NYC Data Science Academy is an educational program teaching key concepts in data science. The qualified teaching staff uses hands-on projects emphasizing real-world applications.
In addition to teaching 1-2 day courses in R, Python, and Hadoop, NYC Data Science now offers a full-time bootcamp with a focus in R. Students will learn the practical skills needed for a data science career while building solutions for real business and industry problems.
Once the foundation of learning has been set, students work on a 2-week, hands-on project with the instructor and mentored by top Chief Data Scientists in NYC. During the final week, students will have the opportunity to interview 300+ hiring companies in New York and the Tri State area.
Ideal applicants should have a Masters or PhD...Read more
|Description||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 have no programming experience and people have little exposure by taking Python class. Students told us our classes are very engaging, interactive, hands-on and have tons of content. Syllabus Each class is 20 hours of classroom guidance with an optional three week-long showcase project of students’ own choices and optional presentation of their projects. Day 1 – Introduction to Python Python is a high-level programming language.You will learn the basic syntax and data structures in Python. Ipython provides a robust and productive environment for interactive and exploratory computing, which is great tool to do scientific computation and education. Introduction to Ipython Basic objects in Python Variables and self-defining functions Control flow Advanced data structures Day 2 – Explore deeper with Python Python is a object-oriented programming language. Learn a little about OOP will help you understand how Python codes work. To do data analysis, the first thing you need to know is how to deal with files which contains data. Sometime the data is dirty and unstructured, you will learn text processing including regular expressions to deal with them. Classes: introduction to object-oriented programming How to deal with files Run Python scripts Handling and processing strings Day 3 – Scientific computation tools There are three modules for scientific computation that make Python as powerful as Matlab: Numpy, Matplotlib and Scipy. Numpy, short for Numerical Python, is the fundamental package for scientific computing in Python. Matplotlib is the most popular Python library for producing plots and other 2D data visualizations. SciPy is a collection of packages addressing a number of different standard problem domains in scientific computing. Numpy Matplotlib (mainly the sub-module “pyplot”) Scipy (mainly the sub-module “stats”) Day 4 – Data Visualization Python can also generate graphics easily using “Seaborn”. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Seaborn Day 5 – Data manipulation with Pandas Pandas provides rich data structures and functions designed to make working with structured data fast, easy, and expressive. The “DataFrame” object in pandas is just like the “data.frame” object in R. Pandas makes data manipulation(filter, select, group, aggregate, etc.) as easier as in R. Pandas Project Demo Day and Certificates From the rudimentary building blocks of programming basics, to data manipulation and use of advanced drawing packages, the course ends with a demonstration of a project of your choice on Project Demo Day. On Demo Day you will access and analyze real data, utilizing the tools and skill sets taught to you throughout the course. Upon successful completion of the course and demonstration of your final project, you will qualify for one of three certificates: Extraordinary Standing, Honorable Graduation, and Active Participation. Certificates are awarded according to your understanding, skill, and participation. No prerequisite needed for the course.|
|Subjects||Data Science, Python|
|Start Date||Rolling Dates|
|Class Size||15 students|
|Commitment||35 hours in class/wk|
|Description||This intensive Data Science with R – Beginner Level course being offered by NYC Data Science Academy is a five week course that will introduce you to the wonderful wold of R and provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon.Why R is important: R is a powerful, comprehensive, and dynamic. Syllabus Each class is 27 hours of classroom guidance with an optional three week-long showcase project of students’ own choices and optional presentation of their projects. This intensive class will introduce you to the wonderful world of R and provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon. From the rudimentary building blocks of programming basics, to data manipulation and use of advanced drawing packages, the course will conclude with a demonstration of a project of your choice on Project Demo Day. For Demo Day you will access and analyze real data, utilizing the tools and skill set taught to you throughout the course.Upon successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing, Honorable Graduation, and Active Participation. Certificates are awarded according to your understanding, skill, and participation. 