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NYC Data Science Academy

Online, NYC
Best Bootcamp

 Ranked 2021 Best Bootcamp

About NYC Data Science Academy

Location: Online, NYC

NYC Data Science Academy is the only national accredited Data Science Bootcamp in the United States. We are also proud that we are the only bootcamp that teaches Python and R. The academy is well known for its industry project-oriented learning experience... Read More

- The only national accredited Data Science Bootcamp in the United States
The academy offers accredited data science and data analytic bootcamps in New York City and remotely online. The programs can be completed within 3 months, 4 months, and 6 months. In these programs, students learn beginner and intermediate levels of Data Science with Hadoop, Spark, Github, Docker, and SQL, as well as popular and useful Python and R packages like XgBoost, Caret, Dplyr, Ggplot2, Pandas, Scikit-learn, and more.

- Individual/ group projects showcased to hiring partners
Once the learning foundation has been set, students work on multiple projects through the Bootcamp. The program distinguishes itself by the breadth of its curriculum as well as by balancing intensive lectures with real-world project work. Students will work individually and with teams throughout the program to create at least four projects showcased to employers through multiple channels; private hiring partner events, student blogs, meetups, and film presentations.

- Lifetime Career Support
The academy also offers solid lifetime career support. There are four channels of engagement: Tech interview prep, unlimited mentorships, career services adviser who's forwarding your resume on your behalf, and a lifetime job portal. We also provide mock interviews, including challenges and behavioral questions and 1-on-1 post-interview reviews and feedback meetings from career mentors.

Courses

12-Week Data Science Bootcamp

Cost: $17,600
Duration: 12 weeks
Locations: Online, NYC
In-person Available Online
Course Description:

NYC Data Science Academy offers 12 week data science bootcamps. In these programs, students learn beginner and intermediate levels of Data Science with R, Python, Hadoop & Spark, Github, and SQL as well as the most 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. Along the way, students are assisted in preparing for employment process through resume review and interview preparation. 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.

NYC Data Science Academy works closely with hiring partners and recruiting firms to create a pipeline of interest for its students. 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.

Subjects:
Linux, Git, Python, Machine Learning, SQL, Hadoop, R Programming, Data Visualization, Data Science

Big Data with Hadoop and Spark

Cost: $2,990
Duration: 6 weeks
Locations: NYC
In-person Only
Course Description:

Overview
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.

What is Hadoop?
Hadoop is a set of open-source programs running in computer clusters that simplify the handling of large amounts of data. Originally, Hadoop consisted of a distributed file system tuned for large data sets and an implementation of the MapReduce parallelism paradigm, but has expanded in many ways. It now includes database systems, languages for parallelism, libraries for machine learning, its own job scheduler, and much more. Furthermore, MapReduce is no longer the only parallelism framework; Spark is an increasingly popular alternative. In summary, Hadoop is a very popular and rapidly growing set of cluster computing solutions, which is becoming an essential tool for data scientists.

Syllabus

Unit 1 – Introduction: Hadoop, MapReduce, Python
Overview of Big Data and the Hadoop ecosystem
The concept of MapReduce
HDFS – Hadoop Distributed File System
Python for MapReduce

Unit 2 – MapReduce
More Python for MapReduce
Implementing MapReduce with Python streaming

Unit 3 – Hive: A database for Big Data
Hive concepts, Hive query language (HiveQL)
User-defined functions in Python (using streaming)
Accessing Hive from Python

Unit 4 – Pig: A Platform for Analyzing Large Datasets Using MapReduce
Intro to Apache Pig
Data Types in Pig
Pig Latin
Compiling Pig to MapReduce

Unit 5 – Spark
Intro to Spark using PySpark
Basic Spark concepts: RDDs, transformations, actions
PairRDDs and aggregating transformations
Advanced Spark: partitions; shared variables
SparkSQL

Unit 6 – Project Week
Case studies/Final projects

Subjects:
Hadoop

Data Science with Python: Data Analysis and Visualization

Cost: $1,590
Duration: 5 weeks
Locations: NYC
In-person Only
Course Description:

Overview
This class is a comprehensive introduction to data analysis with the Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

Syllabus
Unit 1: Introduction to Python
Python is a high-level programming language. You will learn the basic syntax and data structures in Python. We demonstrate and run codes within Ipython notebook, which is a great tool providing a robust and productive environment for interactive and exploratory computing.
Introduction to Ipython notebook
Basic objects in Python
Variables and self-defining functions
Control flow
Data structures

