NYC Data Science Academy

I was coming from a Computer Sicience background. And I decided to make a career switch to Data Science. One of my data scientist friends recommended me to take the NYC Data Science Academy's 12 weeks Data Science Bootcamp. It turns out to be money worthy. 

The bootcamp covers all the skills that I need to know to be a successful Data Scientist. It provides training on: programming skills (in both R and Python), basic Linux/Unix commands, basic database SQL programming, exploratory data analysis & data visualizationn (in both R and Python), Machine Learning in both R and Python, and basic Hadoop and Spark skills. Some people might wonder why both R and Python were taught. From my experience, R is really helpful and handy when doing the EDA and visualiztion at the beginning. Python is more useful when building production pipeline models. Also Jupyter Notebook (orignially called iPython Notebook) makes sharing the code and charts easier. So I feel it was right that they taught both R and Python.

The program was very intensive. There was so much information thrown into my brain every day so thus I had to study and reivew the course material every night late in order to digest them. We were assigned homework every other day and projects every 2 weeks. There were about 5-6 projects through the course of the bootcamp. Each project had a different focus. While the earlier projects focused on Exploratory Data  Analysis, the later ones were more Machine Learning focused. I found myself was always rushing to wrap up the projects because I always had diffcult time to come up with an idea or started it late. Lesson learned!

During the bootcamp, because I was from computer science backgroud, I was quite relaxed on the programming parts. But I definitely struggled a little bit on the Machine Learning part since it invovled advanced Math and statistics. So I had to go back and review some of the Math material and took some online training on statistics. Therefor it would be great advantage for people with statistics backgroud. If one without statistics background is trying to take the bootcamp, I would suggest that you study some statistics material from online before the bootcamp so that you can really understand the theory behind. 

I also want to give credits to the awesome instructors. Special thanks to Sam, Chris, and Luke who did a great job on teaching the class and answered all my questions. There were also two TAs who has provided big help on homeworks and projects.

Quick notes on job assitance, NYC Data Science Academy has connections with hiring partners. Therefore we got many job opportunities directly from the hring partners, such as Goldman Sachs, Chase, NBL, Booz Allen Hamilton. There was a time that the former job placement manager was slacked. But it was quickly fixed and the job placement manager was replaced and things were back on track again. We were also provided training on preparing interviews and sharping up my resume. Even though I got a job from an opportunity I found from LinkedIn, but the job assistance from the Academy was definitely helpful.  

To sum it up, I have learned a lot from the bootcamp and I also spent a lot time to digest them after I finished the bootcamp.

1. Because EDA and Data Visualization are so import, it would be great to have a example on those that we could have walked through or done together. Or for at least one of the EDA and Data Visualization projects, we work on the same topic and review it together. 
2. More hands-on Spark excise. I would suggest using of Databrick community version or student excise.

This class touched upon the basic to more sophisticated application of R programming through use of available and popular packages.  Amy has been a very caring and responsive instructor.  She is always willing to help and offer insights in selecting appropriate approach to gain efficiency.    The materials covered  were vast and familiarity requires a lot of practice.  The classroom work and homework gave the students the push to get more involved and provide the hands-on experience. This class was engaging. 

Hiring a Data Scientist in NYC is a full-time job on its own. Your inbox may be exploding with hundreds of applications, yet it is extremely challenging to find the right candidates. While I was in the middle of sorting through resumes I was invited to a Data Science Academy happy hour by Vivian. I heard of Data Science Academy but never thought to look for hires there.

As soon as I arrived I met Vivian and her team who already knew what type of Data Scientist I was looking for and introduced me to a few students. I was so impressed by them that I decided to invite them for an interview and within one week hired one of the graduates. He has been working with us for a few months and has already made a difference. His amazing technical skills combined with a very strong business acumen helped him take a lead on critical corporate projects and execute them with excellence.

I strongly recommend all employers looking for Data Scientists to contact Data Science Academy as it truly helps prepare the next generation of data scientists for real world jobs.

The NYC Data Science Academy bootcamp is everything I hoped it would be. I entered as a recent Mathematics PhD unsure of my job prospects and left well prepared for the data science job market. I was offered a job within weeks of graduating and now have a great job as a data scientist.

The bootcamp is an intense experience that will reward you if you put in the appropriate effort. The lectures are fantastic and the instructors are incredibly helpful with your projects. They will offer you as much help and guidance as you want (they were, more often than not, there before me and were still there when I left). 

Job placement assistance was also crucial to my success. I met my current employer through a bootcamp and felt more then prepared during my interviews. You really feel that you are given individual attention and they will do everything in their power to help you find a job.

