I attended the Introductory Python course at NYC Data Science Academy taught by Tony Shultz. I really appreciate his style of teaching - it was a great balance of theory and practice. He also brought a lot of energy to the class which made a big difference especially as it was in the evenings after work. He also takes the time to go through the HWs and clarify students' questions - he is clearly very engaged in teaching and makes a lot of effort to support his students. 
I highly recommend this class.

Jon Krohn gave a wide overview of basic deep learning models - convolutional, recurrent, some unsupervised learning approaches, etc. This is obviously a lot of material and there are only 5 sessions, so discussion was typically sacrificing depth for breadth.

As a starting point to understand DL, e.g. for someone with traditional stat/quant background, this is a great course. Jon explains the basic mechanics of a neural network down to matrix transformations. 

On the other hand, if you already understand the basic DL and are looking to develop practical application skills including TensorFlow, this course may be a slight disappointment. 

The course “Data Science with Python: Data Analysis and Visualization” is perfect if you are fairly new to “Data Science”. It begins with a general overview of basic knowledge in python before going to the analysis content. You will learn Numpy, Pandas, Visualization and Seaborn after you finish the course. The instructor Anthony Schultz introduced every chapter in a slow and comprehensive pace. The course notes are well organized. You can ask any questions during the class and Anthony will explain your question with great amount of details. I would love to recommend to take this class if you are planning to pursuing your career in Data Science. Five Stars!!

The teacher was able to properly explain all the concepts. Very easy to follow and understand. Homework looks challenging but if you listen in class, it's doable. Would definitely recommend. 

You can read books about deep learning and in fact the instructor Jon Krohn has a new book out as well. However, there is a gap between reading and understanding conceptually and writing code to solve a real-world problem. This course completely fills the gap.

The instructor does give you the conceptual foundations, assuming no prior knowledge. I did have some prior knowledge, but still the class is self-contained.

I would say 30-40% of class time is spent discussing code that solves a practical problem. I think this is the perfect balance: you can't delve more into code without a global understanding why all those parameters may be required and you can't delve deeper into theory without neglecting the practical question of "where do I begin".

The instructor is very personable and easy to approach about his own experience in the industry.

I came across NYC Data Science Academy when I was looking for certification courses with classroom learning and I am glad I made a decision to take it up. It was a well planned course which was spread over five Sundays. Our instructor, Anthony Schultz, was extremely knowledgeable and passionate about teaching Python which made the whole course a very enjoyable experience. The course content had in depth coverage with examples to elaborate each concept, giving us the opportunity to learn as we progress. By the end of this course, you would get a good grip of Python which you can apply in your daily work.

I took Data Science with Python: Data Analysis and Visualization with Tony Schultz. The course was a wide-ranging overview of basic Python, Numpy, Scipy, Pandas, and Matplotlib (and a little Seaborn) that managed to fit into 5 sessions. The materials were organized and gave nice opportunities to practice. Tony was extremely helpful and made all questions welcome.

The class was concise and impactful. I really enjoyed the topics covered and thought that the homework was really beneficial in driving home the coursework. The pace of the course was pretty reasonable and I was able to follow the curiculum very well.

I really enjoyed the contents and the samples that Anthony gave us. If you have some background about coding and maths this course is for you to start to build up your skills with phython.

I chose the NYC Data Science Academy over other bootcamps mainly because of its outstanding reviews as well as the fact that it offers its courses simultaneously in R and in Python. 
After working through the extensive prework in Python, R, and SQL (an absolute must if you do not have any previous coding experience), I was perfectly prepared to attend the bootcamp.

The bootcamp itself offered far more than I had expected. Phenomenal teachers that will give their everything to help you understand the topics, TA's that are always there to help with smaller questions as well as great classmates that made studying a lot easier. Nevertheless, you should not come here expecting a relaxed ride. Teaching data science in twelve weeks requires a fast pace and it will be up to you to put in those hours to complete the homework and review lectures.

The curriculum itself is centered around four projects that deal with all aspects of the data science process, from gathering data (web scraping) over creating a dashboard to working on machine learning group projects. My initial intention coming here was to learn as much as possible as fast as possible and this bootcamp turned out to be everything that I had hoped for.