1. Introduction to R – 3 hours Abstract: Students will learn the fundamental characteristics of the R language, and acquire essential programming skills to apply to future techniques in data handling, analysis, and visualization. Outline: What is R? Why R? How to get help R language resources Installing and using packages Workspace 2. Programming with R – 10 hours Abstract: This session teaches how to manipulate data and use R for all kinds of data conversion and restructuring processes that are frequently encountered in the initial stages of data analysis. We will also cover string processing operations and advanced data capture such as web scraping, API usage, and external database connections. Outline: Data Objects: Vectors, Matrices, Data Frames, and Lists Local data import/export Functions Control Statements Data sorting Merging Data Remodeling Data String Manipulation Dates and time stamps Web data capture API data sources Connecting to an external database 3. Principal Statistical Methods – 7 hours Abstract:This session will cover the essential statistical methods used in data science, focusing on the fundamental building blocks which more advanced predictive modeling hinge upon. Outline: Descriptive Statistics Hypothesis testing Linear Regression Logistic Regression Introducing non-parametric statistics 4. Data Graphics and Data Visualization – 7 hours Abstract: We will cover some basic plotting types and two advanced drawing packages (lattice and ggplot2). Throughout the lectures we will focus on using the two graphing schemes to develop an understanding of the fundamental data visualization processes and to explore the various options of describing and examining data. Outline: Core ideas of data graphics and data visualization R graphics engines Base Grid Lattice ggplot2 Modern data graphics with ggplot2 Intended Audience and Prerequisite Are you interested in better understanding your data, and not so interested in mastering a programming language? Have you tried learning R from a book or website, but have been discouraged? If so, this is the course for you.We assume that you’ve never programmed before (although some experience doesn’t hurt), and we teach you the best tools to help analyze your data. You won’t be a master programmer by the end of this two-day course, but through immersion you will have learned the basics of R’s syntax and grammar, and you’ll have started building an effective R vocabulary for visualizing, transforming, and modeling data.|
|Start Date||Rolling Dates|
|Class Size||15 students|
|Commitment||35 hours in class/wk|
|Description||The class is 35 hours of classroom guidance with an optional 3-week showcase project of students’ own choices and optional presentation of their projects. This class introduces a number of statistical models for supervised and unsupervised learning using R programming language. The goal is to understand the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the R language environment. A selection of important models (e.g. tree-based models, support vector machines) will be introduced in an intuitive manner to illustrate the process of training and evaluating models. Syllabus Each class is 35 hours of classroom guidance with an optional three week-long showcase project of students’ own choices and optional presentation of their projects. Upon successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing, Honorable Graduation, and Active Participation. Certificates are awarded according to your understanding, skill, and participation. Week 1: Introducing Data mining – 7 hours What is data mining and how to do it Steps to apply data mining to your data Primary statistical methods and tests Supervised versus unsupervised learning Regression versus classification problems Review of linear models Simple linear regression Logistic regression Generalized linear models Week 2: Performance Measures and Dimension Reduction – 7 hours Evaluating model performance Confusion matrices Beyond accuracy Estimating future performance Extension of linear models Subset selection Shrinkage methods Dimension reduction methods Week 3: kNN and Naive Bayes models – 7 hours The k-Nearest Neighbors model Understanding the kNN algorithm Calculating distance Choosing an appropriate k Case study Naive Bayes models Understanding joint probability The Naive Bayes algorithm The Laplace estimator Case study Week 4: Tree models and SVMs – 7 hours Tree models Regression trees and classification trees Tree models with party Tree models with rpart Random Forest models GBM models Support Vector Machines Maximal margin classifiers Support vector classifiers Support vector machines Week 5: The Association Rule and More Models – 7 hours Market Basket Analysis Understanding association rules The A priori algorithm Case study Unsupervised learning K-means clustering Hierarchical clustering Time series models Stationary time series The ARIMA model The seasonal model Intended Audience and Prerequisite Practitioners who wish to learn how to execute on predictive analytics by way of the R language; anyone who wants “to turn ideas into software, quickly and faithfully.” The students who have taken NYC Data Science Academy’s “Data Science with R: Data Analytics” course or for those who already have a firm understanding of R and are looking to extend those R skills to machine learning and advanced statistical methods.The goal of this course is to bring the students to near-expert level in this field. Be sure to read the course syllabus below to ensure your level is appropriate.|
