NYC Data Science Academy

I graduated from NYC Data Science Academy in September 2019. I was recommended by a friend of this bootcamp, as he told me that I will be able to learn a full spectrum of skills needed to enter a data-related job. Having almost finished my Master in Statistics, I wasn’t sure about the tools needed in the industry, because the master program focuses very much on theory but not on the practical tools.  Hence, I was very glad to be introduced to a toolkit of data science skills, those of which will be very applicable to any data science/engineer jobs in the future. One thing I really liked about the program was its intensity. I understood that students coming to this bootcamp came from different backgrounds, some of which might find it a bit challenging. For someone who has background in statistics and a little bit of computer science, the pace was very good because I could learn a lot of things within 12 weeks, bombarded by one project after another. I really liked the instructors here, especially Luke Lin and Zeyu Zhang, as they were so knowledgeable and patient. I also made many friends here, especially for someone who just moved to New York City. 

NYC Data Science Academy’s program covers more material than any other leading bootcamp. Curriculum is created with long-term professional development in mind, while at the same time being incredibly “up-to-date” with all the latest developments in the industry. Thanks to the things above, bootcamp positions students for immediate success right after the completion of the program, while keeping on serving their career in the long run. In other words, NYC Data Science Academy’s curriculum includes some extra components, which are essential for getting a job at the top companies in the industry (e.g. FAANG). 
In addition to the core of the program, NYC Data Science Academy also offers practical and extensive preparatory material (already included in the price of the bootcamp). Thanks to these materials students are capable of building a strong foundational knowledge of the core material, and as a result, have a much easier time during the program.
One of the greatest advantages of NYC Data Science Academy’s bootcamp is the group of extraordinarily supportive teaching assistants. Since 100% of teaching assistants are former bootcamp students, they possess a unique understanding of anything that current students are going through at any given moment. 
Another pivotal part of the program is its amazing collective of instructors, most of whom possess advanced degrees and multiple years of industry experience. No other place creates such a remarkable mix of academic and industry knowledge that allows students to not only learn how to code but to understand the underlying principles of the scientific and business processes. 
Last but not least, is the selection of projects that students have a chance to complete during the bootcamp. These projects not only allow students to apply newly-learned coding techniques but also push them to solve “real-life” problems and to create industry-specific practical solutions. Bootcamp has a healthy mix of individual and group projects, which gives students a chance to work with different kinds of dynamics, and most importantly to learn from fellow students. 
Overall, the unique selection of material, amazing teaching and supporting staff, along with the ability to be among a diverse group of people with all sorts of professional backgrounds is what distinguishes NYC Data Science Academy from the rest.

Before thinking about any kind of bootcamp, my background was in finance and I wanted to brush up on coding skills and my statistics knowledge. Once I decided I wanted to explore careers in data, I spent an insane amount of hours researching and talking people. I was scared to leave my job without any guarantee of getting a new job. Ultimately, I chose NYCDSA, and looking back, I am very grateful that I did. The people that I talked to from admissions were friendly, informative, and helpful. They were empathetic about the fact that this was a big step and they were not pushy or sales-y at all, which was very refreshing compared to some of the other bootcamps. During the bootcamp, the instructors and TAs were very invested in the students. They helped me think through project ideas, review concepts, fix a bug, and all of the above. I came out of the bootcamp with Python, R, and SQL skills along with a portfolio of projects that I wasn't embarrassed to talk about in interviews. The hard part of networking, interviewing, and studying for a new job doesn't go away - but NYCDSA gave me the tools and the job support to feel confident that I can do it. I've learned so much more in the 3 months than in the one year I tried self-studying for a career pivot. I've also met a great group of friends and people while doing so. Even with COVID, which forced our cohort to go online for the last 2-3 weeks, I was able to get a handful of data scientist and data analyst interviews.   I went in with the goal of making a successful career pivot and they've helped me accomplish exactly that as I accepted an offer at a new firm which I'm very excited about. 

I have nothing but positive things to say about the instructors, curriculum, job support, and community at NYC Data Science Academy! 