On top of that, Vivian and the team offered several job search and interview preparation lectures and talks that I have not seen anywhere else. If you are looking for a data science bootcamp that covers all data science topics from start to finish and you are willing to put in the work, this bootcamp is the best decision you can make.

I took NYC Data Science Academy's deep learning course, taught by Jon Krohn. Jon is a terrific teacher, and I would heartily recommend this course! Jon had tremendous enthusiasm and patience for questions while still keeping us on track with the schedule. He really broke things down into bite-size, understandable pieces, while still covering a lot of breadth and depth. I appreciated Jon making available his draft book, this really complemented the lectures. I liked the format of once-a-week lessons because it gave time for concepts to sink in and to practice things with my own data in between sessions. I appreciated that Jon made time to troubleshoot challenges we experienced in our own projects. The course was both a great introduction to concepts and to some of the ways people are applying deep learning; here examples from Jon's day job were valuable. One aspect of the course that was very helpful was that Jon set up a Docker environment for us, and shared very clear instructions for getting our computers set up with it in advance. We were all ready to go from the start. I'm all the more grateful for Jon having set that up after recently spending half of a workshop (run by a different data science academy) wrestling with Anaconda. The Jupyter notebooks all just worked, so we could focus on learning. 

I entered the bootcamp with a background in Mathematics and some previous exposure to machine learning theory. Programming was relatively new to me at the time, so my first course of action was to familiarize myself with Python and R. Thankfully, NYC Data Science Academy provided over 30hrs of pre-bootcamp material (video lectures, in-person lectures, and textbooks), which included coding in Python, R, and some introductory statistics. The program kicks off with a small overview of the tools needed to begin studying data science (mostly to bring everyone up to the same page), and then dives right in to data visualization as the first major topic of study. There is a strong sense of camaraderie with students throughout the program, and there is a strong family-like atmosphere in the space. Most importantly, the curriculum is very strong, and the instructors have deep practical and theoretical knowledge on the material covered. I found it a privilege to study under some of NYCDSA's staff, as they really are the cream of the crop. On top of a strong curriculum, there are many career workshops during and after the program is complete. Some of the workshops include professional development, discussions with industry leaders and practitioners (Kirk Borne spoke to my cohort), and interview/case study prep. The post-graduation network is incredibly strong and supportive for alumni seeking jobs, with frequent interview and networking opportunities provided. Vivian (the program founder) has a vast professional network, which she utilizes for student and alumni needs.  I'm incredibly happy to have completed the program, and to have worked with such a terrific set of instructors, teaching assistants, and classmates during that time. I couldn't have asked for a better start to my data science career.

NYCDSA is definitely a great program for anyone looking to get into data science. I can honestly say it was one of the most challenging and rewarding things I have done. Despite not coming from a STEM background or having any sort of relevant experience initially, the bootcamp still prepared me to be able to land a data science job after. 

Prior to deciding to join the bootcamp I was referred by a friend who also did not come from a STEM background that highly recommended the program. I decided to try a part time Python course that was offered by the academy to see if this was something I could continue to pursue while also doing more of my own research. After the class I applied and got into the January cohort but decided to defer my acceptance to the following cohort. I think that if anyone is considering the bootcamp and does not have any sort of applicable experience like myself, I highly recommend giving yourself time to complete as much of the pre-work as possible before diving in. Between Python and R, I would try to get as familiar with Python as possible through the pre-work and even beyond that if possible. The staff emphasized this during the interview process and after being accepted, but this became even more apparent once the bootcamp started. The first few weeks of the cohort were more of a review from the pre-work and the class I had taken as we quickly learned both R and Python. However, because I had completed the course and completed a good amount of pre-work, I was able to focus more on solidifying concepts rather than struggling to understand and keep up with learning the code on the fly. This also helped towards the end when completing more complex projects, but I think that if I had dedicated an even greater amount of time beforehand, this could have helped me to push myself even further during the bootcamp. 

The reason I picked NYCDSA was because it had the best overall reviews and I wanted to learn both Python and R (I noticed some other bootcamps focused only on Python). The whole program was well structured and organized, but quickly became very intense once we started going into completely new material. It is all definitely manageable however, and what you get out of it really will be what you put into the bootcamp yourself. The staff also makes sure to simultaneously help you get your resume ready by the time you graduate and prep you for what to expect for interviews. 

