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NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.84 ( 289 reviews )

NYC Data Science Academy offers 12-week data science bootcamps in New York City. In these programs, students learn beginner and intermediate levels of Data Science with R, Python, Hadoop, Spark, Github, and SQL as well as popular and useful R and Python packages like XgBoost, Caret, dplyr, ggplot2, Pandas, scikit-learn, and more. Once the learning foundation has been set, students work on multiple projects through the bootcamp. The program distinguishes itself by balancing intensive lectures with real world project work, and by the breadth of its curriculum. Throughout the program students work alone and in teams to create at least four projects that are showcased to employers through multiple channels; private on-campus hiring partner events, student blogs, meetups, and filmed presentations. 

Ideal applicants should have a Masters or PhD degree in Science, Technology, Engineering or Math or equivalent experience in quantitative science or programming. Candidates with BA’s who have appropriate experience are also considered.

Throughout the data bootcamp, students are assisted in preparing for the employment process through resume review and interview preparation. NYC Data Science Academy works closely with hiring partners and recruiting firms to create a pipeline of interest for its students.

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  • 12-Weeks In-Person Data Science Bootcamp

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    Start Date July 6, 2020
    Cost$17,600
    Class size50
    LocationNew York City
    In this program students will learn the modern data analytic techniques and master the requisite skills, such as Python and R programming languages as well as Hadoop, to address real-world data science problems. Throughout the program, students work alone and in teams to create at least five projects that are showcased to employers. Along the way, students will have assistance in preparing for the job search through resume review, interview preparation, and opportunities to interview with our hiring partners. Successful completion of the curriculum will present a certification of graduation certified by the New York State Board of Education.
    Financing
    Deposit$5,000
    Financing
    $397.88 pm for 60 months
    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
    More Start Dates
    July 6, 2020 - New York City Apply by June 19, 2020
  • Big Data with Amazon Cloud, Hadoop/Spark and Docker

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    Data Science, Hadoop, Spark, Data Structures, Python, Cloud Computing
    In PersonPart Time5 Hours/week2 Weeks
    Start Date None scheduled
    Cost$2,990
    Class size10
    LocationNew York City
    This is a 6-week evening program providing a hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class. We will do our work on AWS (Amazon Web Services); instructions will be provided ahead of time on how to connect to AWS and obtain an account.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelStudents are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful.
    Placement TestNo
    InterviewNo
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

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    Start Date June 13, 2020
    Cost$1,590
    Class size20
    LocationNew York City, Online
    This five week course is an introduction to data analysis with the Python programming language, and is aimed at beginners. We introduce how to work with different data structure in Python. We covered the most popular modules, including Numpy, Scipy, Pandas, matplotlib, and Seaborn, to do data analytics and visualization. We use ipython notebook to demonstrate the results of codes and change codes interactively during the class. Our past students include people with no programming experience or those who have minimal exposure to Python. Students told us our classes are very informative, engaging, and hands-on.
    Financing
    DepositN/A
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelKnowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) Familiarity with concepts of list comprehension and for/while loop
    Placement TestNo
    InterviewNo
    More Start Dates
    June 13, 2020 - New York City Apply by June 12, 2020
  • Data Science with Python: Machine Learning (Weekend Course)

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    Data Science, R, Artificial Intelligence, Machine Learning
    In PersonPart Time7 Hours/week5 Weeks
    Start Date June 13, 2020
    Cost$1,990
    Class size10
    LocationNew York City, Online
    This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. This includes linear regression, Naïve Bayes classifiers, logistic regression, linear discriminant analysis, cross-validation, bootstrapping, feature selection, regularization, model selection, SVM, decision trees, random forest, PCA, K-Means, and Hierarchical clustering. In addition, this course teaches the basics of natural language processing. After successfully completing this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelCompletion of Data Science with Python: Data Analysis; Data Science with R: Machine Learning
    Placement TestNo
    InterviewNo
    More Start Dates
    June 13, 2020 - New York City Apply by June 12, 2020
  • Data Science with R: Data Analysis and Visualization (Weekend Course)