From day 1 I felt welcome and part of an extended family. While the curriculum is incredibly challenging; the instructors, classmates, and even alumni are ALWAYS there to help guide you through it. 

Like most things in life, you will get what you put in. If you work hard and embrace the collaborative community NYCDSA has built, you will be incredibly well-positioned for success in the Data Science industry.

I secured a role as a Data Analyst at FanDuel Sportsbook 2.5 months after graduating from the program. We have now hired another NYCDSA graduate and will be looking to continue to build this pipeline in the future!

Thank you NYC Data Science Academy for changing my life. 

 I picked NYCDSA because it offered the most comprehensive curriculum. Surely, the amount of content introduced was overwhelming especially in the second half of the bootcamp but with unwavering dedication and commitment, which was pretty ubiquitous and contagious in the bootcamp, the workload was just about manageable. I would describe myself as having a relatively strong quantitative background as I have a PhD in physics (though my knowledge of statistics and coding in general is abysmal), the coursework was still challenging. I would also confidently say that the coursework is taught in a way to be conceptually accessible by all. The bootcamp teaches two popular languages (Python and R, some SQL) as well as the core concepts of machine learning. The machine learning topics are taught with a good mix of qualitative and quantitative reasoning. The students are required to do four intense projects along with many exams and homework. Overall, the bootcamp sets up the students well for future success-- it gives us the foundation necessary to continue to grow as data scientists. The job support is unexpectedly great. The bootcamp works with us personally to become competitive candidates for the job market and also actively makes work connections for us. The instructors are beyond amazing as they rise above expectations always. They make you feel that they are truly responsible for your understanding of the topics and they are very open to feedback (in fact, they actively look for it). In hindsight, as a more experienced data scientist and knowing that there is only 12 weeks, I wish that we had focused purely on Python and SQL, and spent more time on coding challenges, algorithms, case studies, AB testing and big data techniques. There were duplicate content taught as we switched between Python and R though for some this may be good because repetition of topics with a different spin can help with better understanding and retention. And despite saying I wish we focused purely on Python, working with R and having the knowledge to use R Shiny was what ultimately made me more attractive as a candidate since I was able to showcase my work as an app. All this back and forth rambling probably just means that 12 weeks is very short and given that the field of data science is growing so quickly, it's really hard to gauge what exactly and what amount to master. Given this situation, if I turned back time and had to decide all over again where to invest my 12 weeks of time, I would without any doubt pick NYCDSA. With their expertise, receptiveness, hustle and attitude of wanting to do what's best for us, I truly believe that any student there will be in good hands. 

I was in one of the online bootcamp cohorts. Before I registered for the bootcamp, I just finished my graduate school in a biomedical related major. I liked doing data analysis, but I quickly found out that there was a gap between what I knew and what the industry wanted. Therefore, I decided to choose a bootcamp to ramp up my technical skills. I decided to join NYC Data Science Academy because it offered a very competitive curriculum that included Python, R, SQL, Spark, Hadoop and Hive. The projects we did, which includes Rshiny app, web scraping, machine learning, and a final capstone project, are a good portfolio that students can present to potential employers and those skills can be directly applied to work. 

 It was hard to keep up as I was also doing a full time job while attending the online bootcamp. NYCDSA provided many service to keep students on track. One of the service I like the most is the TA assistance. I was paired with a TA and we met regularly to go over my progress and the questions I had. My TA was also a NYCDSA student before and was working in the industry as data scientist. Therefore, he not only knew what my struggles were on the course work, and he also taught me a lot of practical experience that was using in the industry.   

I took the 2019 summer bootcamp at the Academy, and chose it over other bootcamps based on the excellent reviews and ratings. I was not disappointed. Aiko, Luke, Alex, Drace and Michael were terrific teachers, and taught us Python, R and Machine Learning from scratch. The bootcamp is three months long, but because there is a lot of material to cover, it is incredibly fast paced. The material was however covered in a thorough and accessible manner. The Academy also does a nice job of preparing us ahead of time for the volume of work to come, by having the students do a lot of prework before the first day of class. In addition we get excellent and detailed course notes (which I refer to all the time). Overall it was a fantastic experience, and I strongly recommend it to anyone looking to build their skills in data science.