In terms of my experience post bootcamp, the staff still does everything they can to support you in the job hunting process which I really appreciated and have a pretty large network to tap into. For myself, there was a lot of work to be done in terms of networking and preparation given that I was pivoting into an entirely different field, but I found that domain experience definitely helped in my case in terms of getting some interviews. 

When I look back on the bootcamp I'm really amazed at all we accomplished and learned in a relatively short time. I also made a lot of close friends in my cohort given that the majority of our time during the three months was spent together. It was great  to learn alongside people with diverse experiences/backgrounds and I feel that for the most part everyone was supportive of one another. If you're interested in the field, willing to sacrifice the time, and want to challenge yourself, then I highly recommend signing up!

This course has been my first attempt to learn how to code. I have enjoyed it a lot. Very interesting, and I would recommend it to anybody that, as myself, has no prior experience with coding. Among other things, our teacher ( Tony Schultz) was outstanding. He is extremely well prepared, very focused on actually making us learn and like Python, is well organized, and has a very engaging way of running his lessons. Overall it has been a great experience, and it's very likely that I will take other classes at NYC Data Science Academy.

This review is for the Introduction to Python course. My background is in business intelligence, business process and data governance. I use SQL at my job daily but have had no formal programming training.

The introduction Python course was perfect for beginners. Tony, my instructor was extremely knowledgeable and did a fantastic job explaining concepts clearly.  I was very impressed with the number of topics that were covered in 8 sessions and how organized the course and the communication was. For an evening class after a full work day, his personality kept the class lively and interesting.

If you’re like me and coming in with no programming background, I highly recommend reading up and taking some free online Python courses beforehand. Overall, this was a great learning experience and I highly recommend NYCDSA.

Best decision I have ever made for my career. If you also have realized that the way we collect, analyze, understand, and utilize data will determine the future and potentially your career path too, this boot camp is what you need to do. I came in expecting a strict syllabus and skilled instructors but what I got was so much more. 

My experience with the NYC Academy for Data Science was fascinating, exhausting, humbling, and rewarding. I learned more than I could have imagined and I made new life-long friends. Your fellows will be a colorful mix from across degrees, disciplines, and nationalities. That also means that you will hear and learn from different perspectives, which will expand your horizon significantly. All instructors and TAs (highly skilled, smart, funny, friendly, and fantastic) are there to support and help you with whatever problem you have and the syllabus is business-oriented to prepare you the best way possible for your future endeavors. After 12 weeks, you will know how to code, how to tackle data science projects, and how to communicate your findings effectively. You will also know how to manage Big Data or how to build complex Deep Learning algorithms.

It won't be easy though and although they give you all the resources you need, it will be up to you how much you get out of it. So expect all-nighters, some frustrating and seemingly hopeless moments combined with desperate runs to the coffee machine. BUT IT IS WORTH IT. You can do anything for 3 months and if you put all you have into it, you won't regret a single minute. 

I would do it again. Like I said, best decision I have ever made for my career. 

Getting straight to the point, this bootcamp is for people who actually want to learn data science, not for people who are looking to get paid more money. If you don't have the passion and grit to handle an intensive bootcamp as NYCDSA teaches, don't bother coming. I am coming from a B.S. in Biology and worked for 3 years at a large company and this is my advice to those who have NO CODING experience.

Advice:
-DO ALL THE PREWORK. This is not a joke, do all the prework as much as you can to be prepared for what's to come during the bootcamp. Yes, it will be disappointing failing over and over again but you need to be okay and comfortable failing in order to be learning the material that is given to you
-SUPPLEMENTAL COURSES. Along with the pre-work, they recommend you take Andrew Ng's Machine Learning course and brush up on your linear algebra because you'll need it for the end of the course and you won't have time to do that during the time of the course
-TRY, FAIL, REPEAT. In order for you to get comfortable with coding, you're gonna need to repeat the same line of code over and over again until you get it in your head. I know, because I had to do this myself throughout the whole course.
-ASK YOUR NEIGHBOR. The first day is always awkward meeting new people, but with how much work you have to do, you'll get over it fast and want someone else's view on a particular matter. The teachers there are great, but sometimes you'll need someone to explain in a new way or you have to explain it to someone else so you can solidify your own knowledge. So do yourself a favor and ask a neighbor before asking the teacher first.
-PREPARE. This course can be a breeze, or this course can be a challenging experiencing. It is so easy for anyone to come and just breeze through all the material they give you, but it is really up to you to make it harder for yourself and learn topics and implementing them right away. If you want to make the most out of it then you need to COME EARLY, STAY LATE, ASK QUESTIONS.