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    Data Science, R, Data Visualization, Data Analytics , Data Structures
    In PersonPart Time7 Hours/week6 Weeks
    Start Date None scheduled
    Cost$2,190
    Class size15
    LocationNew York City, Online
    This course is designed to provide a comprehensive introduction to R. Students will practice programming and analyzing data with R. Students will learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models to data. In addition to a theoretical framework in which to understand the process of data analysis, this course focuses on the practical tools needed in data analysis. This course also covers the creation of dynamic reports with the knitr package in R as well as the creation of dynamic dashboards with R Shiny. By the end of the course, students will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting the code.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelBasic knowledge about computer components Basic knowledge about programming
    Prep WorkNone
    Placement TestNo
    InterviewNo
  • Data Science with R: Machine Learning (Weekend Course)

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    Data Science, R, Machine Learning
    In PersonPart Time7 Hours/week6 Weeks
    Start Date None scheduled
    Cost$2,990
    Class size40
    LocationNew York City, Online
    This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems. Unit 1: Foundations of Statistics and Simple Linear Regression Unit 2: Multiple Linear Regression and Generalized Linear Model Unit 3: kNN and Naive Bayes, the Curse of Dimensionality Unit 4: Tree Models and SVMs Unit 5: Cluster Analysis and Neural Networks Final Project After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelKnowledge of Python programming Able to munge, analyze, and visualize data in Python
    Prep WorkKnowledge of R programming Able to munge, analyze, and visualize data in R
    Placement TestNo
    InterviewNo
  • Deep Learning with Tensorflow (Weekends and In-Person Only)

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    Start Date None scheduled
    Cost$2,990
    Class size15
    LocationNew York City
    Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelObject-oriented programming, ideally Python (introductory course: https://nycdatascience.com/courses/introductory-python/) Simple shell commands, e.g., in Bash (tutorial of the fundamentals: https://learnpythonthehardway.org/book/appendixa.html)
    Placement TestNo
    InterviewNo
  • Full-time Online Data Science Bootcamp

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    Start Date July 6, 2020
    Cost$17,600
    Class size25
    LocationOnline
    This program was designed for students that have the time to be a full-time student, but can't commute to our school. Students will be placed on a rigorous curriculum that spans from 9:30 AM to 6:00 PM EST as well as have access to prerecorded modules with over 1000 coding challenge questions on online learning platform for additional practice. In addition, they have access to dedicated TA’s as well as the larger network of a shared slack channel between both in-person and remote bootcamp students. Classes: You will learn streamed lectures as well as have access to prerecorded modules and coding questions for additional practice. Personalized Job Support: Students also have access to the full resources of NYC Data Science Academy to help them find their dream job upon graduation. Our curriculum covers the expanse of all the skills required in the data science industry. We cover both R and Python as well as Machine Learning Theory, Big Data, and Deep Learning.
    Financing
    Deposit5000
    Financing
    • Full Tuition Total $17,600
    • Climb Credit Loan $400* pm for 60 months
    • SkillsFund Student Loan $397.88 pm for 60 months

    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
    More Start Dates
    July 6, 2020 - Online Apply by June 19, 2020
  • Introductory Python (Evenings)

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    Start Date None scheduled
    Cost$1,590
    Class size40
    LocationNew York City
    This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web. This Introductory Python class runs over four weeks, with five hours of class per week (split into 2 ½ hour evening classes). Classes will be given in a lab setting, with student exercises mixed with lectures. Students should bring a laptop to class. There will be a modest amount of homework after each class.
    Financing
    Deposit$1590
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelThis Introductory Python class is designed for computer-literate people with no programming background who wish to learn basic Python programming.
    Prep WorkIn the class, we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video. Link: https://vimeo.com/160172414
    Placement TestNo
    InterviewNo
  • Part-time Online Data Science Bootcamp