|Start Date||Rolling Dates|
|Class Size||15 students|
|Commitment||35 hours in class/wk|
|Description||This class will introduce you a wide range of machine learning tools in Python. The main focus is on the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the Python language environment. The goal is to understand how to use these tools to solve real world problems. After this course you will be able to carry out your experiments with the public available algorithms or develop your own algorithm. Syllabus This class will introduce you a wide range of machine learning tools in Python. The main focus is on the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the Python language environment. The goal is to understand how to use these tools to solve real world problems. After this course you will be able to carry out your experiments with the public available algorithms or develop your own algorithm. Specifically, students will: Be fluent with popular machine learning techniques with the scikit learn module Be aware of other available machine learning modules Explain and adopt the machine learning algorithm Week 1: Introduction What is Machine Learning Mathematics review Linear Regression, Bayesean Classifiers, K-Nearest Neighbors Numpy/Scikit-Learn lab Week 2: Regressions and Classification Multivariate linear regression Logistic Regression Linear Discriminant Analysis Supervised Learning lab Week 3: Resampling and Model Selection Cross-Validation Bootstrap Feature selection Model selection and regularization lab Week 4: Support Vector Machines and Decision Trees Support Vector Machines Decision Trees Forests Decision Tree and SVM lab Week 5: Unsupervised Learning Principal Component Analysis Clustering with K-Means State Estimation PCA and Clustering lab Intended Audience and Prerequisite Practitioners who wish to learn how to execute on predictive analytics by way of the Python language; anyone who wants “to turn ideas into software, quickly and faithfully.” The students who have taken NYC Data Science Academy’s “Data Science with Python: Data Analytics” course or for those who already have a firm understanding of Python and are looking to extend those Python skills to machine learning and advanced statistical methods.The goal of this course is to bring the students to near-expert level in this field. Be sure to read the course syllabus below to ensure your level is appropriate.|
|Subjects||Data Science, Python|
|Start Date||Rolling Dates|
|Class Size||15 students|
|Commitment||35 hours in class/wk|
|Description||This class is a 6-week evening program with hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The emphasis in this course is on learning several of the major components of Apache Hadoop – HDFS, MapReduce, Hive, Pig, Streaming – by doing exercises of increasing complexity. Programming will be done in Python. Students are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful; the material in Learn Python the Hard Way is sufficient background knowledge. The course format is mixed lecture/lab. Students will need to bring their own laptops to connect to our server; instructions will be provided ahead of time as to how to install any required software. Syllabus Week 1 – Introduction: MapReduce Overview of Big Data and the Hadoop ecosystem The concept of MapReduce HDFS – Hadoop Distributed File System MapReduce with Python streaming Week 2 – More on MapReduce More on Big Data, the Hadoop ecosystem, and MapReduce. Mixed case studies and exercises using MR with Python streaming Week 3 – Hive: A database for Big Data Hive concepts HiveQL User-defined functions in the Hive language User-defined functions in Python (using streaming) Advanced topic: Hive queries in Python code Week 4 – Pig: Simplified MapReduce Basic concepts Pig Latin Pig functions and macros User-defined functions Week 5 – Spark Intro to Spark Intro to Mahout Week 6 – Project Day The Hadoop ecosystem Brief intro to Spark Brief intro to Mahout Case studies/Final projects|
|Start Date||Rolling Dates|
|Class Size||15 students|
|Commitment||40 hours in class/wk|
I will recommend this bootcamp to anyone who is eager to learn and have great passion towards data science. Before I attended the bootcamp, I received my master degree in marketing from school. I did not have a lot of math and coding background back then. With my passion towards data science, I decided to take a deep dive and applied for the NYCDSA’s 12 weeks bootcamp. Due to my limited coding background, they did not accept me at the beginning, instead, they provided one month prep course for me to get prepared for the bootcamp. I attended the September cohort after the prep course. It was one of the best decisions I have ever made. With the extensive knowledge and training, and great support in job assistance, I am able to land my dream job in 2 months after bootcamp.
