NYC Data Science Academy

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.

I got a lot out of this course but the curriculum is very challenging. Calling this a beginner level course is overly optomistic. It's basically just a list of code examples.I have years of experience teaching technical material in statistics and research methods and have learned that it's generally not helpful to just dump a bunch of information on students without explaining the relevance of the information through practical and intuitive examples. There is too much emphasis on basic comp sci and not enough explanation of why understanding these principles is even relevant. Why do I need to write an algorithm to test if a matrix is a magic square or calculate roots to analyze data in python? You really don't. Teach the essentials coding techniques needed to analyze and visualize data first and focus on only the most critical material. Save the computer science for a computer science class. 

I was a member of the January-April 2016 cohort, and I have fond memories of the experience, difficult and stressful as it was. The instructors and TAs are overqualified and brilliant, and Christopher Makris gives the broadest and deepest lectures that time affords. Zeyu is a magician. I would call it comparable to a master's program in machine learning, if the student puts the necessary additional time for self learning and independent study. The bootcamp was one of the most informative, rich and interesting experiences of my life, but it comes with several caveats. For one, this isn't grade school, so you are expected to learn from trial and error on your own and be comfortable with mastering theory as well as execution. The staff are there as a complementary resource, and shouldn't be relied upon 24/7 as a crutch for lack of ability to work independently. In other words, you get out of the bootcamp exactly what effort you put in, and you should be able to figure out the gaps on your own.

This brings me to my next point, probably the only complaint I have with the bootcamp--the lack of selectiveness in admissions. Management is responsible for choosing a group of students that befits the brand exclusivity, and in my view admissions is not selective enough. This may hurt the camp in the long term. Several people came from non-technical disciplines and were very much able to learn quickly, but there were a couple who saw the instructors are their own personal tutors and significantly slowed the lecture process for others. If you have to ask a question every 10 minutes that interrupts the class schedule, or spend hours with TAs only to forget everything and have to repeat repeat the personal tutor process again, you should not apply here. It's not fair to other students. You will monopolize instructors' valuable time. There is no magic fix for becoming a data scientist, and after school age you should be able to learn on your own.  In the age of the internet, there is no excuse for not being able to use Google. What I noticed from our cohort was the less someone knows, the more they talk. This is more a problem of the admissions officers/CEO than of the students who do not fit in; they should foresee these kinds of problems in the interview process and make sure that whoever gets in is technically competent. People who see this as a quick entry to becoming a data scientist should also be aware that not everyone who learns to program will be a good data scientist, and you won't simply be offered a job afterwards. What is instrumental in your career post-bootcamp are your original skills and experience. It is not a way to expedite the job search if you have recently become unemployed. 

The interview process post-bootcamp is also autonomous, and you shouldn't expect to be given many interviews automatically unless you manage to find contacts on your own. Your projects are your own personal portfolio, and being self reliant on your ability will serve you better in the long run.

To summarize, the main lecturer is brilliant, an amazing teacher, who covers as much as possible in the limited time. You will learn more than you ever expected. The TAs are a major resource, but the main weakness is the admissions process. And lastly, if you can't learn things on your own, don't sour the bootcamp for others. There are many online courses, such as Coursera, which will be better for you. 

I have mixed feelings. The students are really the best part about the program. They have such an eclectic background that I learned a lot from them. The classes themselves are at best, overpriced. The materials and intruction is roughy on par with what you'd find in Coursera courses. I don't regret the program and I did learn a lot, but I question the value for the cost. That said, it probably depends on your needs. If you need time where you solely devote yourself with like-minded individuals, it might be worth it. If you place emphasis on the curriculum and instruction, there is a slew of resources out there that are at least as decently taught (if not better), but at a much better value. Also, the curriculum states there are advisors, but although they may be advisors to the program, you'll only see one or two of them for a short 30 minute talk at the end of the program.