Many think this course starts off slow, but take this time to grab a strong foundation in your coding skills in R and Python because after the first weeks are gone, you're gonna miss it. The course becomes relentless in terms of the material they give you and how much time you have to get it done.

PROS:
- This bootcamp helps those who have no coding experience but does require you to have some knowledge in statistics and linear algebra (if you don't have any of that under your belt, brush up)
-Materials given to learn and understand the material is a great learning experience
-Super friendly and willing to help you
-Upfront about hiring and what you need to do to get hired
-Hiring event was an amazing opportunity to get you started with interviews

CONS:
-Some of the teaching staff know English as a second language, so it may be difficult for some to understand clearly what they are trying to explain. Be patient and ask questions when appropriate if you are confused.
-Space is rather small and does not provide the most optimal space to do group projects.
-Although the hiring event was absolutely amazing, some of the requirements do ask a pretty demanding qualification and a majority of job postings are not entry level/associate positions (keep this in mind)

CONCLUSION
I recommend this course to anyone who is planning to take data science as a new career path. Keep in mind that this field is interdisciplinary and allows for people of all range of backgrounds to be in data science. The teacher, staff, and fellows were amazing and glad I got to learn as much as I did, however, after this bootcamp, it is actually just the beginning of how much you actually can learn of what's out there in the field today.

Well arranged curriculum.

Very nice and helpful professors.

Although it seems as a great initial investment, but definitely worth it

I took the part time Data Analysis and Visualization weekend course with Tony Schultz. I really enjoyed the curriculum and the class. I did not have any experience with Python before joining his class but had basic knowledge on data structures. The first class is a basic overview and felt a bit rushed for me as I had no previous python experience. However the pre-reading for this class got me upto speed on those concepts. The rest of the classes were well paced and I enjoyed learning those concepts. Tony's curriculum was very hands on and thats the bit I really liked about this class. Would recommend this class to anyone.

(I’m reviewing the 12-Week Full Time Data Science Bootcamp. They offer an identical program online, but there are some benefits to attending the program in-person.)

Background: I received a PhD in chemistry from a great university in 2016 and worked as a postdoc for 2 years. In grad school I learned to code and analyze data extensively with MATLAB and during my postdoc I learned very basic Python. When I reached the end of my contract I realized that I was not enjoying other parts of scientific research as much as programming and data analysis, so I decided to apply to do software engineering science-adjacent companies. With a background only in MATLAB and minor Python, I didn’t get any positive responses. After stumbling across an article about transitioning from scientific research to software engineering, I soon started reading about Data Science which sits at the intersection of my professional experience and my ideal workday schedule (coding over chemistry).

Choosing a bootcamp: I did some research online into free data science courses and bootcamps by reading reviews and talking to a friend who was a hiring manager in software engineering. To save the money, I had considered “rolling my own” bootcamp using open and available courses online, but my friend warned me against this, saying I needed to focus more on building a code portfolio for companies to see and focus less on taking courses. Looking back, I realize she was right. Without any guidance it would have taken me 9 months or more to do what I did in 3 months at the boot camp. I focused my applications on the top-rated programs as well as ones that guaranteed jobs or provided scholarships. After being accepted into two programs (the other offering me a significant scholarship) I based my decision on the reviews of previous students- and NYCDSA had the better reviews. Deciding to change fields after a PhD felt so risky (and even foolish) that I wanted to have no regrets even if I failed. So I picked the program with the most reviews from satisfied students and with the least dissatisfied students.

