the depth and breadth of interview questions from the Data Science Academy convinced me that they take this very seriously - and that this would be a very difficult but rewarding 12 weeks.

To start from the beginning. the application process of the time was rather straightforward - a web application, a short programming assignment, and an interview. Although I didn't have much familiarity with Python or R and had been working in the retail wealth management industry for the past 3 years, my educational background is a B.S. in Mathematics and a minor in Computer Science and was considered sufficicient for me to be accepted into the program. In hindsight, I think the most important prerequisite (outside of the base math/computer knowledge) is having passion for data/programming/AI/statistics/etc then the potential difficultiy of the content becomes secondary to your desire to learn and improve.

The Data Science Academy did a terrific job selecting the studens - the main commonality being the passion and desire to learn a lot of difficult content in a short amount of time.

The program is project oriented - both individual and group projects. There is a good deal of flexibility about the project topic/content so each student ends up with a unique portfolio of ~4 projects (and corresponding presentations/blog posts).

For example, I did an EDA (Exploratory Data Analysis) project and created an interactive webapp using R and Shiny based on data for a particular anti-poverty initiative from a large mulitinational charity. This webapp visualized the breakdown in demographics, geography, and other metrics to help that organization identify trends in the data and better allocate their resources going forward.

The other projects I worked on utilized a wide array of popular datascience tools and techniques, my personal favorites: web scraping (big fan of Selenium + BeautifulSoup ), natural language processing, visualizations, and machine learning.  There are a few technical resources available that not all students take adavantage of, those are the various linux servers and hadoop/spark clusters available. During the machine learning project, each time I ran my ensemble of models it woud incapacitate my laptop for ~30hours. I soon discovered the value of using a high powered remote server for number crunching - and polished my linux/BASH scripting skills in the process.

The staff and instructuors are very thoughtful about keeping an environment condusive to learning. Every Friday there would be a Q&A session with the staff where they would ask the students their thoughts on the pace of this week's ciriculum along with other questions/issues. I mentioned that the hand sanitizer dispensar was empty, they listened and had it filled the next week.

The instructors are great. The lecture sessions can run on a bit long, but they are finely tuned machines, packed with a ton of great content and example code. They are accessible on slack and answer frequently answered my coding questions at odd hours of the evening. As the content grew in difficulty, it was very obvious that each instructor has mastered the topic of their lecture - I never felt dissapointed with their answers to my questions.

The atmosphere was condusive to learning, everyone was open to helping each with homework/project coding questions if asked. I was pleased with the sense of comradary among the students as opposed to the toxic competitive environments I've seen at some traditional higher education institutions.

The Raspberry Pi computers have been a personal interes of mine, and one of my projects usilng a Pi 3 caught the attention of the managent staff at the Academy. He saw some good potential, and put me in touch with their PR contact, two months later my project gets publised online and in print  - https://www.raspberrypi.org/magpi/issues/61/ (page 13).

Towrds the end of the BootCamp their staff put me directly in contact with several managers/exeutives at business hiring data science personell. I had much better job application results when I leveraged their network as opposed to a cold application. Althought it took a few months after graduating, the Data Science Academy put me in contact with my future employer.

Currently I'm working with the title Data Science Contractor for a Data Science Consultancy. I am extremely happy with the work I'm doing. Every day I get to work on interesting conceptual puzzles and work with Amazon and Google's cloud computing and big data tools. Our work is primarily done in Python, BASH, and SQL, and the NYC Data Science Academy really helped me prepare for the various technical and statistiacal challenges in this profession.

I couldn't be happier with my decision.

What impressed me most about my experience at NYCDSA was that it exceeded all of the expectations I had from speaking with the instructors and researching the program online. The entire team truly went above and beyond and I have only positive things to say about the instructors, the curriculum, and the way the experience changed me personally.