I took the DATA SCIENCE WITH PYTHON: DATA ANALYSIS AND VISUALIZATION (WEEKENDS), with Aiko Liu. Aiko is an excellent teacher, who taught methodically and progressively. The course was extremely well designed, and elevated my skilset by building my understanding step by step in a structured fashion. I would highly recommend the instructor and the class if you want to truly develop a solid foundation to build your skills on.
I graduated from a USNEWS top 10 university, was admitted by several top 5 PhD programs. During my free time, I took this institution's boot camp to boost my empirical research skills. To be honest, I was amazed by the excellence of this boot camp. The coursework is carefully designed and perfectly delivered in a highly effective fashion. The faculty here actually care about their students and are always there to help, no matter whether it's a question on the courses or on career development. Also, Vivian personally designed a significant part of the lectures, and the content's logical flow is so clear that it makes me think it's a great pleasure to read them. In addition, the program provides great help on job/intern placement. I got a hedge fund internship opportunity in two weeks bec...Read more
I attended the July cohort and was then a Data Science in Residence at the NYCDSA prior to accepting an offer for a Data Scientist role at a consulting firm based in NYC. I will do my best in this review to be as straightforward about my experience and address a lot of questions I had prior to the program and have understood a lot of incoming students to have had through prepping them for the program.
My Overall Opinion:
Lets start with this: get over your hesitation, take a leap of faith, and you surely will not regret your decision to attend the NYC Data Science Academy 12 Week Bootcamp. Whatever reservations you have, you are not alone; almost all graduates have felt that prior to attending the program. You may be wondering about if this program will really teach me the ...
I was searching for a good Data Science bootcamp program online to further my career until I found this one. Since I'm working, I can only take the online bootcamp.
The program is really high standard. What I mean is the contents are really very in-depth. They don't just teach you how to 'write code', they also teach you the algorithm behind those code. Also the exercises are no joke. It was fun learning form those videos. And a better thing of doing online bootcamp is that you can pause, search some supplement material and keep going. And you can always go back if you feel you need to review.
You will need to complete 5 projects to graduate. And those projects are actually hard and you can definitely add it into you resume.
Overall, I think it's very worthy of your time and mo...Read more
I had a Business Analytics background before joining NYCDSA, with a knowledge of programming and data science/machine learning. Though I knew how to make graphs and build models with R and Python, and knew some concepts learned from the online course on EDX and Coursera, this bootcamp was still truly helpful for me.
My goal was to explore more deeply the big data techniques including Hadoop and Spark and get a chance to review data science and machine learning stuff in a systemic way. This bootcamp gave me almost everything I desired, with so many unexpected benefits.
It was seriously life-changing for me. I achieved something that would otherwise never be possible had I just stuck with online courses. Read on for more detail.
All the courses were we...
I recommend this bootcamp to anyone who wants to enter the Data Science field. Before this bootcamp I was a process engineer for a large international steel company. With the rate of growth in the technology industry, I knew it was time to transition to a new career, and I could have not chosen a better place to do so than NYC Data Science Academy.
The Instructors were amazing. Chris is extremely knowledgable in statistics, and his passion for teaching really shines through. With every lecture, he not only shows mastery of the material but also the best way to teach complex materials to a class of non-programmers.
Luke goes above and beyond to describe the theory behind the algorithms. His work ethic is shown through the countless hours he has stayed to review lessons ...
