NYC Data Science Academy

I started working in the financial industry in 1998 and have had roles in IT spanning development and production support. I attended the NYC Data Science Academy bootcamp during a three month sabbatical from work and it was a worthwhile investment. I have gone back to work to a new and challenging role that allows me to apply my new found skills (such as data exploration, visualization, and analysis for decision making) as a production support manager for an ultra low latency and algorithmic trading platform at a top financial institution.

 

What did it take for me to achieve success at the bootcamp?

 

My three months at the academy was intense. I had a strong support system at home and at the school. My instructors and TAs were smart, caring and invested in my development every step of the way. Delivering on five different projects that stretch your knowledge of Data Science and Machine Learning fundamentals, Python and R programming, through daily classroom and homework practice was exhausting yet rewarding because you were not alone through the journey. As an early riser, the academy facilities were available to me starting at 7AM daily.

 

My advice for a strong finish.

I strongly advise that you complete the prep work provided by the academy by the time you start the bootcamp. The amount of work expected to be completed during the three-month journey is not an easy feat; however, the projects you are exposed to, the knowledge you gain and the practical experience you collect through individual and team projects is indispensable and can be quickly applied upon your return to work. Going into the bootcamp I felt uncomfortable thinking of myself as a potential Data Scientist. Leaving the bootcamp I am comfortable with the fundamentals of Data Science and the application of hypothesis testing to data problems. I am not a Data Science unicorn, hence, I rely on my new found strengths and maximize the talents within my team to investigate and find solutions to technical problems.

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I studied mechanical engineering and physics for my undergrad at a top university and work in product management with a focus on search. I took this class to satisfy a personal interest in the subject matter and familiarize myself enough with the fundamentals of machine learning to be able to explore the field more deeply on my own. I was also motivated by a career interest: the subject matter is highly relevant to my domain, and I feel that developing an understanding of the concepts and how to deploy them myself will make me better at my job long-term. Prior to enrolling in the class, I spent roughly 8-10 hours learning R and felt sufficiently prepared (I had some previous programming experience).   In the end I was extremely happy with this class (Machine Learning in R on Saturdays, 8 hrs at a time). The curriculum and content were excellent, the instructor, Luke, was fantastic and the assignments were challenging and informative.    I felt the course did a really great job of driving home the core fundamentals of each subject with a focus on statistics, mathematical theory, derivations and best practices. We covered a LOT of material, yet the material had a lot of depth. I thought the sequencing of the subject matter was very well thought out as well. The class was demanding and had the caliber of a graduate-level course.   The course also struck a very nice balance between theory and implementation. After learning about a new model, we would immediately implement it in class using R on our own machines. Luke did a particularly great job at relating the implementation back to the concepts and teaching us how to interpret outcomes of our analyses (I can’t stress enough how important this latter point was for me). He has a really strong grasp of the subject matter, he’s very patient and responsive to questions, offers a lot of insightful commentary on the theory, implementations and best practices, and he cares about his students a lot. The homework assignments complement the class nicely as well, helping to drive home the methods taught in class and how to interpret your work.   If you’re interested in developing a strong understanding of the fundamentals of machine learning in a rigorous format, this class is for you. I also couldn’t recommend Luke as an instructor more. He’s awesome! I was also was very pleased with my choice of the R class. R reduces a lot of the friction in model implementation, which allowed me to focus on developing an understanding of the concepts and interpreting results.  Read less

1.12 weeks' course with machine learning, spark, hadoop helped me solve almost technical interview questions. Also introduce several latest and popular topic, such as NLP, Deeplearning (CNN) and tensor flow. 

2. This bootcamp faces people with different backgrounds, who can choose between "how to use machine learning" and "how to implement the machine learning(more math). TAs helped a lot if you wanna learn more advanced level.

3. Chris and Vivian helped prepare resume and the interview practice, and the hiring partner event was very helpful to present myself to the hiring managers directly. 

Great appreciate!

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Took the weekend course for Machine Learning with R. Course was very helpful in helping me understand the basics of Machine Learning, different models. My instructor was Luke. He was very helpful and would spend enough time covering each topic. He even took an additional class because he didnt want to rush through the material. Overall I am quite satisfied with the results. Would recommend Luke to anyone else who is interested to venture into Machine Learning field.