I found the instructors at NYCDSA to be not only incredibly knowledgeable, but approachable, thoughtful teachers. They seemed to really care about each student’s development and regularly stayed late in the evenings to offer help. If they could not be present in person, the instructors were always an e-mail/Slack message away and made it a point to check in with students and offer additional resources. I also liked that they not only taught the theory behind machine learning algorithms, but explained their most common applications and pitfalls to watch out for.

The curriculum at NYCDSA is constantly updated to reflect the most valuable skills for the real world. I found during interviews that whenever I was asked whether I had experience with a certain data science technique or language, I could either say “yes” or show a project to demonstrate my skills directly. What I was taught always matched up with what was requested of me in the interviewing or working world. Even after the end of the bootcamp, I kept my slides and materials for review, and was provided with hundreds of interview questions to help me succeed going forward.

Most importantly, the NYCDSA provided an amazing support group and helped me transform myself during a critical point in time. The other students were dedicated, kind, and came from all different backgrounds. I learned a huge amount from them and the instructors about the process of learning a skill like data science/programming and collaborating successfully. Apart from teaching the curriculum, instructors also provided resume reviews, listened to elevator pitches, and made themselves available to discuss interview experiences. I felt as though I had a whole village behind me, rooting for my success.

I would without a doubt recommend NYCDSA to any friends or colleagues looking to learn data science. It was an exceptional experience and I feel grateful to have found it.

I attended the Spring 2017 Cohort of the data science bootcamp at the NYC Data Science Academy. Originally I had read up about the program in 2015 but did not feel prepared to quit my job and take a leap into a full-time bootcamp. I spent about 2 years preparing myself by taking courses online (mostly through coursera) to build up my Python and R skills. Part of me was hoping that coursera courses would be enough to land a job as a Data Scientist or even as a Data Analyst, however that was not the case. Finally in January of 2017 I felt a bit more confident to apply to the bootcamp, and once accepted I decided to officially make the switch into a career in data science. 

The bootcamp curriculum is very intensive and cannot be taken lightly. A majority of my fellow students were in a similar situation where they quit their jobs, and invested a great deal of time and money considering that this program is not cheap. Keeping that in mind it's very important to understand that this is not a golden ticket to a better paying job. Simply showing up every day will not be enough to get the most out of your investment while attending this program. 

Before the Bootcamp:

The pre-work which is part-time and can be completed online or in person is very important as it serves as a preview of the first couple of weeks of the program. If you complete all of the pre-work (which I highly suggest) the first few weeks of the bootcamp might seem to be full of redundant information and make you question your decision to pay for a very expensive course which is teaching you things you already know. Don't worry too much about that because that is just the calm before the storm as things pick up very quickly as you approach the end of the first month. It's also much better to spend the first few weeks mastering the basics by going over things you know as compared to jumping into the deep end by learning everything for the first time on Day 1 of the bootcamp. Take advantage of this "down time" to master the basics and prepare yourself for what's to come because once you get to machine learning things start to get very serious. 

The pre-work mostly covers Python and R programming which are vital skills to have in order to make it through the program. If you can have a basic understand of Python and R before the bootcamp it will only help you because just like anything else the more you do it the better you get at it. The pre-work doesn't include SQL which I believe is very important to have at least a general understanding of the basics. It also doesn't unclude linear algebra or statistics which are also extremely important especially if you have been out of school for a while. You do not have to master any of these (Python, R, SQL, linear algebra, or basic statistics) before the bootcamp but having a basic understanding will help a great deal. 

During the Bootcamp:

During the 12 weeks every day involves learning new information, and that is why it is extremely important to keep up with the homework to review what you have learned. The projects are also very helpful to link everything together. The projects are a great opportunity to showcase your data science skills and you should pick topics which are relevant to industries you are interested in. Make sure you can explain every aspect of your project because you will be asked questions about them during interviews. 

The staff is extremely helpful and will take the time needed to help you with anything you need to keep you moving forward so do not hesitate to pull an instructor or TA aside to ask questions. The program is not perfect and the staff is constantly working to improve the curriculum. Pulse check on Friday afternoons is a great opportunity to voice your concerns (which my cohort took full advantage of) and speak your mind of what you find helpful and what can be improved. This is not only helpful to you and your cohort, but also for future cohorts.   