I chose NYCDSA because of its focus on data science applications in both R and Python and because of the quality of the faculty. The program lived up to every expectation. I came from a quantitative background, but the program did an excellent job of providing material so that it was accessible to people with a less quant heavy background, while also allowing people with a quant background to take a deeper dive. I was looking to switch from finance to a data science role, and this program helped me bridge the gap. The program is intense but deeply gratifying.

The Deep Learning course at NYC Data Science Academy provides a solid introduction to the fundamentals and application of the suite of deep learning models. The instructor, Dr Jon Krohn (DPhil, Oxon), has a talent for teaching deep learning in an accessible, conceptual manner and for demonstrating the wide range of applications that benefit from these neural network architectures. The course content covers the basics of neural networks from the basis of neurons, activation functions to shallow / dense layers and to more advanced models including convolutional neural networks (CNNs), long short-term memory (LSTMs), auto-encoders, Generative Adversarial Networks (GANs) and reinforcement learning. Course readings are taken from the recently published Deep Learning Illustrated book, co-authored by the instructor. There is heavy emphasis on coding using TensorFlow 2.0 and Keras in Jupyter Notebooks along with an introductory lesson on PyTorch. Dr Krohn has a very extensive code repository featuring a myriad of deep learning architectures. The lessons guide us through model code line by line, gradually building up in model complexity across the weeks. Dr Krohn discusses model tuning and addresses common model fitting pitfalls including preventing over-fitting and exploding / vanishing gradients. By the end of the course, the students are able to showcase projects fitting several advanced architectures including CNNs, RNNs and LSTMs to interesting datasets for image recognition, speech recognition and natural language processing. This course is highly recommended if you are looking for a comprehensive and stimulating face-to-face introduction to one of the most revolutionary methodologies in machine learning today.

I took Jon Krohn’s deep learning course in the fall/winter of 2019.

Jon is that rare communicator with the aptitude to succinctly explain rigorous and complex topics in a digestible fashion. His course is an ambitious undertaking, but Jon makes the subject matter as accessible as it’s likely to be. After the course, you’ll not only possess a foundational understanding of neural nets, but also Jon’s compilation of resources for your continued self-experimentation. Jon’s approachability as an instructor, his unabashed love of this cutting-edge science, and his genuine interest in seeing his students advance their understanding could not place you in better hands.

I have more than 15 years of experience working in supply chain and I took the bootcamp with the idea of updating my data analysis skills and obviously also learn about machine learning, clustering, algorithms, etc. and I can say that it was totally worth it. The material, explanations and projects are really very interesting and challenging.

The main reason I choose this school was because it was the most professional one, most of the teachers had been there for a long time and they don't switch instructors in the middle of the cohort as most other schools.

A normal day is, usually, theory for about 3 hours in the morning and then practice and homeworks in the afternoon.

The part that I liked more is that the school really support the students in making sure we learn but also are able to create a very friendly and collaborative environment. I made really great friends!!

The reason I picked this Bootcamp over the other's was that after talking to the admission office they seem to be really serious about what they were teaching and the people they admit to their program. In my case, I was looking for a challenging environment where I could develop my programming skills and learn new skills in the data science world and the academy helped me achieve what I was looking for and much more.

The academy instructors are very well prepared and passionate about what they do. The only downside is that given only 12 weeks is hard to understand every single thing they are teaching, for example, one day you are learning Random Forest and the next day you are learning something different. But something that they planned really well and is really helpful is that since you are learning Machine Learning in R and Python when switching to the other language the theory remains the same only the code changed, so in a way, you are reviewing theory twice.

The whole experience was incredible, the people that attended my cohort were very smart people coming from different backgrounds like finance, physics, business, engineering, to name a few and from top universities like Harvard, Oxford, Cornell and also different countries which made the whole experience much more interesting. What I enjoyed the most was that working with these different people made you look at data from very different perspectives and in result have a richer analysis of a simple dataset.

After the Bootcamp feel ready to start looking for a data science job, I was already in a quantitive field but this program has really helped me gain that confidence I needed to have a career change.