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NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.84 ( 288 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 June 2, 2020
    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
    More Start Dates
    June 2, 2020 - New York City Apply by June 1, 2020
  • 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

  • David Corrigan  User Photo
    David Corrigan • Bioinformatics Data Scientist • Graduate Verified via LinkedIn
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    I joined NYCDSA for their 12-week bootcamp in fall 2018. I had graduated with a PhD in biology that summer, but I was looking to improve my data science skills (which were limited to basic knowledge of R) to work towards a more quantitative scientific career.  My decision paid off, as I am now working as a bioinformatics data scientist after a relatively short (~2 month) job search and interview process.

    NYCDSA has a great deal to offer to anyone looking to move into data science. They provide excellent career counseling throughout the course and connect students to a large number of opportunities in many different fields (finance, marketing, biomedical, business operations, etc) through in-course talks/panels, and a very large career/hiring event after completion. The career guidance is an invaluable aspect of the course.

    During these 12 weeks, I learned more than I thought possible. I was one of those who considered trying to teach data science to myself. After doing the bootcamp, I would now discourage this for the following reasons: 1) The projects: You will complete 4 thorough projects, with experienced instructors and TA’s, which will serve as a critical piece of your resume during the application process. *I specifically went through my projects and code while interviewing for the job that I ultimately accepted. 2) The aforementioned career guidance, which was so indispensable. And 3) You will learn far more efficiently here than you would on your own. There is simply too much material to learn in such a short time – without knowledgeable instructors who know how to plan and execute a curriculum, you will take much longer to master the same amount of material.

    Be warned – the course is very challenging and will require a lot out of you. It is designed, and is most effective, for driven individuals looking to push hard and create opportunities for themselves in the data science field. A small number of the students were frustrated by the amount of content and workload, but in my opinion, NYCDSA is teaching you as much as you need to be competitive as a data science candidate. Read other reviews from people with no coding experience who were hired as data scientists – this is only possible because of the challenging 12-week curriculum offered here.  Bottom line, it’s a great career move if you are properly motivated – I highly recommend it!

  • Shin Chin • Data Scientist • Student
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    I am really enjoying taking the NYC Data Science Academy data science bootcamp remotely. I find their online classroom and materials very effective for learning the fundamentals of data science. The videos are very informative and the material well communicated and taught by the lecturer. The accompanying slides are clearly written and mostly self contained and you can learn a lot, just by reading them. The course is a nice blend of theory and practical programming experience using R and Python and other tools.

    The learning process is aided by doing and submitting the homeworks for the lecture materials, as well as completing project assignments.
    I interact frequently with the TA assigned to me. We often have Google hangout sessions where he helps set up my environment on my laptop like Git, R and Python. We also review my project assignments through Google hangouts where he provides valuable feedback and suggestions. The TA is very helpful, competent and knowledgable.

    I would definitely recommend the online version of this bootcamp.

  • Dani • Graduate
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    I attended the 12-week bootcamp this past summer and had a great experience overall. I was able to build a solid portfolio of data science projects and got a lot of job search support. The curriculum assumes a basic level of statistics knowledge so for someone like me whose background didn't include any it could be frustrating at times. However, they are serious about student feedback so I believe the course will only continue to get better. You can expect to put in a lot of work but as always what you get out depends on what you put in. Ultimately I landed a job I'm very excited about, and I had a positive experience with the course, instructors, and fellow students so I would definitely recommend it to anyone interested in a career in data science.

  • Jason • Research Scientist • Graduate
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    Now I am going to start my new career as a data scientist.  I would like to share my 2 cents for this bootcamp and hope it would be helpful for the coming students.  Also I hope through those suggestions, you can have an idea about the bootcamp.

    1.  Be open - This is a boocamp, not a usual school class or an online course.  So share your experience and expertise with others.  You will learn much more from each other.

    2.  Be in a hurry - This is a short bootcamp, whereas it covers a LOT of content.  So be ready to work hard.  You need to have a sense of urgent that you are going to jump into interviews after 3 months.  Without your own hard working, it is hard to achieve the success after the bootcamp

    3. Be brave - This is a hard bootcamp.  Some students slept less than 6 hrs per day for three months.  Does it sounds scare?  However I would like to say that be brave, after learning with others, you will see your shining point and know how to show your merits to others.  So be brave to take challenges and be brave to share with others.

    4. Be collaborative - I hope after the bootcamp, you are not only getting a good job(s) but also getting a lot of friends.

    Enjoy the bootcamp,

  • Pokman • Graduate
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    I attended the Data Science Bootcamp in summer 2015. It was a very enriching, useful and enjoyable experience. It offered plenty of important things that one couldn't hope to find by taking online courses or reading textbooks. The instructors possessed valuable knowledge and perspectives in the data science industry, and were able to share them with the students through various activities (e.g. lectures, invited talks, meetups, company visits, individual counseling, etc.). Also, students had a lot of opportunities to interact with established data scientists, as well as collaborate with fellow aspiring data scientists on real-world projects.