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    Start Date July 6, 2020
    Cost$17,600
    Class size25
    LocationOnline
    This is an online part-time self-paced program. Students have 4 - 10 months to complete this program. The curriculum is the same as our on-campus program, with full-financing options, career support and with a one on one support from our mentors. This program is designed for students that work full-time and are not able to quit their jobs. Our curriculum is drawn from data science engagement with corporate consulting and training, hiring partners and active industry participation. Our remote bootcamp ensures that students achieve a very high level of proficiency. Students are expected to dedicate themselves fully to this program and fulfill all the requirements, which include completing lecture videos, daily homework, and four projects. The Remote Bootcamp is built as a collaborative environment utilizing online chat and meeting systems. Students also have the opportunity to collaborate on homework, projects, job applications, interview preparation, paired programming, and even further through our extended alumni community. We work closely with hiring partners and recruiting firms to create a pipeline of interests for students. Each student receives one-on-one support with job searching and access to all kinds of job assistance resources, including coding reviews, interview prep, resume workshop, and access to our exclusive hiring partner network.
    Financing
    Deposit17600
    Financing
    • Full Tuition Total $17,600
    • Climb Credit Loan $400* pm for 60 months
    • SkillsFund Student Loan $397.88 pm for 60 months

    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
    More Start Dates
    July 6, 2020 - Online Apply by June 19, 2020

Shared Review

  • Great Bootcamp
    - 3/28/2019
    Chaoran Chen  User Photo
    Chaoran Chen • Data Engineer • Graduate Verified via LinkedIn
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    Great Bootcamp. 

    The program is very comprehensive and intense. The syllabus is well structured. They make sure your time is only spent on most popular and useful technologies which are needed for a Data Scientist. 

    The instructors are very responsive. They also hold events to build the relationship between you and the potential hiring partners.

  • Daniel Bubb  User Photo
    Daniel Bubb • Principal Software Engineer • Graduate • Verified via LinkedIn
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    NYC Data Science Academy was essential in helping me to make the transition from a career in academia to the field of data science.  It is a very intensive course that is filled with statistics, coding, machine learning, natural language processing, big data, and other topics.  Frankly, there were too many topics for me to apprehend while the bootcamp was running, but one of the great features of the course is that you are able to go back and review key lectures - which I periodically do as my understanding of the field deepens.  Another great strength of the curriculum is that you are exposed to a variety of instructors who have different backgrounds and approaches.  I can think of distinctive strengths for Drace, Aiko, Luke, and Zeyu in their presentation and shared perspective.  Vivian and Chris gave terrific seminars on seeking employment and how to interview.  Finally outside lecturers such as Bernard Ong held court and imparted their wisdom and that comes from a wealth of experience in the field.  

        One other strength of the program is in the variety of lecturing styles and the mix of labs, guided activities, and more conventional lectures. I think this allows people with a variety of learning styles to flourish and speaks to the thoughtfulness in constructing the curriculum. The projects are essential to applying the concepts and give you an opportunity to sharpen your coding and analysis skills.

        In the end, as I said previously, it was all a bit much for me to take in during the bootcamp, but there are a series of materials - such as Jupyter notebooks, pdf slides, and videos that I continue to refer back to as I progress in the field.  For me, the challenge was less about the difficulty of the material but the sheer volume.  I tend to get interested in something and don’t want to hear much else until I feel like I have a deeper understanding.  Only then do I like to move on. Were I to do it over, I would be a little more disciplined about indulging my curiosity and pay a little more attention to the flow of material and presentation because they really know what they’re doing and what you need as you progress.

     

    tl,dr:

        I highly recommend this Bootcamp, it was of tremendous value to me.  12 weeks of instruction from deeply knowledgeable instructors who possess keen business acumen and know how to help people get jobs. It was a very enjoyable experience for me to be surrounded by interesting and smart people from all types of backgrounds and each with something unique to contribute to the cohort.

  • Fellow
    - 8/23/2018
    Sam Marks  User Photo
    Sam Marks • Graduate Verified via LinkedIn
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    I had a truly rewarding experience at NYCDSA. The staff is approachable and friendly, and the material is relevant for a wide range of data professions.

    The program opened doors to several opportunities for me and the staff sets time aside to help you position and present yourself well for job interviews. I accepted a job from one of NYCDSA's hiring partners, and felt very well prepared for the interview process. Five stars!

    Side note: One recommendation I have for those less familiar with coding (which I was), is to embrace the "have you tried googling that?" culture within Data Science and programming as a whole. While the tendency at a supportive bootcamp is to ask your instructors how to debug a line of code or build an ML template, more often than not the answer is a google search away, and there are TONS of excellent resources out there. Patience and getting comfortable with the process of finding the answers to your questions online is absolutely essential.