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 instru...Read more
Attending the NYC Data Science Academy 12-week Data Science Bootcamp was one of the best decisions I have made. It was instructive and rewarding. It provided a speedy career transition and enabled me to get a job within two months of graduation as a Machine Learning Data Engineer at Capital One. I will summarize my background and describe my experience at the bootcamp and why I recommend it highly.
I have a PhD in materials science, which is a blend of math, chemistry and physics. I had programmed models and simulations in Matlab, but have no formal computer science education. I switched to management consulting after the PhD to apply my analytical skills in the business world and quickly realized there is a great need for data analysis at companies. After taking the complete Data Sc...Read more
I would recommend this program to people who are interested in starting or enhancing a career in or related to data science. The program covers a wide range of topics and the school constantly add new materials so students can learn the tools that have high industry demand. I just started working as a data scientist and engineers at my company are learning these new tools as well.
I highly recommend the chief instructor, Chris, at this bootcamp. He is a talented teacher. Those who have prior experience taking a machine learning or a statistics class would understand that it is not easy to have a good instructor. I took statistics and machine learning classes at university, but Chris surprised me with better ways of understanding statistical concepts and advanced algorithms.
This 12-week data science bootcamp is great. The faculties have created an excellent curriculum to help you get in touch with almost everything you need to be a data scientist/engineer. Sure they did not cover every relevant theory, but remember this is only a 3-month bootcamp, which is designed to give you a "jump start," not a full-time college degree.
Besides the curriculum, most of the faculties are brilliant and always willing to help. Also, more importantly, you will make friends with people from many different backgrounds, which in my opinion is even more valuable than the course itself.
Regarding the career development, Vivian and her team have made a great effort to help us to reach out to hiring managers and arrange internal referrals. However, unlike most of my fellows ...Read more
My background consists of undergraduate degrees in Math, Finance and a Masters in Financial Engineering. I found this 12-week intensive bootcamp to be extrememly valuable. Due to my academic background I was already familiar with some more technical concepts, but what NYC Data Science Academy did was to provide practical, hands on training in Python and R as they pertained to Data Analysis and Machine Learning. Following my time at NYC Data Science, I had intensive interviews for Data Science - focused roles in the finance community and I felt very well prepared. The staff is knowledgeable and routinely go above and beyond the contractual teaching time in order to ensure that each student receives the most instruction for their money.
I completed the Data Science bootcamp during the Spring of 2016. I enjoyed the Data Science bootcamp very much. It was a great experience overall.
My background is PH.D in Physics and I has more than ten years research experience as research scientist. I am probably the most senior person in the class.
The course is very intensive and comprehensive. It covers most machine learning(ML) and data science skills: ML in R and Python, website scraping, data visualization, big data with Spark and AWS. It is great to learn both R and Python there. As I know, most boot camps don’t offer R. The school teaches a lot of stuff. It can be little overwhelming at the beginning. Everything I learned there is useful and helpful in my work. Just mention a few small things: Git, Mongodb, web scraping...
I have a Ph.D. in computer science, and I worked in tech industry for nearly two years. One thing I learned was that, great companies come and go, rise and fall, shine and fade. History repeats itself. However, no one can and shall beat the trend, since nothing is immortal.
With a strong belief that machine learning is the key to the ultimate Artificial General Intelligence (AGI), I decided to embrace emerging trend and immerse myself in the field of data science, the field in which a critical piece is machine learning. In joining the NYC Data Science Academy, my motivation was simple: refresh my eyesight, build the intuitions, solidify my understanding, and meet with talented people.
It was a fantastic and rewarding journey with my fellow data scientists at the bootcamp. I will ...Read more
This course was a masterpiece. Derek Darves the instructor, quickly brought us to competency with the R programming language. Then he expanded the course by introducing the packages used for analysis and visualization, progressing through introductory use to somewhat elegant and sophisticated programming challenges. Ultimately Derek brought us to a self-sufficiency level for continuing our R education. The course was a pleasure as Derek is clearly an R expert and aficionado weaving many practical tips and historical insights into the lectures. His programming experience, statistical insights and extensions of the course materials gave it a graduate level feel, while never ignoring the fundamental skills being taught. I highly recommend it.