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Overall:

I will recommend this bootcamp to anyone who is eager to learn and have great passion towards data science. Before I attended the bootcamp, I received my master degree in marketing from school. I did not have a lot of math and coding background back then. With my passion towards data science, I decided to take a deep dive and applied for the NYCDSA’s 12 weeks bootcamp. Due to my limited coding background, they did not accept me at the beginning, instead, they provided one month prep course for me to get prepared for the bootcamp. I attended the September cohort after the prep course. It was one of the best decisions I have ever made. With the extensive knowledge and training, and great support in job assistance, I am able to land my dream job in 2 months after bootcamp.

Curriculum:

The curriculum is very well designed. I did a lot of research before I applied for this bootcamp. NYCDSA is the only program that covered data science in both R and python, big data processing tools such as Hadoop, Spark at once. I am really glad that I started my journey in data science with them, so my foundation in data science is much stronger now than I was when taking online classes by myself before. The curriculum will also help you to develop a portfolio for job hunting, however, you are the one who decide how much effort you want to put in and how far you want to go. 

Instructors:

The instructors and TAs are the best part of the bootcamp. You will never receive this kind of learning experience through online courses. They are all very knowledgeable in computer science, statistics, and machine learning. They are so passionate, and so willing to help each student. There were so many times that they stayed after 7pm, came in during weekends, answered slacks questions at 11pm to provide extra helps. They are far more than just instructors and TAs, but also supportive friends after I finished the program.

Job assistance:

I got hired by a major ad agency as a data scientist within two months of completing the bootcamp.

The hiring team put the best effort to help their students. They hosted awesome hiring partners event, which the students got to make connections and talk to some great companies, such as IBM, citi bank, Mindshare, Publicis and so on. During my job hunting process, Vivian tried her best to provide any connections with the companies that I really wanted to get in. I also received a lot of helps and mentorship from Chris. The way he helped me to prepare for net-working and interviews are very strategic, systematic, and effective. I learned so much and I won’t get this far without him. You can learn all the hard skills anywhere else, but this kind of support and mentorship is hard to find even if you have a lot of money.

They provide as many helps as they can, but you have to be proactive and eager to learn to take everything in! 

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This bootcamp is no longer the great place it used to be. Vivian Zhang (the crazy CEO) has made the curriculum into a joke, and you waste a lot of time on nonsense apps like shiny, blog posts because she thinks that's what employers want. It is not. No one in the industry pays attention to the things she believes they do, which is 1 reason why it has been so difficult to place all candidates in a job. Do not believe for 1 minute she will help you get a job. Only your true qualifications will help with that. 

Her TAs are wonderful and brilliant. It is not their fault that she bulldozes their efforts. But she has complete control of the camp. 

It is very difficult to watch her cheapen the brand of what started out so great, and single-handedly destroy the hard work of everyone who works for her.

She let in people to the camp who were completely unqualified to be there, including non-STEM bachelors, or MBAs with no technical background whatsoever, who were a huge burden on the class. If you are a STEM phd, you had better go to Insight or Ivy Data Science if you want an intensive course in ML and statistics. "Data science" is a marketing phrase for analytics. If you do not have a technical background, you should know that the 16,000$ package Vivian is selling you is too good to be true. You cannot be made a data scientist in 3 months, and there is no way she will be able to place you in a data science job. Most people from cohorts 5 and above are still looking for jobs months afterwards. The ones that do find jobs 6 months-1 year afterwards, do so without any help from Vivian.

I work for a media company that is transitioning to Python. They were made aware of Vivians reputation and decided to send all their data analysts for corporate training in Metis. I was asked for my recommendation and I have to agree that the quality of teaching is better at Metis, since Vivian has fired most of her good teachers, and the median quality of students can be terrible, since she lets in who ever applies, regardless of their technical skills. I also spoke to several prospective students who told me they pulled out of DSA because they heard that Vivian was letting in anybody who wanted to become a data scientist, even business analysts, and was unable to get most people a permanent job. She lists "internships" as permanent jobs, and cannot support the candidate when those internships terminate. The people in my office who know Vivian do not think that DSA looks good on my resume mainly because they think "she is trying to industrialize data scientists like a factory".