After the Bootcamp:

Once you make it through the 12 weeks the learning does not stop, because you will feel that there is much you still do not feel confident about. This is an unfortunately do to the fact that during the limited amount of time (12 weeks) there is a great deal of breadth of materials covered but it's impossible to go into great depth on each topic. This is when your show of commitment comes into play where you have to review concepts which you do not feel confident about. Reviewing the material is helpful, and completing coding challenges through HackerRank also helps. The recommended textbook is another great resource (ISLR) which should ideally be read before or during the bootamp. I unfortunately waited until after to read the text which is great for reviewing the machine learning concepts. There are many resources available to help review. I found some very helpful YouTube videos other other textbooks to review machine learning concepts I did not feel confident about. This will obviously vary from person to person depending on your learning style. You can choose to spend time reviewing everything, or focus on mastering concepts which are relevant to jobs your are applying to. 

Job Hunt:

The NYC Data Science Academy is there for you during your job hunt. They will help you fix your resume which helped me go from getting no replies for jobs that I would apply to before the bootcamp, to having 10 phone interviews lined up within 2-3 weeks of finishing the program. The hiring partner event is a great opportunity to connect with prospective employers. Mock interviews are available to help work on interviewing skills. I had my mock interview 2 days before my actual interview, and it was a previous graduate who is currently working as a data scientist. His advice helped to boost my confidence and within a few days of my actual interview I received an offer. 

Take Home Message:

Overall it was a great experience for me and I do not regret the sacrifices I made to attend the program. If you are interested in this program please give it your best and keep in mind that at the end of the day the burden is on you. You are the one who has the most to lose if you do not take full advantage of this opportunity. There is a great deal of self-studying involved before, during, and after the program. The more dedication and effort you put into your journey to become a data scientist, the more likely it will result in a positive outcome. You might hear back from some of the jobs that you apply to, you might not hear back from most of them. Not all interviews will go well but with each failure you will learn what needs to be improved. My advice is that if you are motivated and dedicated to start a career in data science, and willing to put in the required work then this 12 week data science bootcamp at the NYC Data Science Academy is the right choice for you. 

I would highly recommend anyone who wants to switch career to data science or strengthen data science knowledge to apply NYC Data Science Academy.

Before I joined the Boot camp, two of my close friends already graduated from the program and landed their dream jobs. So unlike most of people who don’t know too much about this program and have to do some research before applying it, I applied the program without a hesitation and also had a high expectation as well.

The curriculum design was excellent and really taught you how to learn new tech skills, frameworks quickly. It could be hard for people who are not exposed to programming or statistics to keep up the pace. Make sure to go through all the pre work and learn basic statistics before attending the boot camp. Once you started to work on your final project, you will notice that you’ve learned so much.

All the instructors are very talented and very patient to students.  

Vivian and Chris work hard to help you to find a good job once you’ve graduated. After you graduate, NYCDSA sticks with you.  Vivian and Claire emailed us frequently with new job opportunities and openings.

It’s not going to be easy. You will have nights you have to stay up to finish the project, missed parties that you don’t have time to attend to. But it will be worth it! The knowledge that I’ve learned in 3 months are way more than my two years master degree and I got my dream job too.

I’ve never regretted to attend NYC Data Science Academy. I’ve met so many amazing friends in the boot camp. It’s a very valuable experience to me in terms of career development and personal growth as well.

If you are passionate about data science and big data and you are willing to put hard work to achieve the goal in a short time of period, there is no better place than NYC Data Science Academy to learn data science skills.

The NYC Data Science Academy 12 week in-person DS bootcamp will give you quality instructional resources, project experience, and beneficial job support. In 12 weeks I was able to learn a vast amount about the field, and gain the momentum necessary to secure a position 2 months after I finished. I greatly enjoyed the intense intellectual environment, as well as the comradery with my fellow students. The instructors, TA’s, and management are approachable and receptive to your needs, as long as you pester them. The bootcamp was not perfect, but I was highly satisfied with my experience, and it was worth the cost. It definitely accelerated my skill set and allowed me to gain experience, while making friends in the process.