The bootcamp: The bootcamp is as hard as it should be. If it weren’t hard, employers wouldn’t take the experience seriously. I treated it like a hiatus in the rest of my life to pursue this singular goal. Sometimes you’re drinking out of a firehose, attending 3-6 hours of lecture a day while working on a project and completing the lecture homework. At the same time, it brings you close to the other students because you’re all going through the same thing. Even after doing a PhD at an Ivy League school, this was one of the hardest things I’ve ever done. 12 weeks will feel like 3 years, but I loved the other students that I got to work with. The course material is good as well- most of the lectures are well-polished and the instructors know the material deeply. Occasionally lectures are hard to follow because not all of the instructors are native English speakers, but the material is improved with every cohort because they collect constant (anonymous) feedback from the students. The instructors are constantly available and asking questions in class is highly encouraged due to the shrinking class sizes. The curriculum is set up well, as the first 4-5 weeks are primarily learning to code, the next 5 weeks focus on machine learning, and the last 2 are divided into data engineering tools and deep learning.

The result: The most important thing you do at NYCDSA is build a 4-project portfolio in the form of a GitHub account and blog posts describing your work and best findings. These projects cover a range of skills and demonstrate your experience to potential employers. At the end of the day, there would only one thing that would make this bootcamp worthwhile... Whenever anyone asked me if I liked the bootcamp I would respond, “I’ll let you know when I get a job.” The last several weeks of the program focus on helping you practice interviews and communicate what you already know. I don’t consider myself a confident person, but by the end of this program I felt prepared enough to appear confident to potential employers, and this led to my obtaining 2 competing job offers within 4 weeks of leaving the boot camp and still daily phone calls from recruiters (even having left NYC for a much smaller data science market). In the end, no program will be perfect, but the NYCDSA is so committed to transforming itself with every group and getting students hired I’m confident that it’s at least as good as any other program out there.

I really enjoyed my data science bootcamp experience at the NYC Data Science Academy. I was initially deciding between several other top bootcamps in NYC but decided to join the NYC Data Science Academy because of the comprehensive course material and programming languages (SQL, R, Python) that were taught at the bootcamp.

The structured pre-work courses that were available online for students before the bootcamp started preparing me for a solid foundation in programming, as I come from a background with minimal programming experience.

The instructors and TAs at the bootcamp are extremely helpful and instructive during the bootcamp in addition to the continuous career support after the bootcamp has ended.

I also really enjoyed the environment of the bootcamp. I was able to connect with people who also love working with data and expand my network through building relationships with current and previous students from the bootcamp.

Prior to my full-time 12-weeks DS bootcamp with the academy, I have started taking weekend classes in Python with the academy for my personal preparation. The weekend class is extremely helpful. You spent 5 hours on Sunday and learn as much as you could during those hours. Because it is taught by using Jupyter notebook, it is really easy to follow and to relearn over the following week. The teacher over the weekend is Tony, who is a physicist and has really good understandings of machine learning theories and on the application of data science across different fields. My weekend cohort consists of recent MBA grads who are looking to lead teams that work closely with software engineers and data scientists. You also see people from the risk practice of consulting/advisory companies. You can also see people who are in the IT department making a transition to the field of Data Science. Last but not least, you'll also have people in graduate school or even college who would dedicate their Sunday afternoon to refine their skills in Python. It is an investment that will pay dividends in your career if you are determined to bring some data science to your toolbox and your workplace.

Since I have attended both the weekend class and the bootcamp, I have strong confidence in the quality of the bootcamp (faculty, cohorts, homework, material). I hope in the future the campus will have more offerings in advanced classes, and possibly classes on linear algebra or computer science in general, which are the foundations of data sci

Really solid course covering a lot of topics for managing data structures and visualization.

Coming from the finance field, I had very little experience in coding. I had dabbled with a little bit of VBA where I worked, but by no means could I have claimed to know how to code. That being said, I feel this class provided me a great foundation to Python. Tony, the instructor, made sure the concepts were reinforced by easy to understand examples, and even silly examples at times which was greatly appreciated. The in class exercises were also helpful to glue the concepts together and homework was also given for more practice.  The one thing I could have suggested in terms of the weekend class itself is to perhaps allow for a longer day so the teacher do not have to rush over any particular topic. Overall, I really enjoyed this class and would recommend it to anyone who is looking to get their hands dirty with coding and data analysis.