She also expanded the bootcamp from 20 students to 80 students, which should be a good indication of the quality you can expect.

Some things Vivian has done:

1. Lied by saying her acceptance rate for students is 10%. It is 100%. Maybe even 200%, if you consider all the people she tries to recruit. Do you know someone who want to be a data scientist? Tell them to send their 5000$ deposit!!
2. Fired her main lecturer and dedicated TAs, but not before working them like slaves.
3. Turned shiny app, visualization, web development and A/B testing into parts of the curriculum because she heard from 1 employer that they like it. It has nothing to do with "data science". She will just throw everything at a wall and see what sticks
4. Focus first on the jobs for chinese, american, or PHD students to improve her job ratio. The way she makes ''tailored recommendations'' is to put all 100 students CVs into a ZIP file, email this zip file to HR/the HIRING MANAGER and ask if there are any jobs for people in the 'awesome' 5GB of cvs she is sending. With many exclamation points!!! 
5. is so aggressive with employers and contacts that she scares them away. See 4.
6. If you want to work in finance she will tell you that finance is dead in New york and no one is hiring in there anymore. You should work for a start up instead, preferably one on her list
7. If you are coming from outside New York she will send you back there and let you find a job on your own
8. Her main advice is: send your CV to 10 companies every day on linkedin and hope someone replies. This advice costs approx. $16,000. Do you want a mock interview from someone not from the same company you applied for? That will cost $50.
9. If you do get a job, be prepared for an awkward phone call about how you should hire data scientists from her when you join your company
10. Even if you are not interested in the job, she will sign you up to interview with everyone in Manhattan that she knows. Do you know this self employed guy Ben Reid or Snakes and ladders? Let them interview you for a job you don't want just for fun. Ben loves interviewing! 
11. If you are a scientist, this is probably the worst one of all. The way Vivian prepares students for interviews is by having them memorize thousands of interview questions, categorized by company. This does two things: hide a lack of deep understanding with shallow memorization, and fool interviewers into thinking that a candidate who has memorized the entire interviewbook is more qualified than one who hasnt. It is as close to cheating as you can get, and the most obvious manifestation of the data science mass industrialization and over saturation: forget about understanding deep scientific concepts, just make sure you know what your interviewer will ask you before the interview. She is a shameless cheater.
12. This is related to 11. If you are Chinese, Vivian will prep you before and after for the interviews. If not, she will tell you that giving you answers to interview questions is "unfair to other students."

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I took the DATA SCIENCE WITH PYTHON: DATA ANALYSIS AND VISUALIZATION (WEEKENDS), with Aiko Liu.  Aiko is an excellent teacher, who taught methodically and progressively.  The course was extremely well designed, and elevated my skilset by building my understanding step by step in a structured fashion. I would highly recommend the instructor and the class if you want to truly develop a solid foundation to build your skills on.

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I graduated summa cum laude from a top college and attend a top Economics PhD program. I attended a boot camp at this institution to boost my empirical research skills. To be honest, even though I've been receiving America's best education, I was still really amazed by the high quality of the education it provided.

The coursework is highly organized with crystal clear logic flow. The faculty here are super competent and very approachable. At this program, you will learn TONS of very interesting and practical skills, build an impressive resume and open many doors in industry.

The job placement support here is fantastic. I attended this boot camp to gain empirical research skills for academia and thus did not actively look for industry jobs, but I saw that my classmates have benefited tremendously on job search from this program. Starting from week 3, the program prepares you for professional development. Many HR's from different industries are invited for talks/info sessions/networking events. Students have many opportunities to build connections and to prove themselves. 

You will have to work hard as the boot camp is very intensive, but that's exactly why it can give you so much and prepare you so well for your future.  

I would highly recommend this program to anyone!

