NYC Data Science Academy

It is a fast paced course. Five weeks of 4 hour sessions but two of them start an hour earlier to go over homework. There isn't so much as a bathroom break so exercise caution with oversized drinks. But in all seriousness it was a very professionally run course. The instructor Anthony Schultz kept things very engaging. His energy didn't waiver or segue into something irrelevant. Anthony was also highly available. He was there early before class to answer questions and available after class as well. He was even responsive to questions I had about the homework pretty quickly. I would recommend taking this course. I found it fun. At the end of the day you really get out of the course what you put into it.  
The class size was small for my group- maybe 10 or so students- which was nice. I was also impressed by the space of NYC Data Science Academy. Along with the main classroom, the space has a lounge area, coffee and tea area, and a gathering table. There might have been some pictures online but I never saw any. It just made it feel like a more comfortable environment.

I attended the 3 month Data Science bootcamp. It was a valuable experience: the staff are very knowledgeable and helpful, Christopher Peter Makris is a genius, and you learn a lot of practical skills. However, if you are not American or Chinese, do not expect Vivian to lift a finger and help find you a job. She will make a lot of false promises but she won't deliver.

She also has a talent for firing all of her staff, including the most hard working and talented ones. She is crazy and has a terrible reputation.

This bootcamp differentiates itself in its teaching of the important theory and statistics. Christopher Peter Makris does an exceptional job of boiling down complex topics into understandable concepts without sacrificing rigor. If CPM is there, you'll learn a lot. I left with a real love of statistics and machine learning theory.

Without him, there isn't much offered here that you couldn't get on Coursera. The job placement is helpful if Vivian decides she can help you. She basically helps the PhDs get good jobs so she can post the success story, but doesn't spend as much time helping the younger students get admittedly less sexy jobs. 

I took Data Science with R: Data Analysis and Visualization with Derek in Fall 2016 and I would like to share my experience with the course. Although I will try to stay objective, my observations may be biased as I established and maintained a positive relationship with the instructor throughout the course. Also, as tempting as it is to discuss extraneous details, I will refrain out of civility.

This course is designed to provide a comprehensive introduction to R and it delivered. From the syllabus that articulates clearly defined learning outcomes to interactive exercises that check for understanding, the course followed a predefined timeline and I walked away with a sense of how I can continue my education in data science. Although students came from various backgrounds, the instructor continuously tried to find the middle ground to provide as much personalized learning as he can.

Likewise. Derek is a working data scientist who uses R on a daily basis. As a result, he shares plenty of relevant examples to contextualize various approaches and explain why certain measures are in place. In addition to brute memorization, which is an inevitable part of learning computer science, he tries his best to help you understand the logic and make you think and approach problems like a computer scientist.

To balance the scale, I will comment on areas for improvement. I felt that the course did not have an appropriate adaptive learning measure in place. The teaching material was rather outdated and I sensed that it had not been evolving in proportion to time. However, the instructor was experienced enough to fill the gap. In addition, there seemed to be a disconnect between the instructor and the institution. Throughout the course, there were a number of instances where the instructor seemed unfamilair with the material he had prepared.

In conclusion, I sincerely enjoyed Derek's class and I suggest considering this class if you are trying to start the first chapter of your journey in R.

I would recommend this program to people who are interested in starting or enhancing a career in or related to data science. The program covers a wide range of topics and they constantly add new materials so you can learn the tools that have high industry demand. I just started working as a data scientist and engineers at my company are learning these new tools as well. 

I highly recommend the chief instructor, Chris, at this bootcamp. He is a talented teacher. Those who have prior experience taking a machine learning or a statistics class would understand that it is not easy to have a good instructor. I took statistics and machine learning classes at university, but Chris surprised me with better ways of understanding statistical concepts and advanced algorithms.

Everyone enters this bootcamp with high expectation in education and career assistance, but you are the only one who can choose to get the best out of it. The TAs and instructors are very helpful when you have questions and they provide guidance to the right tools and methods. I suggest anyone who comes to this bootcamp to be prepared to work hard and learn a lot of new things in a short period of time. 

A final suggestion to those who are interested in becoming a data scientist. The most important thing that I learned from this bootcamp is modesty. Modesty is critical because as a data scientist, you never want to assume results. Also, data science is developing field and requires continuous learning even after the program. I would recommend this bootcamp to anyone who is ready to join the exciting world of data science.