  • Michael Caballero  User Photo
    Michael Caballero • Data Scientist • Graduate Verified via LinkedIn
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    I highly recommend NYC Data Science Academy to folks driven to pursue a data science, analysis, or engineering career. I participated in the 12-week onsite bootcamp with the Spring 2018 cohort and can attest to NYCDSA helping graduates land roles in each of these areas.

    There are two reasons that NYCDSA was the right fit for me to jumpstart my career.

    First, I was attracted to the academy’s strong regional network and job placement support. Vivian and the NYCDSA staff are responsive to your career interests and will put you in touch with relevant folks in the field. In addition, the NYCDSA curriculum is continuously re-evaluated for relevance. While NYCDSA helps bridge a career transition, the challenging task of skill acquisition and evaluation is on you!

    Second, I wanted to participate in a program with diverse learners. Bootcamp attendees range from STEM PhDs like myself to folks with BA/BS with professional experience in analytical fields. It was an exciting process to learn from each other as we worked on group projects or talked about homework challenges. Exposure to working on teams with divergent backgrounds is extremely valuable in a practical sense and reflects how some data science teams are structured.

    Overall, NYCDSA has established a process that works and is committed to continuous improvement. It was a pleasure to participate in this program with friendly students and supportive staff. NYCDSA helped me expand my technical skillset and secure a full-time data scientist role.

  • Franklin Dickinson  User Photo
    Franklin Dickinson • Graduate Verified via LinkedIn
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    I was part of the full-time spring 2018 cohort, coming with a BA in social sciences and a previous business analyst role. I had down prior intro python/R/maching learning classes on Coursera and they proved a good starting point.

    The bootcamp mainly involves buidling a portfolio of projects, each utilizing a new skill taught through the weeks, using mainly first R followed by Python. Also time is spend on SQL in the beginning and intro neural networks and big data architecture. 

    The program is legit and skills gained are real.

    While instructors and management were always friendly and making efforts to assist when asked, everyone was very busy and there was not an intense structure. As a bootcamp, the progam focuses more on flexibilty and less on structure than a traditional graduate program and thus you must be humble, self-motivated, and vocal to succeed. There is a good broad strategy in the cirriculem but the various instructors could be better in tune with eachother in this goal. Management always asked our feedback and have itineratively improved the bootcamp each cohort.

    Students were from a variety of backgrounds, all well-educated and friendly. Many international students. 

    From early on, management was focused on helping us gain employment after the bootcamp and have remained so after the program ended.

    Now 2 months later, I just recently received a full time entry-level data science consulting position. I am very excited and credit my time at the bootcamp in opening up the new opportunity. 

     

  • Great experience
    - 8/21/2018
    Abbey Chen  User Photo
    Abbey Chen • Data Science & Machine Learning Analyst • Graduate Verified via LinkedIn
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    I appreciate all the hands-on projects/assignments opportunities which I did during the data science boot camp. It really gives me chances to apply the machine learning theories to real-world problems. Also, the team is supportive. The teachers, teaching assistants, career coaches are very helpful and experienced. I greatly enjoy joining the boot camp at the NYC Data Science Academy.

  • Immersive Bootcamp
    - 8/21/2018
    Jacob Ralston  User Photo
    Jacob Ralston • Algorithm and Machine Learning Engineer • Graduate Verified via LinkedIn
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    I attended the 12 week in-person immersive bootcamp and am very happy I did.  I have a PhD in pure math (no computers, no numbers, so my coding experience was limited to "having fun" with Python) and spent my first 2 years post graduate school working at a hedge fund (in a non-technology role) and wanted to get into data science and engineering.  I genuinely believe that with enough personal motivation and drive a bootcamp is the most effective and efficient way to go from 0 to 60 in this field (note that "attending" and "absorbing" at the bootcamp alone is not enough - the drive, time, and energy ultimately must come from you - they can only set you up for success).