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Preface:

I attended the July cohort and was then a Data Science in Residence at the NYCDSA prior to accepting an offer for a Data Scientist role at a consulting firm based in NYC. I will do my best in this review to be as straightforward about my experience and address a lot of questions I had prior to the program and have understood a lot of incoming students to have had through prepping them for the program. 

My Overall Opinion:

Lets start with this: get over your hesitation, take a leap of faith, and you surely will not regret your decision to attend the NYC Data Science Academy 12 Week Bootcamp. Whatever reservations you have, you are not alone; almost all graduates have felt that prior to attending the program. You may be wondering about if this program will really teach me the necessary skills to get a job, am I prepared enough for this, will the program be rigorous enough, and most pivotally; is this the right decision for me? I cannot speak for you, but I can attest to my own experience and I graduated this program with no regrets, soak in a waterfall of new knowledge and skills, and have walked away a smarter, more capable, more confident professional now employed Data Scientist in field. The program is amazing, and the instructors are passionate, and you will learn a lot, but that also being said, you get as much as you put in and the first step is trusting in yourself to join and commit to the three rigorous and insightful months ahead of you. 

My Background:

Prior to attending NYCDSA, I was a software engineer at a large shipping company for almost 2 years focusing primarily on front end development. Before that I attending another bootcamp called Hack Reactor, similar to NYCDSA, to study software engineering and javascript. It was growing interest in machine learning, the success of my experience at Hack Reactor and the thirst for that similar immersive and intensive program that led to me to the NYC Data Science Academy. In another life, I graduated from NYU with a degree in Music Business.

Common Questions:

Do you need a masters or a background in a quantitative field?

The answer is no. No, you do not need a masters degree or professional experience in a quantitative field. During my cohort, I thought some of the best and most creative presentations and chosen topics by students were from some that had no prior coding or heavy math experience. That being said, coming in with a math or software engineering background will definitely allow you to vamp up your projects flashiness and you may have an easier time understanding some of the formulas associated with the advanced machine learning algorithms. 

Will I get a job from this program? What are the job stats? etc.. 

Although I can't attest to job stats, I received my first offer less than 6 weeks after the program ended. I met the firm at the Career Day at the end of the program. Vivian, the CEO of NYCDSA, is the job placement tsar and wizard and without her and her relationships with so many great firms there would not be as many companies at the career day as there are: Spotify, JP Morgan Chase, MindShare, etc.. Vivian will do every in her power to connect you with your dream job. That being said, getting an offer is entirely on you. You must have great visual and complex projects, you must interview well, and you must be actively job seeking and cultivating relationships on your own. Vivian and the team will do everything they can to prepare you and connect you with your dream job, but you need to show up with the bus fair to ride the bus. I cannot stress this enough. 

How does this compare to other bootcamps?

I am actually in a unique position to answer this question. A few years prior to graduating NYCDSA, I graduated Hack Reactor, which is a similar 12 week bootcamp, but for software engineering. Prior to that I took web development course at General Assembly, Coursera Machine Learning courses, Harvard Extension School’s Intro to Data Science class to name a few. Needless to say, I’m hungry for knowledge and a challenge. I can’t speak to the other Data Science bootcamps out there like Metis or Galvanize, but I can say that there is nothing like 3 months of learning something at a such a rapid pace. The sum of its parts is greater than the whole and you can spend years learning on your own secondary to whatever else you are doing or you can jump in the deep end. I did before with Hack Reactor and walked away amazed and the same held true for NYCDSA. 

Inside Tips:

Pick good project topics. Your projects will be portfolio to potential employers and if your project outshines the other students, employers will take notice. 

Pick good project team mates. I didn't have a poor experience with anyone I worked with, but it goes without saying that if you pick bright, easy to work with project partners, you'll be able to build a better end product.

If you don’t know how to code in R or Python and have no prior experience code - Learn now. Codeschool.com is an excellent introductory resource. 

DO NOT think you will have a social life. This is only 3 months of your life, but 3 very important months, so give it your all and don't expect to get as much out of it if you don't.

Comments to the Academy:

Scaling the program will require more job assistance personnel.