The curriculum has great breadth ranging from fundamental statistics, to carrying out end-to-end analyses in both Python and R, to covering big data tools. The breadth and 12 week time constraint meant that depth was sometimes lacking, but this was reasonable. It just means that you pay more attention to the subjects you're interested in or where your time would be well spent. For example, I am not very interested in NLP, so I didn't devote much energy there, and instead focused on time-series analyses.

The instructors are all very knowledgeable, and passionate in their respective sub-areas of expertise. They were the real deal. The only drawback was that some of the lectures were hard to follow, but it was not hard to get clarification from the instructor or a fellow student.

The bootcamp structure is open and approachable. Everyone is on the same floor in midtown Manhattan. Feedback is encouraged, and I really appreciated the opportunity to talk to the people who were in control of things, and who could help guide me in the right direction. This openness was a huge plus in my experience.

The job support was pretty solid and consistent. I got some interviews from the hiring event, and the door was open for any job-seeking advice I had along the way. New opportunities were pushed in my direction, and I was frequently reminded to stay sharp with my skills. To help with this post-program training material was provided.

A drawback of the program is that seeking out the help you need was harder than it should have been. As a student you were expected to seek out the help you need. This is fair, but it would have been nice to be challenged a bit more in the curriculum, for example with more mini-DS case studies. Another issue was that It was difficult to get feedback on projects. I think support with projects over the whole process could have been better. On the other hand, you get to choose your own project topics, and you will learn by doing. Also, there is continuing support after the bootcamp has ended until you find a job at the least. You also have a place to come sutdy even after the bootcamp is done.

All in all, go down and talk to them if you have any doubts. Talk to the students, teachers, and anyone else you can find. I think if you love to learn, and you are proactive about seeking out the help you need, then this bootcamp will help you learn a ton in a short amount of time.

How I started

When I started getting interested in data science, I was looking for any online courses/ bootcamp to help me get into the data science world. And I found out NYC Data Science Academy was the best out of all the data science bootcamps in terms of curriculum, and after talked to Claire and Vivian about how they helped students to locate job after the bootcamp, I decided to apply for the full-time bootcamp.

Preapre for the full-time bootcamp-Prework

After I got admitted, I got access to the online prework courses (Python and R introductory courses). I really love how they organized each video and there are exercises following with each part in the video. I didn't know anything about R and Python before, and the pre-work assignments are overlaped with the content of the first 4 weeks of bootcamp, so it helped me to adapt to the speed of the class in the bootcamp.

12-Week Full-Time BOOTCMAP

The bootcamp was very intense, the curriculum was comprehensive, which covered from data anlysis, machine learning in both Python and R, and introduction to Big Data tools. We also did 4 different projects, which was important especially when it comes to job hunting. 

I didn't look for full-time data science jobs after the bootcamp because I got admission to the master program at Columbia University, and thanks to my experience at NYC Data Science Academy, I can handle the programming and data work in my current courses and projects. 

And I still receive ongoing career support from the team and chat with instructors and students time from time now. It was so nice to work with all these hard-working and passinate people in the data science world.  100% RECOMMENDED

Long story short --

I have a PhD in computational geoscience and worked as a geophysicist in Houston for five years. I joined NYCDSA for the 12-week bootcamp, and worked as hard as I could. I was hired after my first interview, with an offer in hand within two weeks post graduation. NYCDSA has helped me achieve this smooth transition into a brand new field in just 3.5 months.

How I made the decision to join --

1) The time commitment is right: I was willing to put in a few months of my time through well-designed highly-intensive training, rather than spending a year or so to learn on my own. I do not want to go through a one-to-two-year data science master's program, considering a) I have a computational PhD degree, and b) although many data science theories have been long established, data science platforms and tools are evolving fast.