     

    At the bootcamp I learned the essentials of SQL, R, and Python - not just the important packages like Numpy, SKL, etc. but how to pick up a new package and learn how to use it (a more important skill than learning any particular package).  The project work was also extremely valuable. The scopes of the projects (there's 4 of them) get wider as the program progresses (from basic visualization and RShiny app construction to open-ended real-life consulting for companies at the end of the course).

     

    During my interviews after the bootcamp I was certainly tested on my coding and ML knowledge, but I spent a lot of time discussing my project work and results - again, the bootcamp presents you with opportunities and resources, it can't do it all for you.

     

    If you plan on enrolling (which you should think seriously about, it's a large investment of both your time and money) I would say you should prepare as much as possible as you'll leave the bootcamp with what you bring to the table (in terms of skills, abilities, drive, and preparation) - I've seen people who leave the bootcamp ready to be "senior data scientists" as well as people who leave knowing how to code and how to use some ML packages (a big difference).

     

    If you're looking for a bootcamp, I highly recommend NYCDSA as they truly care about the students, have a LOT to offer curriculum-wise, project-wise, and importantly their network in NYC data science jobs is EXTENSIVE (and very valuable).

     

  • Peter Yang  User Photo
    Peter Yang • Graduate Verified via LinkedIn
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    The curriculum of NYCDSA bootcamp is great and well-designed. In the first four weeks, you will learn Python, R and SQL which will lay a solid foundation for your data science learning. In the next four weeks, you will learn a variety of machine learning models. In addition, they will teach big data tools and deep learning.

    The instructors at NYCDSA bootcamp are excellent as they all have deep knowledge and significant experience in data science. They make complex concepts and skills easy to understand and master. The instructors teach classes in a good pace and often pause to answer any questions that students ask in class. After class, if you have any questions, they will also try their best to answer them.

    During NYCDSA bootcamp, four projects will be offered not only to improve your data science skills but also to enrich your resume. The projects are really interesting and challenging. When you are working on the projects, you will be able to learn and apply data visualization, web scraping, machine learning theories and skills, big data tools and deep learning. For the capstone project, various corporations and non-profit organizations will come to the bootcamp and pitched their real data science problems to the students, which will gave the students the experience of solving real data science problems.

    For the job assistance, the hiring team at NYCDSA will work very hard to get you jobs. During the bootcamp, job placement lectures will be given to teach you how to prepare for interviews. In addition, they will have several guest speakers and alumni give lectures about the current job market and suggestions on how to get a data scientist position. Before you graduate, they will help you polish your resumes to make them stand out. Right after the bootcamp ends, they will hold a hiring partner event where you will be able to network with employers from a lot of companies.

    The bootcamp at NYCDSA offered us an opportunity not only to obtain significant experience and expertise in data science but also to expand our career options. After going through the bootcamp, people with various backgrounds can apply the skills they learned from the bootcamp and their domain knowledge to start their new career and explore their interest in the field of data science, which I think is the most important benefit NYCDSA bootcamp offers

  • Marissa Jooy  User Photo
    Marissa Jooy • Data Analyst • Graduate Verified via LinkedIn
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    I am extremely happy with my decision to attend this boot camp. Before this, I was very uncertain about what I wanted to do as a career path and never thought I would want a job that involved coding. With the excellent teaching, support, and guidance of NYCDSA staff and teachers, I found my passion. Within 3 months I gained skills in Python, R, Github, and SQL. Not many boot camps teach both R and Python, therefore this gave me a leg up against other people in the job market.

    I graduated college with my Bachelors of Science in May of 2017, while many of my cohort-mates had a Master's or Ph.D. and had been working for numerous years. For this reason, I did not believe the boot camp was for me. It is not an easy course, but with hard work and dedication, it can definitely be done. I felt prepared for the job market once the bootcamp ended and really appreciated the job support provided by NYCDSA staff. They are extremely committed to getting you a job and do everything they can to help you and guide you. Because of the tools and knowledge I've gained from this boot camp, I've landed my dream job as a Data Analyst for MoveOn.org.

  • Lakshmi Prabha Sudharsanom  User Photo
    Lakshmi Prabha Sudharsanom • Data Scientist • Graduate Verified via LinkedIn
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    I took the intensive 12-weeks bootcamp at NYC Data Science Academy. I am a postdoc in India. I had been writing graph algorithms for social network analysis, analyzing data using MS-Excel and mining data using graph theory. I decided to make a transition to industry. 