Increasing the amount of coding done in the program may be beneficial as most jobs now require coding every day and are rarely purely theoretically

Additional content on computer vision would be fun

More TAs and one on one knowledge quizzes (like done as a group at the end of the course)

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*MY BACKGROUND:
I had a Master in Business Analytics before joining NYCDSA, with a knowledge of programming and data science/machine learning. Though I knew how to make graphs and build models with R and Python, and knew some concepts learned from the online course on EDX and Coursera, this bootcamp was still truly helpful for me.

My goal was to explore more deeply the big data techniques including Hadoop and Spark and get a chance to review data science and machine learning stuff in a systemic way. This bootcamp gave me almost everything I desired, with so many unexpected benefits.

It was seriously life-changing for me. I achieved something that would otherwise never be possible had I just stuck with online courses. Read on for more detail.

*COURSES:
All the courses were well-designed. They covered everything I needed in my data science journey. Some might wonder why I chose to spend money on this bootcamp to learn something that seems available online. The reason for me was that I feel my time is quite valuable. For me, the efficiency really matters. Rather than spending an hour searching for the right function or parameter and ending up being confused, I wanted to have professionals help me going through the relevant resources systemically. I also found that when confused by problems after the fact, I would open the slides, code, and my repo for that topic instead of having to jump online and wade through Stack Overflow. 

What’s more, the curriculum covered topics such as Unix, Bash, Git and version control, which seem necessary for a data scientist/programmer but which I never paid attention to when I was teaching myself.

*INSTRUCTORS AND TEAM:
They are so great. Everyone is kind and willing to help you and share their experience and approach with you. In my humble opinion, there is a huge difference between teaching yourself programming or machine learning and learning with instructors and advice. Especially when you are putting things into practice. I saved tons of time.  The NYC Data team is really curious and love to try new techniques with the students.  

They are a caring bunch, and it was great to become friends with the people who participated in and ran the program.

*JOB PLACEMENT:
I got hired by Aetna as a data science analyst within three months of completing the Bootcamp.

The job search is intense but Vivian and the hiring team were always there trying their best to help us. There is a room set aside for graduates to work in when on the job hunt, which made for easy access to staff.   During the Bootcamp, we had several courses about how to sell yourself which was especially important for people who are new to the U.S. job market. From teaching you how to impress your interviewer to helping research relevant details about a target company, the hiring team was very dedicated to providing the necessary support to help me succeed. Also, Vivian seemed to have a contact at almost every company I wanted to apply to, which was a real plus.

I’m glad I made the decision to do this, it was worth it for me.

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I recommend this bootcamp to anyone who wants to transition into the field of Data Science. Before this bootcamp I was a process engineer for a large North American steel company. With the rate of growth in the technology industry, I knew it was time to transition to a new career, and I could have not chosen a better place to do so than NYC Data Science Academy.
Instructors:
The Instructors were amazing. Chris is extremely knowledgable in statistics, and his passion for teaching really shines through. With every lecture, he not only shows mastery of the material but also the best way to teach complex materials to a class of non-programmers.
Luke goes above and beyond to describe the theory behind the algorithms. His work ethic is shown through the countless hours he has stayed to review lessons with students and belief in continuous improvement.
The TAs (Shu, Zeyu) were immense help, and had very good knowledge of big data applications (Hadoop ecosystem, front end work, SQL database design, etc.)
Curriculum:
The bootcamp offers a good basis for understanding prediction models and when to use which types of algorithms. There just isn't enough time to cover all aspects of statistics and the many branches of prediction models. Both R and Python are taught here, which allows for great flexibility. While this bootcamp is rigorous, self discipline is required to fully delve into algorithms and build impressive products for your portfolio (natural language processing, image recognition, recommendation engines, etc. ; these advanced topics are covered briefly but enough for you to take the reins). All in all I believe the bootcamp set me on the right foot into the industry.
Job Assistance:
While there is some assistance, the majority of the legwork work is still on the student to be duly diligent - sending out applications, getting interviews, networking and working their way up towards their dream job. There is no easy formula for this, and you MUST continue learning (Algorithms, data architecture, and more advanced ML topics etc. ; network to find out what people in your ideal companies expect you to know) and reviewing material even after the bootcamp to prepare for interviews. 

