NYC Data Science Academy

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.

This is a very conprehensive course covering R data analysis and visualization techniques. I can't remeber everything I learned in this class, but whenever I encountered data project in my work and study, I always went back to check the slides, examples and libraries provided by this course. 

Vivian is a very passionate instructor and an excellent data scientists. She offers many hands-on example in class, not just reading and showing slides. She is very serious about the course and push students in a "hard" way. She encouraged all of us to try, I can't get lazy because of her.

In general, this is a very postive experience. Just squeeze some time in the weekend, you will be amazed how much you can learn within 1-2 months.

I have posted a review also in a different website - but also wanted to share my opinions on this website for others to consider.

I took the Beginner R course, which was not that great reflecting back on it. In whatever class you take, the teachers make most of the difference, and in my case, we were not taught well. In the end, I cannot even remember if I have absorbed anything at all except for doing basic statistics in R. We were told that Vivian, the founder, would visit, but she never visited. While doing self-study on free R online courses such as EdX, I discovered that the materials we received in this course was similar to what I found in these free online courses about R.

In the end, we presented our projects, but all of our projects were half-baked, because we did not absorb enough knowledge about R to prepare and present. Although my project was really using the bare basics of R, such as basic statistics (e.g. average, max, min), Vivian said that I did a great job and that I should sign up for the R Intermediate course. She kept on pressuring and asking me when I can join, and I later realized that this academy was really focused on making money off the data science buzz rather than really teaching students about how to master data science.

Learning to code requires a lot of work - this class gave me the push to invest the time necessary in a structured manner. I found the lecture notes and homework problem sets very useful. Compared to content found on MOOCs and general online searches, the class curriculum is a lot more practical and concrete. 

Our instructor Liz is very helpful and accommodating. The classroom environment was open to questions/discussion, and Liz was very willing to take the extra step to help me improve my final project.  

PROS:

- Very strong curriculum coverage - maximizes the hours that we had in lecture quite effectively. I was able to do a lot more at the end of class than I originally anticipated, and I now have a base of knowledge to apply it to my daily work

- Great schedule - it's hard to find a weekend class for those who work longer hours on the weekdays 

SUGGESTIONS FOR THE FUTURE:

- More problem sets / heavier homework. I learned a lot through the homework and in-class problem sets. Having more problems / worked solutions, even if optional, could really help reinforce concepts faster    

- I do think people came in with different amounts of knowledge/background experience. Maybe one thing to help equalize the starting field is to assign some 'pre-work' / 'pre-reading' ahead of the class so that a baseline can be established and more time can be spent on the analysis/visualization portion 

The staff are friendly, and well prepared.
This bootcamp helped me to consolidate and organize my knowledge. 
I feel that they gave me exactly what I was looking for, before the bootcamp I was not so sure about my models, now I feel great doing what I do. This is priceless. 

If you have any doubts about those guys, please don't. They are super honest people, for sure this is a trust worthy organisation.

Thank you guys, if any of you from NYDSA read this.

Felipe.

I attend the bootcamp because one of my friends' recommendation that the bootcamp is a great place to get hands on experience of data science. Actually this bootcamp is as good as I expected. The bootcamp offer everything related to data science, not only the advanced part such as machine learning and deep learning, but also fundamental skills like Pyhton, R and SQL. After the bootcamp, I feel like I am prepared to apply for data science job. 

I will never forget the 3 month study experience which is owesome. The teachers and assistant here are very knowledgeble. The classmates are also great who have very strong acdemic/working experience. People here are PHDs or masters with around 2-10 years' working experience. I would highly recommend NYCDSA.