Some tips to prospective students:

• Become familiar with the basics of R and Python (especially Python as this is introduced later in the bootcamp when you are becoming busy).  I didn't enter the bootcamp as a programming expert but I found having some knowledge was a large advantage. A few months of learning the basic syntax and doing exercises in your spare time should be sufficient (that is approximately what I did).
• Enter if you are willing to commit 3+ months to your career (you will be studying and preparing for interviews after you finish the bootcamp).

NYC Data Science Academy was a fantastic introduction to the world of data science. I still stop by the offices to say hi to instructors and students and discuss life. I would make the decision to join the bootcamp again 10/10 times. 

I really enjoyed the R course with instructor Amy Ma. The content is very practical. It can be directly applied to solve real-world data analysis problem.  We had many in-class coding exercises, which helped us understand the R syntax. 

Also, Amy tried her best to provide a lot of useful resources. We could tell that she is very passionate about what she is doing, and she is patient with students. We could reach her after class through email, even the course was finished. I highly recommend this course to anyone who is interested in data analysis and wants to learn R from the beginning. 

I really enjoy taking the R course with Amy Ma. She's patient and thorough. Her analogies made it easier  for me to understand the R syntax. I really like the in-class coding exercise and it was good to practice what I have learned. Amy's class is very interactive. She doesn't just talk off the slides. She always codes with us and shows us different ways of doing the same thing or breaking down the code part by part. She would compare and contrast the nuances between different commands, which was quite helpful. 

The NYC Data Science Academy's 12-week bootcamp is an intense, well-thought out and comprehensive program that accelerates one's immersion into the world of data science. Having worked in IT infrastructure consulting for 8 years at one of the Big Four, I wanted to shift career orientation and focus more on data science, elements of which had begun to interest me over the course of several client projects.

Comments on the program:

• Strong teaching staff who is clearly passionate about teaching data science as evidenced by the late evenings and weekend support
• Focus on concepts and skills that are actually relevant to the marketplace
• Broad and dense curriculum (R, Python, machine learning, Hadoop, Spark, MongoDB, etc) to maximize learning in a limited timeframe
• Flexibility and nimbleness of the program to adapt to feedback from alumni, employers and the market to maintain the relevancy of every topic taught
• Good mix of lectures, homework and projects
• Helpful job prep activities such as interview coding simulations (and others that I won't spoil)
• Eclectic mix of students (though this may vary by cohort) that allowed us to learn from each other's diverse backgrounds and areas of expertise, and develop deep personal friendships and professional relationships
• Opportunity, if selected, to present projects at Data Science Meetups which offers networking opportunities as well as practice of presentation skills

Suggestions for improvement:

• As data science is a dynamic and evolving field, keep up with the latest advances such as improvements in R packages and update the curriculum accordingly
• Give harsher constructive feedback to students when required
• More transparency on student evaluations overall and for Meetup project selections as well

Recommendations for aspiring students:

• Be committed: the bootcamp is demanding but that is by design. It's an investment; you get out what you put in. Don't show up late, don't ignore the homework. If you do, it's your loss
• Don't fall behind: there's a lot of work and learning a very rapid pace, not to mention homework almost daily
• Be proactive: reach out to the TAs or the instructors for feedback (not just help), read up on machine learning outside of the bootcamp, talk to your classmates on how to collaborate
• Manage your time: you will have to juggle many responsibilities and will most likely feel overwhelmed in general. That is also a skill you will need in the professional world
• Learn to be autonomous: don't run to the TAs or the teachers for help as your first recourse. Google, StackOverflow and other resources most likely already ahve the answer you are looking for. Only when you have made a decent attempt at figuring out the problem should you ask for help. After the bootcamp, that safety net will no longer be there so it's best to practice early

Overall, very positive experience. I can't speak to the job placement assistance as I returned to my original firm, but regardless, I'll maintain the great relationships built with the Academy.

NYC Data Science Academy's 12-week bootcamp was a life-changing experience. It was an intensive 3 months but definitely helped me transition from healthcare to data science.

Comparing against other bootcamps, I decided to go with NYCDSA for its thoughtfully planned schedule. From the large volume of content to the scheduling of each day, it had what I needed to succeed. Each day consisted of lectures with really useful slides and concluded with time for homework and projects in the afternoons. The allocated time in the afternoons gave us time to digest what we learned and the homework assignments reinforced the new knowledge.

Coming from a background in biomedical engineering and biological sciences, I was used to being told everything I had to know. The bootcamp gives a lot of information with its detailed lectures but also challenges students to find their own answers. For example, a homework assignment had a problem mentioning a function that was not mentioned in lecture. We had to research it and learn how to use the function before we could do the problem. This taught me how to be resourceful and find answers on my own instead of relying on someone to tell me everything. Because data science is a growing field, you cannot expect to learn everything in a short period of time but should know how to find solutions on your own.

