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.

NYC Data Science Academy was my first choice because of my interest in data science, and their reputation as the best data science program. They did not disappoint.

As a recently separated veteran with a master's degree in physics, I was looking for a way to renew my credentials for employment in the technology sector. A friend of mine told me about his successful full-stack engineering boot camp experience and subsequent employment, so I began doing some research. I did not know what data science was, at the time, but I read about it on Course Report, and it seemed like a perfect fit for me, so I signed up. 

NYCDSA is exactly as advertised: an intensive program on data science methodology aimed at students from a wide variety of backgrounds. With my background in physics, math, and C++ and Java coding, I felt that the Python and R syntax taught at NYCDSA was not too difficult. Other students were less prepared but often did okay anyhow. The course began with Python and R syntax for handling and visualizing data, and then continued with machine learning methods in both languages as well. SQL and Docker were also covered, but rather briefly. At the end of the course, deep learning and Tensorflow were introduced, as well as Spark and Hadoop. I would have preferred that more time be dedicated to those topics, but this is a three-month boot camp, so time is limited. Nevertheless, given the allotted time, I feel that I have achieved an incredible amount, and I'm very happy with my experience. Boot camp is not cheap in either time or money, but this one was well worth both. I intend to continue building on the knowledge, experience, portfolio, and network that I have accumulated at NYCDSA as a professional data scientist.

I needed the shortest path to pivot from a research position in physics to a career in data science.  I considered grad school, but I have a family, and I need to be there for my kids during their developmental years.  NYC Data Science Academy offered an effective restructuring of skills over the course of three months.  Not only did I learn skills in data sctructures, statistics, coding, and machine learning, but I also learned skills necessary in the job-hunting process.  The support that was given to me during my time at NYCDSA was instrumental in receiving a job offer from a well-stablished company in financial services.

Enrolling in the NYCDSA is easily one of the best decisions I have made, not only in my professional career but also in my life.  Being of a statistics background, I knew that programming knowledge was critical when trying to utilize math to solve real-world problems. During my undergraduate years, virtually everything I did mathematically was done by hand. This was a massive constraint because although one may be capable of solving a problem correctly by hand, it is difficult to truly understand how to it is used in the real-world without any true form of automation. After a lot of research into graduate schools, bootcamps, and program offerings with a Data Science focus, I decided to go with NYCDSA based on the consensus of positive reviews I received. During my time there, I was refreshed on advanced statistics, learned a plethora of programming languages that I now feel very comfortable using to build large-scale projects, and even developed my presentation skills tremendously since every project had to be presented in front of a class that included a panel of highly-skilled Data Scientist. Consequently, this helped me feel confident throughout my entire interview process and land my dream job in Data Science. They say hindsight is 20-20, and although I couldn't have said that I was sure this was going to be the outcome coming into the program, I can say now that it was easily one of the most pivotal decisions I have ever made in my entire life.