    I researched for about six months to choose a proper academy to learn data science. I came up with a decision to join NYC Data Science Academy so that I could learn both R and Python within three months. But, I learnt more than I thought. I gained hands-on experience in Python, R, SQL, Spark, deep learning, Hadoop and Hive.

    The materials are technically sound for job interviews. Many experienced professors and industry professionals design and teach the materials. The course laid a solid foundation in data science. The people in the academy show keen interest in every student to understand the concept, in resume writing and above all, to get a job.

    With their continuous guidance, I got hired as a data scientist in New Delhi, India. My decision to join the academy to learn both R and Python simultaneously, has been proved to be a wise decision. I wanted to 'eat, live and breathe' data science and I do it today because of the academy. Thanks to the academy.

  • David Kogan  User Photo
    David Kogan • Graduate Verified via LinkedIn
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    I attended the 12-week data science bootcamp right after graduating from college with an engineering degree. I highly, highly recommend this course for anyone with some coding/stats background looking to work in data science or machine learning. I'll note what I believe are the most important features of the program with bullet points so it's easy to read:

    • The course is rigorous but not overwhelming -- if you put it a lot of work you will get a lot out of it, and there are a lot of TA's, classmates and teachers to help if you need it.
    • The coursework is oriented towards practical application in the real world. This is what stood out the most to me in contrast to my undergraduate education. The teachers at NYCDSA do a great job of teaching the theory and then explaining how concepts tend to actually be applied in the real world.
    • All the class content is still accessible after the course is done -- it is EXTREMELY valuable to have all this stuff permanantly saved on your computer.
    • They help you get a job after you graduate
  • Mitchell Hung  User Photo
    Mitchell Hung • Graduate Verified via LinkedIn
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    My career before NYCDSA was one without numbers or Excel spreadsheets; past me wouldn't have dreamed of touching complex data modeling algorithms or cloud computing infrastructure. If you're like me, you probably are expressing some hesitation at how much a 3 month bootcamp can give you, especially when you're competing against graduates in STEM fields for a quant position.

    No fear, though! NYCDSA gives you precisely what you need to succeed in today's competitive data science job market. During the bootcamp, you'll become well versed in almost all the standard tools and methodologies that data scientists use to do their jobs. While the curriculum isn't perfect (I definitely thought some days and sessions were on superfluous material, e.g. too many days spent on web scraping, and way too much focus on R as most companies are transitioning towards Python), it is more than sufficient to prepare you to compete with others.

    Six months ago I was a journalist. Now I'm doing cutting edge data science at my new firm. Couldn't have asked for a better outcome.

  • Daniel (Donghyun) Kang  User Photo
    Daniel (Donghyun) Kang • Data Scientist • Graduate Verified via LinkedIn
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    Since 2002, I had served as a wireless communication system design engineer for Samsung Electronics, where I have developed optimal solutions based on the analysis, simulation and modeling of filed data and system features. One of my latest works was finding adaptive solution for fast tree search avoiding local minima and maxima. 

    To boost up my skills and map into diverse field, recently finished an immersive data science boot camp where I completed four projects including machine-learning projects where I deployed predictive models using extreme gradient boosting, random forests, support vector machines, and logistic regression. Work experience on a data science project - Capstone project - for a big CPG company was a huge benefit as a data scientist. As a team, we analyzed product trends, competitor's products and its reviews, customer satisfaction ratio by using NLP such as TF-IDF(term freq/inverse Document freq), Topic modeling(LDA; Latent Dirichlet Allocation), Sentimental analysis and then we delivered comparative analysis results, analysis tool to the client company. Through this project, I got convinced that customer reviews and analysis of competitors tell a lot information not only for the current problems but also the next business opportunities.

    Throughout this boot camp, I could have an experience on the very projects which are closely related to the data science industry, and make portfolios which could demonstrate qualification for related industry requirements. Eventually, I could get a job which requires data science such as machine learning and natural language processing by connecting my previous work exprience to portfolios in NYC data science academy.

    If you want to be a actual data scientist, NYC Data Science Academy could be a best choice ever.

Thanks!