Being challenged to learn may sound intimidating but it really isn't when you're surrounded by supportive peers and staff. The staff are extremely hardworking and friendly. They stay late, sometimes even until 11pm and they love what they do so they won't be grumpy or annoyed at you. They also come in on the weekends and will help with homework assignments and projects if you require it. The TAs are excellent resources who are passionate about data science and motivate you to always do better.

Advice: Try to familiarize yourself with the suggested R and Python readings before you begin the bootcamp so that you can spend less time trying to understand concepts and more time on the data science parts.

Even after graduating from the bootcamp, I still feel like family around them. The instructors and staff do not forget you and continue to help with your job search. Vivian actively connects you with resources and posts jobs you may be a fit for. She also holds coding review sessions so that we are better prepared for interviews.

Job Prospects: Besides the countless data scientist and analyst job posts found online, NYCDSA hosts a hiring party at the end of the bootcamp that connects recruiters from multiple companies with students. It gives you a big opportunity to speak with recruiters and build connections. I interviewed with one of the hiring managers from the event and got an excellent data scientist job approximately 1 month after graduation. You have other opportunities to stand out from crowd when Vivian reaches out to her contacts and sends in your resume so you're not just another applicant in the pool. There are tons of jobs and the support from NYCDSA is outstanding.

I have no negative memories of the bootcamp besides the late nights spent on projects but you get as much as you put into the program. Overall, the bootcamp does a good job preparing you for a career in data science as long as you work hard for it. Your peers will be your friends and consultants for your data science problems. I would join another cohort at NYCDSA if I could!

I took the data analysis and visualization with R class in June. The lecture and presentation was very thoughtful. The instructor was very helpful. After taking the course, I can actually pick up some skillsets like doing some descriptive analysis and creating some plots. But more practice is needed outside the classroom if you really want to get something from the course.

Overall, I would recommend the course to people who have some coding experience and would like to learn more on the topic.

I finished the bootcamp yesterday. I have a BA with honors in Math, an MA in Applied Math, and an MS in Civil Engineering. I have 8 years of work experience in both the private and public sectors. I committed to this program to help me transition to becoming a data scientist. I had minimal professional programming or statistics experience when I applied.

• I bought a new computer and studied statistics, R, and Python for over a month before my first day in the bootcamp.
• I worked more intensely and consistently at the bootcamp than I've worked on anything over a 3-month period, either in school or in a job.
• The staff are maybe the bootcamp's finest feature. The instructors are technically proficient, patient, and articulate. Somehow, I didn't feel intimidated, more inspired. In three months, every time I requested support, I was supported.
• The office has a simple aesthetic and is well-equipped and well-maintained. It is often crowded, but overall a space where I felt focused and could work.
• I gave the curriculum 4/5 stars. I found the material fascinating. There were many times when I simply didn't keep up. There is a lot of material. Read that sentence again. The curriculum moves so quickly that it often felt like a day-tour of Europe.
• I found my classmates highly-educated, focused, and committed. I forged some excellent working and social relationships.

Overall, I recommend this bootcamp under the following conditions:

1. You will commit yourself to this program for 3 months. For these 3 months, this is what you will do during the day and in the evening, weekdays and weekends.
2. With whatever time you have before the bootcamp, you will study statistics, R, and Python.
3. You accept early on that sometimes it will be acutely stressful and overwhelming.

Having completed the bootcamp, I see the value in Vivian's approach: by working at capacity among such skilled staff and focused students, my skills and knowledge developed quickly. Overall, this bootcamp worked well for me and I'm glad I did it.

This is an awesome program you will not regret attending! 

I was in the Jan-April 2016 cohort. The course covers everything you need to know to apply for data scientist jobs. We started from the fundamental of stats in R, and moved into machine learning in both R and Python. In the last two weeks we also got a fair exposure to big data tools like Hadoop and Spark. The instructors we have are AMAZING!!! They are super knowledgeable and also very passionate about data science. TAs are the most hard working group of people I know! They really try their best to help you. Students at the bootcamp are impressive as well. Most of them either have a phd degree or have significant/successful work experiences prior to joining the bootcamp. We did five projects during the bootcamp which you can totally show off during your job interviewsss! And you will have a least a few job interviews guaranteed during/after the bootcamp. They really tried their hardest to help you preparing and securing job interviews. I personally had at least 5 interviews while I was still in the bootcamp, and was hired only two weeks after the bootcamp ended.

The program wasn’t easy, you will have a ton of homework and projects to do, but they are always there to support and help you. I would recommend this 12 weeks bootcamp to anyone who wants to be a data scientist or simply interested in data science.