  • Punam • Graduate
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    Data Science Bootcamp was the best experience in my career. Instructers were not only helpful in teaching the regular materials but also guide you to establish your confidence in yourself to be a Data Scientist. They will help you even after completing your bootcamp. Nice and honest enviroment. 

  • Sam
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    I took both Data Analysisand Machine Learning with Python with Vivian.

    I highly recommend these classes to anyone who wants to take their analytics skills beyond Excel, pivot tables, and averages and into more advanced predictive modeling methods. Luckily, a lot of the work has already been done for us by the developers who created pandas, matplotlib, statsmodels, and scikit-learn. I didn't know anything about these tools prior to taking this class. Vivian makes machine learning easy. At work I can now stand on the shoulders of Python's giants. Pretty cool. Extremely useful.

  • Machine Learning
    - 6/30/2015
    Liz • Graduate
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    I took the Data Science with Python: Machine Learning course and I learned a lot. This course helped me to improve my data analysis and general Python skills. It introduced me to several new libraries and algorithms, most of which I plan to use at work.

    Overall, I had a very positive experience.

  • Bret Fontecchio • Python Developer • Graduate
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    I took Vivian's Data Science course and had a fantastic experience. I networked with Data professionals from the NBA, the Federal Reserve Bank, NYC startups, and more. I learned a lot very quickly and had a lot of fun. It's a nice part of the city and the building has a great startup feel to it.

  • Anonymous • Graduate
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    I spent three months there applied my knowledge on several interesting projects, including Kaggle competition and data visualization of new york crime analysis with shiny (R).  Also the students and instructors are inspiring and motivating.  It was a nice experience for me.

  • g"R"eat
    - 5/19/2015
    Anonymous • Student
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    I sharpened my skills by using different R packages to solve realistic projects.  It is a wonderful experience to solve those unique and challenging projects.

  • current student
    - 3/9/2015
    anonymous • student • Student
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    Great course so far! 4 weeks in and we've learned version control with Git, mapping in CartoDB, visualizations with D3, and machine learning, stats, and programming in R.

    We've also gotten to network with Data Scientists at Google and Enigma and we'll be featured on Interview Jet in a few weeks!

    Smart students and interesting projects are generating many strong portfolios for students. I don't think think any will have trouble finding a job when this is all over.

     

  • Anonymous • data science / technical writing • Graduate
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    NYC Data Science Academy is a great choice for anyone looking for a bootcamp.  They look for students who have a science / engineering/ math background, but they don't require a PhD (though scholarships are easier to get for PhDs).  This school really distinguishes itself partly because of the emphasis on R.  People skilled in R command the highest salaries - and for a reason.  It is the gold standard for data manipulation and for all the most cutting edge algorithms.  I looked all over for a program that taught R (as well as Python) and NYC Data Science Academy is the only one I found!  Bootcamps are 12-weeks long and cover everything a data scientist needs to know to be up and running upon graduation, included R, Python, Hadoop, Raspberry, and much much more.  Vivian Zhang, the primary teacher and founder, is a great data scientiest, statistician, programmner and teacher.  She's also a great person.  Much of the emphasis is on preparing people to get jobs in data science and there is a lot of support in recruiting - including relationships with  firms Vivian has done corporate training for and partnerships with several recruiting firms.

  • Python Intro Class
    - 4/15/2019
    Anonymous • Student
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    5-week class with Anthony Schultz. Solid class in acquiring the basics of python coding. Methodical weekly build coupled with Tony's patient approach made for a good base of python knowledge to move forward. Option of extra time before class to go through assignments or ask questions was very helpful. Does require concerted personal time to solve homework assignments for those with no coding background. Overall, would solidly recommend. 

  • Anonymous • Graduate
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    Tony is a great insctuctor and makes basic Python concepts very accessible. My background going into this course was primarily in SQL, but I felt that the necessary Python concepts were covered quite well in the first session.

    The course provides an overview of the various uses and popular packages in Python and each week focuses on one area. I would recommend getting as much practice as possible early on with basic concepts like for and while loops, as this will prepare you best for the later weeks.

  • Anonymous • Graduate
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    I enrolled in this course with some working knowledge of machine learning, but no prior experience with neural nets or coding in Python. I thought this course was a great introduction to the topic, especially given that we were able to preview the instructor's forthcoming textbook on deep learning. Being able to go back and forth between the book and the lecture notes, and walk through the code together as a class, was really useful for solidifying newer concepts. The instructor was really approachable and friendly, and was good at drawing from various strengths that were present from other participants in the class. A great course, I enjoyed it. Would recommend to anyone who wants to get a high level intro to deep learning in a more structured setting than just an online course. 

  • Anonymous • Sr Software Engineer - Machine Learning • Graduate
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    Attending NYC Data Science Academy is one of the most important and accurate decisions I have made in my life. The academy provides strong training on statistical and machine learning theory. Coupled with 3 real-world problem solving projects and 1 business-oriented capstone project, I gained hands-on insight on how data science address business demands.

