NYC Data Science Academy

I won't go into a description of what the Bootcamp offers - you can find that in the reviews below. All of them are accurate as to the material that the program offers.  

I'll give my opinion of the FINANCIAL sense of going to this bootcamp versus others and the level of job assistance provided - which is what most of you reading this are actually interested in.

Let's do a compare and contrast with springboard.com (not paid for this, just using as an example)

Springboard

• $7,500 upfront tuition ($8,900)
• Full Refund Guarantee (after 6 months of no job)
• Deferred Tuition until you land a job

NYC Data Science

• $17,000 tuition ($5,000 deposit)
• No refund guarantee
• No deferred tuition option

You'd think with that price tag, there'd be some awesome job network or assistance provided, right?

Not really. 

They might send you a prospect once every two weeks. 

But, all the interviews I gained were through my own efforts on LinkedIn. 
The most they'll tell you is some superficial styling tips for your resume. 

And that's about it. Connections they do not provide. 

There was supposed to be a networking event with employers at the end of the program.

Didn't happen. 

The closest to that was an invitation to a "Gender Equalithon" hackathon by Esteem whose stated goal is to "Hire & Retain Women, People of Color, LGBTQ, Latinx in Tech". 

The point of this is - is that job assistance is important. The material can be learned anywhere - other bootcamps, online, universities. 

But the major selling point for these operations is the job assistance and access to their professional network.

And by that metric, NYC Data Science doesn't deliver. 

P.S.
The reason you probably don't see more negative reviews of these bootcamps, is because people are still angling for job assistance and most are scared to speak up or burn bridges. 

Earlier this year, I decided to pursue a career change. I was working as an actuary for several years, and became interested in the world of data science. Even though actuarial science is considered to be the original data science, in modern times, the two career tracks are quite different. It felt intimidating to make the transition into the field of data science on my own, not knowing where to get started.  In the beginning I was looking into getting a master’s in data science. However, I was deterred by the huge time, and cost commitment of pursuing a master’s degree. I started investigating various data science bootcamps having known several people who had successfully made career changes by attending bootcamps for programming, or design. A friend of mine recommended I check out NYCDSA. In the end, I chose NYCDSA over other programs I was considering, because it is the only data science academy that teaches both R and Python.  The bootcamp is only 3 months long, but covers a ton of material, and will be one of the most intense learning experiences of your life.  The program focuses heavily on practical applications. and is structured so you’re picking up the right skills in the most efficient manner. The instructors are super knowledgeable, and always there to support you along the way. A career in Data Science is a continuous learning journey, and NYCDSA was the perfect place for me to get started. I think the bootcamp does a fantastic job of helping you build a solid foundation of data science skills applicable to a range of data science career tracks. Simultaneously, there’s a lot of freedom to explore the niche field you are interested in. 

After working in the financial sector for over ten years, I have decided to strengthen data science skills, such skills are increasingly critical in problem solving. I chose NYC Data Science Academy over other bootcamps since it has a more comprehensive curriculum, the quality of faculty and the successful graduate networking. The 12-week curriculum covers the basics of data science, including Python, R and SQL coding skills, machine learning, clustering and algorithms knowledge, such a curriculum is indispensable in developing the data science-based analytical skills. The projects and the homework that we were assigned to are also quite useful to prepare job interviews. Alex and Luke are great instructors who helped out a lot with my study, as did TAs, I also made great friends from the cohort that I worked with here. I received invaluable support from the bootcamp in data scientist and data analyst interviews. I must say this is the best bootcamp to learn and improve data science skills and develop professional networks. 

I had been an advanced analytics director in media looking to make a career change into the healthcare field as a data scientist. I was working with marketing scientists in my prior job that were using machine learning but was not fully aware of the power, accuracy, and methodology behind statistical models and machine learning. Not only did I learn the ins and outs of traditional machine learning through this course, but I also picked up hands on experience web scraping, analyzing many types of data sets, and creating my own algorithms and machine learning models to tackle real-life problems such as predicting critical disease. R & Python are taught side by side, allowing you to pick up both languages and also understanding pros and cons of each.
Don't get me wrong, this was a lot of hard work, but if you're looking to make a career change and become a data scientist there is really no better way than to enter the belly of the beast. This is called a bootcamp for a reason so expect to roll up your sleeves and work very hard. However, if you do all your assignments, listen to the helpful advise from the instructors and administrators, and really make practical use of your projects and career support, this will be a career changing experience for you, as it was for me.

This 12-week course begins with a well-structured introduction to Python, R, and SQL and is friendly for beginners with no programming background. You'll be able to complete the first two projects: one is about web scraping and one is data visualization with R Shiny. We then covered the most popular machine learning models and concepts, which we implemented in the last two group projects. The boot camp also helps with resume, elevator pitch, code challenges, and there is a well-planned post-boot camp review section to prepare for theories and interviews. I have four years of experience working as a financial analyst and managed to get an offer one week after the career event. The things you learn here are the most practical skills in the professional world and will help you continue learning data science on your own after the boot camp. Job support is great!