In October, I signed up for the 12 week bootcamp which starts in January. They recommended I take this course (free of charge) in preparation for the bootcamp to prepare myself in the language (I'm already comfortable in Python).
I'm giving this course 5 stars because, for the format, I think they did a perfect job. The instructor, Derek Darves, was definitely qualified and a nice guy in general. They gave the tools to learn the basics of data analysis, manipulation and visualization. That's all you can expect from a program like this. You have to do the learning yourself.
Great course to get started with R programming. Convenient location in the city, nice classroom mates with very different backgrounds, and an amazing instructor, Derek has an impressive deep knowledge of R and he is a very talented and dynamic teacher
Totally recommended to gain beginner understanding of this language
This was a great class that I truly enjoyed attending every Saturday for 5 weeks. The class had a pretty steep learning curve but the slides and the homeworks did a good job of teaching the material. Our instructor, Derek, was an R guru and could answer any question with aplomb. I definitely plan to continue learning R and I can attribute my enthusiasm to having taken this class. It helps to have some outside knowledge of R before taking the class, but you will still get a lot out of it if you know at least one other language (I knew SQL but not R).
Great course! The slides were clear and the content was very useful. Plenty of opportunities to practice and work in groups. Derek was a great instructor, allowing plenty of time of questions and making the course very interactive. He was also always available to answer questions in between classes and help us with work related projects as well. I have learned a lot and would definitely recommend this course.
I attended NYCDSA 5-week course (5 full-time days, one per week) in 4Q16 as part of my preparation to start the same school bootcamp. This was a great introductory start to learn R due to the comprehensive syllabus and dedicated teacher effort. About the syllabus, you will learn Base R syntax and principal data structures identification and manipulation plus a bunch of other packages (e.g. DPLYR) that will make your life easier when treating data sets. The instructor was a Sr. Data Scientist that really gave us two sides: theoretical and hands-on day-to-day professional experience views. This was very helpful. I found that there're lots of courses out there but most of the times taught by recently-graduated teachers that haven't applied a lot of the syllabus to real-life professional si...Read more
I would highly recommend the NYC Data Science Academy bootcamp. I have been an IT Executive for many years and wanted to supplant and round out my experience with skills in data science and machine learning, as it is my belief that these are one of the most critical technologies of our times. While I did try lot of the online courses, the academy brought a more organized, regimented, and immersive track that allowed me to not only learn and absorb the materials quickly, but also engage in real world projects and applications that would elegantly blend theory and practice.
The quality of both the instructors and course materials is high. It has been an amazing learning experience for me and definitely worth the investment that helped me get to my next stage of career growth. Regardles...Read more
I was an experienced academic professional with one-year experience in the oil-gas industry. After I joined the oil industry with a big oil company, the oil price started to go down till now. This caught me out of guard. As I expected, I was laid off at the end of 2015 .
By checking with other programs, I realize NYC DATA SCIENCE ACADEMY will provide the necessary tools and the big pictures about the emergent field of data science.
The instructors and TAs from the school are from diverse backgrounds including statisticians, physicists, mathematicians, and computer scientists. They make Ph.D. students feel comfortable to ask more technical and fundamental questions without feeling being discouraged. They also let you be responsible for choosing your own projects to enhance your p...
The decision to enroll in this Bootcamp was one of the best decisions I’ve ever made. I had high expectations coming in to the program, mostly due to the reviews I’ve read, yet the 12 weeks I spent at the academy exceeded those expectations. The coursework is difficult enough to challenge those with a PhD, while the instructor’s and TA’s help out to the extent that even those with a Bachelor’s will find it manageable.
The program covers a wide array of topics, combining theory and application to give you a well rounded understanding of the material. Students are encouraged to learn by doing, and home-works are given out 2-3 times a week, with deadlines not always easy to meet. But given how much the instructors offer to help, students usually always find a way.
