NYC Data Science Academy

I really enjoyed the Data Analysis and Visualization course.  I took the Intro to Python class with Tony a couple months ago, and this course (also taught by him) was a great next step in my journey towards hopefully becoming an expert in Python.  Whereas the first course gave us a great background in the basics, this one gave us a more concentrated ability to apply what we had learned to real-world problems (analyzing data and being able to display the results, which is more what you would do in a work environment).  Also, the opportunity to put together our own project at the end of the coures is extremely useful.  Highly recommend this course!

I took the Deep Learning Course from July - August 2018. This is a part time course for 5 weekends. Jon Krohn is an amazing instructor. I am a Web Engineer and have zero to little idea about Deep learning. For me to sit in class and not be intimidated by the course and fundamentals of Deep learning is a testament to Jon's teaching. The course is a really good starter kit for anyone looking to get into Deep Learning and AI in general. There is some knowledge of python that might be useful to have prior to taking this course as there are many live exercises worked on python. You will be given an opportunity to complete a project during the course which I suggest you do as it will make you apply the skills you review right after class that will make learning the concepts more enjoyable. All in all, I will 100% recommend this course for anyone who is looking to get a thorough overview of the fundamentals and philosophy of Deep Learning and its future.

The Intro to Python course was great!  Tony is a fantastic teacher who really took the time to get us acquainted with a new programming language, while simultaneously making the course challenging through both the in-class exercises and the homework assignments.  I felt the course was especially well structured, as each part really built on top of the last to provide us with a clear understanding of the basics of Python.  I highly recommend this course to anyone interested in starting on a path towards Python fluency.

I did not have experience in programming before this course. Had taken some courses in Java and pearl almost 9 years ago. I have a healthcare background and I was worried if this course will be advanced for an entry level. I can say now with confidence that this course not only taught me the basics of python and data analysis but also established a foundation to which I will be able to add advanced concepts in the future. I like the way the course was structured. Tony was punctual and the class always started on time. Tony encouraged us asking questions and was patient in explaining the concepts. Overall very happy with my decision to take this course and would recommend to anyone who is ooking to jump into the field of datascience.

I could not be more satisfied with my experience at NYC Data Science Academy. I met incredibly impressive and interesting people, learned more than I ever thought I could, made friends, and worked very hard. Within a few weeks after graduating, I was offered my first full-time job as a data engineer. (I do not come from a CS, math or stats background).  

NYCDSA is very succesful at getting their students fluent in the tools and technologies of data science, and prepared for finding great jobs in the field. The bootcamp is exhilerating, and the people are truly the best. By the end, you will be ready to for a data science position, and you will have broadened your horizons.

I took the 12-weeks bootcamp at the NYC Data Science Academy. I am a postdoc in India. I had been writing graph algorithms for social network analysis and analyzing data using MS-Excel. I decided to extend myself to data science from data analysis.

I researched for about six months to choose a proper academy to learn data science. I came up with a decision to join NYC Data Science Academy, because I can learn both R and Python within three months. But, I learnt more than I thought. I gained hands-on experience in Python, R, SQL, Spark, deep learning, Hadoop and Hive.

The materials are technically sound for job interviews. Many experienced professors and industry professionals design and teach the materials. The course laid a solid foundation in data science. The people in the academy show keen interest in every student to understand the concept, in resume writing and above all, to get a job.

I attended the 12-week boot-camp at NYC Data Science Academy from Sep. 2016 to Dec. 2016. Before that I got my PhD in Chemistry and worked 6 years' as a Post-doc in biochemistry. 

The curriculum was well organized. 12-week courses covered R and Python programming, statistical analysis and machine learning. During the last few weeks, big data tools like Hadoop and Spark were also covered. For one coming from academic background, the breadth of topics and difficulty level felt just right. The boot-camp courses weren't supposed to teach you everything, but they did prepare me very well if I wanted to explore further data science topics.   

The boot-camp was intense. I was only sleeping for 4 hours during boot-camp days. It was demanding both physically and mentally, but I wasn't alone in the journey. The instructors, TAs and classmates were all amazing. This boot-camp takes students from very different backgrounds, which I think is a unique advantage. I was able to see people applying data science tools to their own expertise brilliantly, fashion, marketing, IT, health care... It was very helpful for me who was looking to step outside of academia. 

The job assistance is good. There is networking, resume-editing and mock-interviews. Vivian also personally helps students tracking their job hunting status. I wasn't able to use lots of their services due to my personal situation, but for the one position I applied for I was able to impress the hiring manager with the machine learning knowledge and skills I acquired at NYCDSA and got an offer the same day. 

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.

The approach in this course was more different that what I've seen in most workshops on this topic. It was more visual because Deep Learning sometimes requires mathematical derivations to understand the algorithms. I have read a few seminal Google papers on the topic and they're not alwasys easy to follow. John did not dive too much into this area and preferred to use powerpoint slides and diagrams by hand to explian it before we looked at the code.

Overall the coding portion was made easier by John's coding style and we were able to follow along with the Keras modules. However, we did not get to cover all the topics (like autoencoders or deep belief networks) that I've seen other presentations get into and I would have liked to learn more. But the foucs on NLP and the last topic on GAN got me more curious about those topics. I would recommend it to those who want to learn it more and I would even be willing to talk about what I tried as a result of taking the course in any future session that he runs with a new group.