 

I took  Dr. Krohn's Deep Learning class with the intention of keeping my self updated with new skills. After a week of class I found myself immersed on Deep Learning reading as much articles as I could on the subject. Dr. Krohn taught the class with a perfect balance between the math and the implementations, ideal for our class which had professionals with different backgrounds. We received a lot of information in each of the five sessions but the pace was OK because the 5 weeks were not exactly in a row. I would recommend this class to any body who wants to get ahead with  new technologies or who is just curious about them. If you decide to take the class, make sure to do the reading assignments and play with the software on your own. For the one optional project, I would recommend to immediately start a project with the material of the second class (our first implementation); so that you can get the help that you need if you get stuck and you can even have a chance to make a second project (maybe in NLP).  This would be specially useful  if you are a software developer and want to try the different implementations.

I attended the in person 12 Week Bootcamp during the end of 2017 and I am working as a Data Scientist at a highly recognized tech startup!  For those considering studying data science I highly recommend NYC Data Science Academy.  The main differentiator of NYC Data Science Academy from all the competitors is the curriculum.  The founder herself(Vivian) was one of the first era of data scientists who studies statistics and programming before "data science" got popular.  So the curriculum reflects this in that the bootcamp knows what it takes to succeed in data science in the real world.   The 12 weeks consist of SQL, R, Python, Machine Learning Algorithms, and Bid Data/Deep Learning with the emphasis primarily on practical aspect of these tools.  After the 12 weeks I wasn't a master of all things data science, but the bootcamp really equips you to be able to find your own answers to any data science question (engineering as well!) and make yourself a highly sought after candidate.  

The only "con" I guess is that you have to work your butt off! You're cramming years worth of an equivalent master's degree into 12 weeks, so you really have to be able to focus and have a passion for data and learning.  But as an encouragement,  all the people that worked hard ALL ended up with a really solid data job that they are proud of. You don't have to take my word for it, check LinkedIn to verify that NYC Data Science Acadey worked for a lot of great students

Before I attended NYC data science academy bootcamp, I already had chances to take some short introductory courses in R, Python, and machine learning at other institutes. But even after taking such courses,  my skills  and understanding were very limited, and I actually had difficulties doing researches. This is why I came to attend the NYC data science bootcamp.

During the bootcamp, we learned data science/machine learning not only in R but also in Python. The codes that I got from lecture codes and  homework are valuable, and I find them  very helpful for conducting my research. In particular, since R and Python have their own highs and lows, I'm able to use both R and Python in complementary ways in my researches, which are actually very good points of the bootcamp program.

When I conmpare the lecture notes from NYC data science bootcamp with lecture notes obtained elsewhere, I find that the lecture notes from NYC data science bootcamp are well organized to successfully explain fundamental theories.

I think the teachers here are very knowledgeable, and their lectures are very  efficient. Many of the teachers also shared their experience in the data science industry and gave us ideas about what it is like to work in the data science industry, which was another good point of this program. When students needed helps, TAs, managers, or teachers were available either in person or on slacks for discussions.

I think this is a highly recommendable program for anyone interested in data science. But, before this program starts, enrolled students are highly recommended to do their best in the preliminary lectures to be better prepared. In addition, students need to work really hard in lectures, homework, and projects.

I'd highly recommend this program.

I recently completed two NYCDSA courses: 'Python Introductory' and 'Python Data Visualization & Analysis' taught by Tony Schultz in July 2019. First, the courses provide a really great foundation to learn python and bulding out your technical skills for applications. Second, Tony is a great instructor and walks you through everything. He takes the time to explain difficult concepts and provides simple explanations to understanding code. Highly recommend to those who are looking to build out their technical skills.

Amazing bootcamp and experience! The teachers and TA's are extremely knowledgable and caring- going over and beyond in their support of students. The program is also exceptionally well-managed and upper management genuinely cares about the success of their students; both throughout the bootcamp and after graduation.

As in most things in life- you will get what you put in. Be prepared to work really hard- you will learn a TON and set yourself up for success in the data science field. You will also build an outstanding network and support system of like-minded, good people!

HIGHLY recommended!

I took the "Data Science with Python: Data Analysis and Visualization" class taught by Tony Schultz this summer.  The course content is thorough, well formed and covered a lot of areas within the short amount of time. Tony was great with explaining all the concepts. Often times he covered more than just one way to solve a problem and encouraged us to develop our own coding styles. Definitely recommend this  class to all working professionals interested in learning Data Science using Python. 

       The bootcamp offers even seasoned machine learning practitioners something new, and everyone (regardless of their coding background) can get something out of it.  The instructors are nice and want you to succeed but there is so much information packed into such a short amount of time. I liked this bootcamp but found it difficult.

       On a more personal note, I would not have gotten the job that I have now at Bloomberg without this bootcamp. I received letters of recommendation from two instructors as well as a practice interview with one of the camp’s alumni who had recently gone through the same interview process. The interviewer mentioned that the bootcamp made me stick out as an applicant and was part of the reason why I got this position. As I reflect on this experience though, I felt like I was floundering the entire time and overall the experience was not enjoyable because of the magnitude of work. However, I ended up learning so much about R and the different machine learning techniques. Even though I came from a mathematical background with some machine learning experience I was not able to comprehend some material during the program.

        Some of you reading this review may be thinking in a strictly cost-benefit mindset. For me, the financial benefit has outweighed the cost especially because I am at the beginning of my career. Because of the quality of the instructors, the opportunities the bootcamp unlocks, and the interesting subject material I would recommend this bootcamp to anyone looking for a challenging way to jumpstart their machine learning adventure.

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!!