The whole team goe...Read more
I completed the Data Science bootcamp during the Fall of 2015 and immediately was hired as a Data Scientist the following January. During the NYCDSA bootcamp, I converted my skill set from a traditional statistician/analyst to a data scientist. This bootcamp elevated my ability to apply a variety of advanced machine learning algorithms using codes like Python or R. Today, I continue to use the skills that I had developed during this bootcamp. In my opinion, this bootcamp was successful for me for three reasons:
The course content covered coding in SQL, python and R during my time at the bootcamp. As a data scientist, ability to code in this basic languages is critical to developing and testing statistical hypotheses and machine learning concepts. All machin...Read more
The 12 week bootcamp at NYC Data Science Academy combines everything a budding data scientist needs while finding a balance between depth and bredth. Coming from a background in strategy consulting I appreciated the intense nature of the schedule with tight turnarounds and explicit deadlines for coursework and project submission. You'll be given the chance not only to learn from some of the best instructors with phenomenal backgrounds and real world experience, but also to showcase your own skills and ideas through 5 data science projects where you get to form hypotheses on data sets of interest to you and make meaningful conclusions.
Though the pace is relentless, the program is very managable given the dedication of the instructors to see you succeed. You'll form great relations...Read more
The NYC Data Science Academy's 12-week Bootcamp provided an unparalleled experience - from the high quality of the instruction material to the dedicated teachers and staff, I gained both a strong personal and professional network and greater exposure to the world of data science. The return on my investment was substantial, as I've successfully received five competitive job offers in a variety of industries (1 in finance, 2 in marketing, 2 in health) within just a few months of graduating.
Most importantly, learning in the immersive environment quickly accelerated my technical know-how. I became adept at R, Python in addition to structured thinking, open-ended problem solving, and most importantly, communicating my ideas and work across to a wider audience.
Additionally, prepari...Read more
I am writing to strongly recommend NYC Data Science Academy to anyone who is looking for opportunities as a data scientist. As a recent graduate student of the 6th data science bootcamp of NYC Data science Academy, I was immediately able to find an internship in an hedge fund as a quantitative trading analyst 2 weeks after I graduate from the bootcamp and currently actively seeking full time job under the guidance of Vivian, the founder of NYC Data Science Academy. Before attending the bootcamp, I graduated from Courant Institute of Mathematical Sciences, New York University with a master of science degree in Applied Math. I have always been in love with math and attracted by the beauty of proofs and theorems. However, I did not know how to make a use of my quantitative background into ...Read more
I moved to United State in October 2015. I wanted to do a career transition and applied to NYC Data Science Academy 12-week Full-Time Data Science program after some searching over the web.
I come from a Mechatronics Engineering background, with 2+ years as a Business and Sales Consultant and an MBA in general business management. At the beginning I was a little bit afraid about the programming side because the only programming experience I had is a course in my Bachelor's about C++ and two simple projects throughout that course. Also mathematically speaking during my bachelor's I only took linear algebra and differential equations, not that much statistics and programming before entering the bootcamp.
The curriculum was mostly includes programming with SQL, R and Python and also ...Read more
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
Hi. I found your program while searching for a bootcamp for data analysis careers. Two questions.
1. I live in Virginia. Do I need to relocate to attend your program?
2. Is there any pre-requisite requirements(e.g. math/science background, etc.) to attend your program?
NYC Data Science Academy only offers an in-person bootcamp in New York City. You will need to quit your job and pay for living expenses of 3 months or longer in NYC.
I work at the first online data science bootcamp for working professionals. Check out our curriculum: www.k2datascience.com
A good applicant will have experience with programming or be able to self-study the basics for our technical assessment. Applicants should have also taken a college level statistics & probability course, and review any topics that may be unfamiliar now.
|Subject||Data Science, R, Python, Hadoop, Spark|
|Hiring %||No data|