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Attending the NYC Data Science Academy 12-week Data Science Bootcamp was one of the best decisions I have made. It was instructive and rewarding. It provided a speedy career transition and enabled me to get a job within two months of graduation as a Machine Learning Data Engineer at Capital One. I will summarize my background and describe my experience at the bootcamp and why I recommend it highly.

I have a PhD in materials science, which is a blend of math, chemistry and physics. I had programmed models and simulations in Matlab, but have no formal computer science education. I switched to management consulting after the PhD to apply my analytical skills in the business world and quickly realized there is a great need for data analysis at companies. After taking the complete Data Science Specialization on Coursera, I knew I wanted to switch to data science and found the NYCDSA bootcamp to be the most comprehensive, teaching R, Python, and Big Data technologies.

I recommend this bootcamp for three reasons: quality of teachers and materials, structure, and networking, both at the bootcamp and in job placement.

It takes a lot of knowledge, experience, and hard work to distill complicated and complex topics and communicate them in a simple and understandable way. The materials presented in this bootcamp were presented that way. When I can understand statistical concepts which I had tried to understand for a long time in a matter of minutes, it means the quality of teaching and materials are excellent. During the job search, I also realized that the correct balance between breadth and depth had been selected to give us a very solid foundation on which to start a job in data science.

The teachers were exceptional. Their passion and dedication to the students were visible from day one. This was shown again and again in how hard they worked to constantly improve and expand lecture materials to how much support they gave to each individual’s success outside of class. Having them as teachers was an honor.

The structure of the bootcamp allowed an incredible amount of materials to be covered in a short amount of time. Particularly, it used both R and Python for statistical concepts and machine learning. In addition, we learned about many other tools in extra sessions designed to round out our knowledge. Big Data technologies such as Hadoop, Hive, and Spark were covered toward the end of the bootcamp. Spark was asked for often in interviews, and familiarity with it was helpful. Having five projects under your belt is exactly what you need when interviewing. I always had an example I could use to answer questions. The value of this is not to be underestimated.

Lastly, the opportunity to network was incredible. You are beginning your data science career having forged strong bonds with 35 other incredibly intelligent and inspiring people who go to work at great companies. The value of those friendships and the ability to create a strong network at the beginning of your data science career will become evident a couple years down the road.

I was fortunate enough to meet and give a presentation to managers at Spotify, at Meetups, and get connected to many hiring partners. Vivian, the founder, is a strong proponent and has an incredible network. She seemed to have a contact at almost every company I wanted to apply to. Her one-on-one evaluation of interview performance with me was very helpful. She and the rest of the staff are very dedicated to each student’s success, being clear in their purpose that this experience will change both you and your family’s lives for the better. Their hearts are in it and their dedication is clear.

If you are considering this bootcamp to get more into data science, it is exactly the accelerator you need to get your career in this field off the ground. I cannot recommend it enough.

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My background is in Data Engineering and Analytics and I have been consulting for the Banking and Financial Services clients. I strongly believe that Machine Learning is the key to the Artificial Intelligence and decided to embrace this emerging trend and immerse myself in the field of Data Science.

Although there are several Data Science bootcamps that are available, NYC Data Science Academy stood out for me because of their curriculum and the strong portfolio of projects they help and empower the students to build. The portfolio of projects, Github code and the corresponding blog posts definitely help make a good impression with the interviewer and showcase the great work accomplished during the 12 week period at the academy.
It has been a truly amazing learning experience for me and definitely worth the investment that helped me advance to my next stage of career growth.

To begin with, the curriculum is quite comprehensive covering all the important Machine Learning algorithms and their fundamentals, detailed working of the algorithm,assumptions, diagnostics, pros and cons. There are regular homework exercises that allows the students to apply the knowledge that they learned in the class and solidify their understanding of the concepts. The class was very diverse and represented students who hailed from various backgrounds - Finance to Engineering to Medical sciences.

I would like to highight the quality of instructors here: All the instructors were extremely professional, technically strong and quite articulate in explaining the concepts. They were also available after hours and helped clarify the questions pertaining to homework, projects and software installation.