    With software engineering background, I always want to relate my software knowledge to solve business problem. I believe the training at NYC Data Science Academy help me step forward my career path closer to what I want to achieve.

    I wish I can attend NYC Data Science Academy at a much earlier date.

  • Highly Recommend
    - 5/23/2018
    Anonymous • Graduate
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    It is a fast paced course. Five weeks of 4 hour sessions but two of them start an hour earlier to go over homework. There isn't so much as a bathroom break so exercise caution with oversized drinks. But in all seriousness it was a very professionally run course. The instructor Anthony Schultz kept things very engaging. His energy didn't waiver or segue into something irrelevant. Anthony was also highly available. He was there early before class to answer questions and available after class as well. He was even responsive to questions I had about the homework pretty quickly. I would recommend taking this course. I found it fun. At the end of the day you really get out of the course what you put into it.  
    The class size was small for my group- maybe 10 or so students- which was nice. I was also impressed by the space of NYC Data Science Academy. Along with the main classroom, the space has a lounge area, coffee and tea area, and a gathering table. There might have been some pictures online but I never saw any. It just made it feel like a more comfortable environment.

  • Review
    - 1/7/2018
    Anonymous
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    I appreciate the sincerity and enthusiasm of the instruction and assistance offered. Wish some things such as the execution of the curriculum would have been more polished and professional. Overall learned a lot and thankful for the experience.

  • PIN short course
    - 6/26/2017
    Anonymous • Graduate
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    The lecturer is very energetic and knowledgeable. After taking the four-week course, I feel like I have basic understandings of Python.

  • Anonymous • Student
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    Tony was a great instructor for this course - very knowledgeable and personable. However, I wish the course was spread out over a larger timeperiod so that we could have more time to digest the material. Each session was four hours long - I can't remember a time when I had a four hour class and expected to focus for that long. My learning could've improved by having more frequent, shorter length classes.

  • A great course
    - 5/16/2017
    Anonymous • Graduate
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    The Data Science Machine learning class can be overwhelming but you end up learning a lot if you work hard and do the homework! The class goes very in depth through all the types of machine learning methods and advanced methods. It gave me a great perspective on what is out there to master.

  • Anonymous • Graduate
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    This class is overall very helpful. It emphasizes the data visualization part, which was more than I would have liked, but still good. It covers lots of material in a short timeframe, and it requires some after class time to work on and get through.I am able to apply what we covered in the class in my daily job. 

  • Anonymous
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    I attended the 3 month Data Science bootcamp. It was a valuable experience: the staff are very knowledgeable and helpful, Christopher Peter Makris is a genius, and you learn a lot of practical skills. However, if you are not American or Chinese, do not expect Vivian to lift a finger and help find you a job. She will make a lot of false promises but she won't deliver.

    She also has a talent for firing all of her staff, including the most hard working and talented ones. She is crazy and has a terrible reputation.

  • Anonymous • Graduate
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    This bootcamp differentiates itself in its teaching of the important theory and statistics. Christopher Peter Makris does an exceptional job of boiling down complex topics into understandable concepts without sacrificing rigor. If CPM is there, you'll learn a lot. I left with a real love of statistics and machine learning theory.

    Without him, there isn't much offered here that you couldn't get on Coursera. The job placement is helpful if Vivian decides she can help you. She basically helps the PhDs get good jobs so she can post the success story, but doesn't spend as much time helping the younger students get admittedly less sexy jobs. 

  • Anonymous
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    I took Data Science with R: Data Analysis and Visualization with Derek in Fall 2016 and I would like to share my experience with the course. Although I will try to stay objective, my observations may be biased as I established and maintained a positive relationship with the instructor throughout the course. Also, as tempting as it is to discuss extraneous details, I will refrain out of civility.

    This course is designed to provide a comprehensive introduction to R and it delivered. From the syllabus that articulates clearly defined learning outcomes to interactive exercises that check for understanding, the course followed a predefined timeline and I walked away with a sense of how I can continue my education in data science. Although students came from various backgrounds, the instructor continuously tried to find the middle ground to provide as much personalized learning as he can.

    Likewise. Derek is a working data scientist who uses R on a daily basis. As a result, he shares plenty of relevant examples to contextualize various approaches and explain why certain measures are in place. In addition to brute memorization, which is an inevitable part of learning computer science, he tries his best to help you understand the logic and make you think and approach problems like a computer scientist.

    To balance the scale, I will comment on areas for improvement. I felt that the course did not have an appropriate adaptive learning measure in place. The teaching material was rather outdated and I sensed that it had not been evolving in proportion to time. However, the instructor was experienced enough to fill the gap. In addition, there seemed to be a disconnect between the instructor and the institution. Throughout the course, there were a number of instances where the instructor seemed unfamilair with the material he had prepared.

    In conclusion, I sincerely enjoyed Derek's class and I suggest considering this class if you are trying to start the first chapter of your journey in R.

Thanks!