2) Word-of-Mouth: I have friends in New York working in the data domain recommending this academy over other data science training offerings. "richer content", "up-to-date material", "good instructors" are among the key words that I recall.

3) A balanced focus on teaching and job service: I have interviewed with a few different data science bootcamps. Many of them gave me a feeling that they want me to be 90% ready for a data scientist role coming in, and they are only willing to do the 10% polishing to get me "sold". NYCDSA convinced me with their road map that they will first focus on teaching the content that they are proud of, then switch gear near the end to the job search part. They shared their online pre-work content with me, so I could get ready. I was impressed by the quality of the recorded lectures and coding platform, which further boosted my faith in the academy.

Experience at the bootcamp --

1) The content

The teaching material is well developed and feels fresh. They keep polishing the core content and introduce many newly developed "jump start" sessions along the way. You are well informed about what's new out there while learning all the fundamentals.

2) The instructors

They have a stable teaching team here. Unlike many other camps which keep losing instructors and hiring recently graduated trainees as instructors, NYCDSA has a stable team. The majority of them started working here from years ago when the 12-week bootcamp was initiated.

They are a knowledgeable, friendly and hardworking group of people, with finance, math, computer science, physics background. When they are not teaching, they either help the students or work together on their side projects. It is smooth to learn from people you respect and admire.

3) The fellow campers

A vast majority of the students here have or are working on a graduate STEM degree, with a solid quantitative background. Many also bring in years of experience from finance, health care, software engineering, marketing or other fields. What they all share is a strong will to perform and succeed in data science.

I feel honored to have worked with a few of them on the group projects. We helped each other not just during the bootcamp, but also during the job search period. I am convinced that it is a great professional and personal network to be in, for the long future after our time at the academy.

4) The career service

NYCDSA organizes hiring events for each cohort. You will see quite a few Fortune 500 companies coming to the event, as well as promising start-ups. The NYSDSA career team verify the job vacancies, collect details about the hiring teams, and prepare cohort members individually for a successful outcome (resume, LinkedIn, GitHub, blog posts, interview skills, and many other aspects.) They also utilize their own personal network to get interview opportunities when they see a great match.

They keep supporting and motivating the students during the course of job search. There are rooms set aside for graduates to come back to and work on things. Here you get daily check-in's from the instructing team and helpful discussion with fellow cohort members. I have been enjoying this cozy and welcoming space often, and plan to keep gaining knowledge and energy from this ideally located data science hub.

Advice for future students --

1) Complete the pre-work, have an initial plan for the projects coming in.
2) Work hard during the bootcamp, be curious and independent. Treat it as a 3-month internship.
3) Plan to jump right into job hunting effort right after.
4) When working with wonderful teammates, make sure to deliver your parts; after achieving your goals, remind yourself that you have been kindly helped along the way.

Closing comments --

It has been a great investment. With the guidance, help, and support from NYCDSA, my job preparation and search time frame has been shortened by at least 3-6 months. For people with solid STEM background and strong desire to work in Data Science, this bootcamp should be a challenging and rewarding journey. I would continue to cherish the relationship I have built with my mentors and friends met during Cohort 9 at the academy. I wish them well.

The 12-week data science bootcamp is a highly useful experience both for people looking to transition into data science and those who seek to expand their skills set in their current field. A core dimension of the program is exposing and drilling you in a range of technologies that are very sought after on the market for data scientist/data analyst/quantitative analyst positions, spanning fields from tech, finance, healthcare, marketing, research etc. On this front, a distinguishing feature of the bootcamp is that you work in both either R and Python as primary languages, so that you become proficient in both languages by the end of the bootcamp, which is very valuable. Besides these languages, the bootcamp also teaches staples of database, file management and version control like bash, git, SQL (MySQL, SQLite) and MongoDB as well as in demand big data tools like Spark, Hive, Hadoop, AWS, etc. This is a very powerful set of tools to have under your belt. Certainly, the more familiar you are with these technologies before joining the bootcamp, and the more intense effort you are willing to put into practicing these skills, the more you will be able to get out of the experience, and you must be prepared to absorb new technologies very fast, but at the bootcamp, you get a structured and very supportive environment to help you achieve this. The TAs are excellent, very dedicated, and willing to assist you with any question or problem you may have.