Regarding career development, Vivian and her team have made great efforts to help the students reach out to hiring managers and have arranged internal referrals at the firms. The Academy also provides a strong support system for the students as they start looking out for career opportunities as a Data Scientist. 

At the end of the day, You'll form great relationships not only with the instructors but with other talented students as well as everybody learns together about this exciting field. If you are thinking about uplifting your career to a whole new level and set your feet into the fantastic world of Data Science, I strongly recommend NYC Data Science Academy.

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This 12-week data science bootcamp is great. The faculties have created an excellent curriculum to help you get in touch with almost everything you need to be a data scientist/engineer. Sure they did not cover every relevant theory, but remember this is only a 3-month bootcamp, which is designed to give you a "jump start," not a full-time college degree.

Besides the curriculum, most of the faculties are brilliant and always willing to help. Also, more importantly, you will make friends with people from many different backgrounds, which in my opinion is even more valuable than the course itself.

Regarding the career development, Vivian and her team have made a great effort to help us to reach out to hiring managers and arrange internal referrals. However, unlike most of my fellows in the cohort, I have a pure business background (bachelor in accounting & master in analytics). Therefore even though I have been studying DS and programming through online courses for about one and half years before joining this bootcamp, I still ended up taking a Data Analyst position instead of continuing my job hunting for the Data Scientist jobs.

At the end of the day, the cold fact is that most of the "real" data scientist positions require years of academic training and domain knowledge. The skills you have learned here at NYCDSA is definitely enough to grant you for an entry-level analytics position. However, if your target is the sexy DS title with six-digit salary, then unless you have a STEM Ph.D. or advanced degree in Computer Science / Statistics, this bootcamp will just be the start of a long journey.

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My background consists of undergraduate degrees in Math, Finance and a Masters in Financial Engineering. I found this 12-week intensive bootcamp to be extrememly valuable. Due to my academic background I was already familiar with some more technical concepts, but what NYC Data Science Academy did was to provide practical, hands on training in Python and R as they pertained to Data Analysis and Machine Learning. Following my time at NYC Data Science, I had intensive interviews for Data Science - focused roles in the finance community and I felt very well prepared. The staff is knowledgeable and routinely go above and beyond the contractual teaching time in order to ensure that each student receives the most instruction for their money. 

 

 

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I completed the Data Science bootcamp during the Spring of 2016. I enjoyed the Data Science bootcamp very much. It was a great experience overall.

My background is PH.D in Physics and I has more than ten years research experience as research scientist.  I am probably the most senior person in the class.

The course is very intensive and comprehensive. It covers most machine learning(ML) and data science skills:   ML in R and Python, website scraping, data visualization, big data with Spark and AWS. It is great to learn ML with both R and Python there. As I know, most boot camps don’t offer R. The school teaches a lot of stuff. It can be little overwhelming at the beginning. Everything I learned there is useful and helpful in my work. Just mention a few small things: Git, Mongodb, web scraping, AWS. I used all these skills in my work from day one.

There are enough TAs there and they are consistently available to offer help on homework, projects and software. I used windows system, the installation of some software is not straight forward.  Their great supports saved me lot of time in that way, and I can have focused on my projects and homework.

The instructors there are excellent too. They come from different background: statics, physics, and EE. Since the students come from different background, it is challenge for the lectures to deliver complicated pictures in clear way for most students.  They can present the material in a clear and logical sequence, explain complicated things in a simple way. I believed they did great job.

NYC Data Science Academy have a lot connection from local companies and this can add great values for the job seeking. I live in west coast and still got continuous supports. I worked at a start-up right after the bootcamp and then went to another  larger company a few months later.

Data Science is largely different from other science. It requires many skill sets and focuses on application much more than pure science. NYC Data Science Academy offer great opportunities for people who are seeking new career in data science in a quick way :  learn the fundamental skills of data scientist in the real world in 12 weeks.  I fell fully supported and encouraged from day one to the final job hunting.  

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I have a Ph.D. in computer science, and I worked in tech industry for nearly two years. One thing I learned was that, great companies come and go, rise and fall, shine and fade. History repeats itself. However, no one can and shall beat the trend, since nothing is immortal. 

