NYC Data Science Academy

5-week class with Anthony Schultz. Solid class in acquiring the basics of python coding. Methodical weekly build coupled with Tony's patient approach made for a good base of python knowledge to move forward. Option of extra time before class to go through assignments or ask questions was very helpful. Does require concerted personal time to solve homework assignments for those with no coding background. Overall, would solidly recommend. 

Tony is a great insctuctor and makes basic Python concepts very accessible. My background going into this course was primarily in SQL, but I felt that the necessary Python concepts were covered quite well in the first session.

The course provides an overview of the various uses and popular packages in Python and each week focuses on one area. I would recommend getting as much practice as possible early on with basic concepts like for and while loops, as this will prepare you best for the later weeks.

I enrolled in this course with some working knowledge of machine learning, but no prior experience with neural nets or coding in Python. I thought this course was a great introduction to the topic, especially given that we were able to preview the instructor's forthcoming textbook on deep learning. Being able to go back and forth between the book and the lecture notes, and walk through the code together as a class, was really useful for solidifying newer concepts. The instructor was really approachable and friendly, and was good at drawing from various strengths that were present from other participants in the class. A great course, I enjoyed it. Would recommend to anyone who wants to get a high level intro to deep learning in a more structured setting than just an online course. 

Attending NYC Data Science Academy is one of the most important and accurate decisions I have made in my life. The academy provides strong training on statistical and machine learning theory. Coupled with 3 real-world problem solving projects and 1 business-oriented capstone project, I gained hands-on insight on how data science address business demands.

With software engineering background, I always want to relate my software knowledge to solve business problem. I believe the training at NYC Data Science Academy help me step forward my career path closer to what I want to achieve.

I wish I can attend NYC Data Science Academy at a much earlier date.

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.

Tony was a great instructor for this course - very knowledgeable and personable. However, I wish the course was spread out over a larger timeperiod so that we could have more time to digest the material. Each session was four hours long - I can't remember a time when I had a four hour class and expected to focus for that long. My learning could've improved by having more frequent, shorter length classes.

The Data Science Machine learning class can be overwhelming but you end up learning a lot if you work hard and do the homework! The class goes very in depth through all the types of machine learning methods and advanced methods. It gave me a great perspective on what is out there to master.

This class is overall very helpful. It emphasizes the data visualization part, which was more than I would have liked, but still good. It covers lots of material in a short timeframe, and it requires some after class time to work on and get through.I am able to apply what we covered in the class in my daily job. 

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.

Great course! The slides were clear and the content was very useful. Plenty of opportunities to practice and work in groups. Derek was a great instructor, allowing plenty of time of questions and making the course very interactive. He was also always available to answer questions in between classes and help us with work related projects as well. I have learned a lot and would definitely recommend this course.

This course provided all the fundamentals and resouces we need to learn data analysis and visualization. The instructor was approachable and helpful when special assistence was needed inside and outside of classroom. Even though the five weeks course was intense but I'm a pleased to receive after class assistance and was encouraged to learn continuously. I hope to receive job assistance and look forward to seeing support in this area for all students and alumnis.

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 course reallly gave me the foundation for understand R and applying it to real world data. Studying programming syntax can be challenging and Vivian really encouraged me to complete the final presentation. I am so glad I did!! I now have the confidence to work in R and use in the workplace. Thank you NYC Data Science Academy!! See you again soon.