A second core dimension of the program is anchoring you in the mechanics of statistical inference, machine learning algorithms, and data mining. The curriculum is grounded in canonical texts in machine learning and is organized such that you get to cover a lot of ground in a short time. The concepts are explained very clearly, first from a mathematical perspective and then implemented step by step in both R and Python. The instructors have various specialties in which they teach, are extremely knowledgeable and can answer questions at any level of depth.

Tying these first two dimensions together are mostly daily problem sets and five projects, where you get to use the skills you are learning. For the projects, you can even go beyond, learning and adding a technology or skill that may not have been directly taught, if it serves your project. You will have ample support from the TAs and instructors through all this, and your classmates will make a very useful learning community. For the projects, it is helpful to give yourself plenty of time in advance to come up with ideas, but here again your TAs, instructors, and classmates will be very helpful sounding boards.

The third core pillar of the program is job search assistance. During the bootcamp there is a series of job search preparation lectures that include resume review, search and interview tips, interview type quizzes, and industry guest speakers. You will be encouraged, given many useful tips, and get motivated to become very disciplined and efficient in your approach. At the end of the bootcamp, each cohort is invited to a career event focused exclusively on the newly graduated cohort, with hiring managers from a diverse set of companies. You won’t be handheld, and you must apply discipline and commitment to your search, applications and preparation for interviews after the boot camp ends, but the job search assistance from the bootcamp will continue until you find a job, with the opportunity to interview with companies that hold interviews at the bootcamp, referrals to positions you’re applying through the frequent advertisements sent to alumni or to positions you apply on your own, a bank of practice interview questions, interview performance review, and an alumni dedicated place to come work on your search at the bootcamp headquarters available to all alumni.

The bootcamp is a very intense experience and you have to commit yourself to it 120% percent, but you will leave with a skill set that will advance your career, a set of projects and a certification that, in my experience, will attract many recruiters, the drive and inspiration to push yourself to reach your maximum potential, and a support network that will be there for as you pursue your professional dreams post-bootcamp. In addition, in my experience and that of several of my cohort classmates, both during and after the bootcamp, you will become an independent learner in the field of data science who is capable to advance and add to their skill set on their own.  This is an invaluable skill in any data-related role you will hold. In sum, the bootcamp is a highly valuable experience that will leave a very positive impact on your professional life. I am very grateful I made this choice and I can only wholeheartedly recommend it to everyone else.

Going to NYC Data Science Academy is a decision I don’t regret for a second. These were ones of the most challenging 3 months but well worth it. I learnt a lot and got a lot of support that I would not have gotten anywhere else.

As long as you are ready to put in a lot of sweat, hours and effort, you will be successful and do extremely well because you will always get the support of the TAs, staff and fellow students. You are surrounded by a bunch of smart people and TAs who are here to support you and help you grow. 

The fact that NYC DSA selects students with a Masters or PhD degree is a big plus because you end up working with people from whom you can learn tremendously. Their experience and background make the bootcamp that much more interesting.

The curriculum is solid and half of it is dedicated to machine learning. Some bootcamps only dedicate a few weeks to machine learning which does not make sense to me given that it is the core of a data science position. The curriculum keeps evolving based on the feedback the students give every week during the pulse check. 

I believe that you won’t have any difficulty finding a data science position after attending the bootcamp as long as you have the drive and treat the bootcamp and your job hunting as a full time job. 

Also, NYC DSA offers a lot of help in your job hunting. The last 3 weeks of the bootcamp are dedicated to helping you with your job hunt (don’t worry you’ll still be working on your data science skillset in the meantime with probably the toughest classes of the bootcamp happening at that time too…). You’ll receive a lot of support to find a job from the staff and they will prepare you for interviews.

All in all, get ready to work hard and if you do, this will be one of the best decisions you will ever make to advance your career in data science