With a strong belief that machine learning is the key to the ultimate Artificial General Intelligence (AGI), I decided to embrace emerging trend and immerse myself in the field of data science, the field in which a critical piece is machine learning. In joining the NYC Data Science Academy, my motivation was simple: refresh my eyesight, build the intuitions, solidify my understanding, and meet with talented people.

It was a fantastic and rewarding journey with my fellow data scientists at the bootcamp. I will mainly concentrate on three aspects here: the curriculum, the instructors, and the like-minded peers. By no means they are complete, but hopefully they are useful for some people.

Firstly, the curriculum is comprehensive and well-tailored to balance both the breadth and depth of the data science field. Although each year there are perhaps hundreds of new machine learning algorithms devised in academic world, the essential ones are no more than ten and still widely applied in industry (see here for full list: http://www.datasciencecentral.com/profiles/blogs/top-10-machine-learning-algorithms). Almost all of them, including their nuances, nuts and bolts, pros and cons, were covered in the curriculum and explained well by instructors who really know them.

Secondly, the team is very efficient and professional. The instructors are technically proficient, articulate, and beyond my expectations. The lecture materials were self-contained, and covered related points that a data scientist should know. Especially, I was truly impressed by Luke’s passion in preparing useful materials and zestfully articulating his topics (such as sklearn framework, pandas, EM algorithms, or math, etc), by Zheyu’s professionalism and dedication in helping cohort to fix head-scratching problems and made their life easier, and by Shu’s enthusiasm in designing high-quality materials (sorting, hadoop, spark, etc) to broaden cohort’s skills. Besides, statistics was once a somewhat mysterious subject in my knowledge sphere. For a long time, I wanted to step my feet into its door to systematically cover it but I never managed to. I believe that if people want to explain something very easily without pain, they have to understand it deeply from beneath the bottom. It was Chris who unfolded the foundations of statistics and distilled many useful gists in my head within very short period of timespan. And he truly knew his subject inside out. I gained the gists, built the statistical intuition, and I am now more comfortable than ever before on this subject. 

Thirdly, most importantly to me, the feeling of spending three months with a group of talented people is simply awesome. There is a saying that, “if you are in a room and you are the smartest one, then you are in the wrong room” (https://www.quora.com/Who-can-this-quote-be-attributed-to-If-youre-the-smartest-person-in-the-room-youre-in-the-wrong-room). There were many smart people in that cohort so I was sure I was in the right room. I felt that sharing the joy of enlightenment (when somebody/team achieved high scores in Kaggle leaderboard) or sadness of frustration (when somebody’s code did not work no matter what) with other talented minds was unique and unforgettable experience. Besides, through brainstorming with like-minded, new insights were sparked and new ideas were transferred. Eventually those ideas were distilled into my projects and morphed into my sixth sense of mental muscles. I felt that I was fortunate to be amongst a group of great fellows: there was no bullshitting, no day dreaming, and people were nice and nobody wanted to waste time. They all knew what they were after and worked extremely hard. I am sure I will miss them from time to time.

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This course was a masterpiece.  Derek Darves the instructor, quickly brought us to competency with the R programming language.  Then he expanded the course by introducing the packages used for analysis and visualization, progressing through introductory use to somewhat elegant and sophisticated programming challenges. Ultimately Derek brought us to a self-sufficiency level for continuing our R education. The course was a pleasure as Derek is clearly an R expert and aficionado weaving many practical tips and historical insights into the lectures.  His programming experience, statistical insights and extensions of the course materials gave it a graduate level feel, while never ignoring the fundamental skills being taught.  I highly recommend it.

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In October, I signed up for the 12 week bootcamp which starts in January.  They recommended I take this course (free of charge) in preparation for the bootcamp to prepare myself in the language (I'm already comfortable in Python).

I'm giving this course 5 stars because, for the format, I think they did a perfect job.  The instructor, Derek Darves, was definitely qualified and a nice guy in general.  They gave the tools to learn the basics of data analysis, manipulation and visualization.  

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