Unit 2: Explore Deeper with Python
Python is an object-oriented programming (OOP) language. Having some basic knowledge of OOP will help you understand how Python codes work. More often than not, you will have to deal with data that is dirty and unstructured. You will learn many ways to clean your data such as applying regular expressions.
Introduction to object-oriented programming
How to deal with files
Run Python scripts
Handling and processing strings

Unit 3: Scientific Computation Tools
There are two modules for scientific computation that make Python powerful for data analysis: Numpy and Scipy. Numpy is the fundamental package for scientific computing in Python. SciPy is an expanding collection of packages addressing scientific computing.
Numpy
Scipy

Unit 4: Data Visualization
Python can also generate graphics easily using “Matplotlib” and “Seaborn”. Matplotlib is the most popular Python library for producing plots and other 2D data visualizations. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing statistical graphics.
Seaborn
Matplotlib

Unit 5: Data manipulation with Pandas
Pandas provides rich data structures and functions for working with structured data. The “DataFrame” object in Pandas is just like the “data.frame” object in R. Pandas makes data manipulation (filter, select, group, aggregate, etc.) as easy as in R.
Pandas

Final Project
After 20 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 afterwards.

Subjects:
Python, Data Visualization

Data Science with Python: Machine Learning

Cost: $1,990
Duration: 5 weeks
Locations: NYC
In-person Only
Course Description:

Overview
This 20-hour course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of 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.

Syllabus

Unit 1: Introduction and Regression
What is Machine Learning
Simple Linear Regression
Multiple Linear Regression
Numpy/Scikit-Learn Lab

Unit 2: Classification I
Logistic Regression
Discriminant Analysis
Naive Bayes
Supervised Learning Lab

Unit 3: Resampling and Model Selection
Cross-Validation
Bootstrap
Feature Selection
Model Selection and Regularization lab

Unit 4: Classification II
Support Vector Machines
Decision Trees
Bagging and Random Forests
Decision Tree and SVM Lab

Unit 5: Unsupervised Learning
Principal Component Analysis
Kmeans and Hierarchical Clustering
PCA and Clustering Lab
Final Project

After 20 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 afterwards.

Subjects:
Python, Machine Learning, Data Science

Data Science with R: Data Analysis and Visualization

Cost: $2,190
Duration: 5 weeks
Locations: NYC
In-person Only
Course Description:

Overview
This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Prerequisites
Basic knowledge about computer components
Basic knowledge about programming

Syllabus
Unit 1: Basic Programming with R
Introduction to R
What is R?
Why R?
How to learn R
RStudio, packages, and the workspace
Basic R language elements
Data object types
Local data import/export
Introducing functions and control statements
In-depth study of data objects
Functions
Functional Programming

Unit 2: Basic Data Elements
Data transformation
Reshape
Split
Combine
Character manipulation
String manipulation
Dates and timestamps
Web data capture
API data sources
Connecting to an external database

Unit 3: Manipulating Data with “dplyr”
Subset, transform, and reorder datasets
Join datasets
Groupwise operations on datasets

Unit 4: Data Graphics and Data Visualization
Core ideas of data graphics and data visualization
R graphics engines
Base
Grid
Lattice
ggplot2
Big data graphics with ggplot2

Unit 5: Advanced Visualization
Customized graphics with ggplot2
Titles
Coordinate systems
Scales
Themes
Axis labels
Legends
Other plotting cases
Violin Plots
Pie charts
Mosaic plots
Hierarchical tree diagrams
scatter plots with multidimensional data
Time-series visualizations
Maps
R and interactive visualizations
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 afterwards.

Subjects:
R Programming, Data Visualization

Data Science with R: Machine Learning

Cost: $2,990
Duration: 5 weeks
Locations: NYC
In-person Only
Course Description:

Overview
This 35-hour course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications of machine learning techniques 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 of 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.

Syllabus

Unit 1: Foundations of Statistics and Simple Linear Regression
Understand your data
Statistical inference
Introduction to machine learning
Simple linear regression
Diagnostics and transformations
The coefficient of determination

Unit 2: Multiple Linear Regression and Generalized Linear Model
Multiple linear regression
Assumptions and diagnostics
Extending model flexibility
Generalized linear models
Logistic regression
Maximum likelihood estimation
Model interpretation
Assessing model fit

Unit 3: kNN and Naive Bayes, the Curse of Dimensionality
The K-Nearest Neighbors Algorithm
The choice of K and distance measure
Conditional probability: Bayes’ Theorem
The Naive Bayes’ Algorithm
The Laplace estimator
Dimension reduction
The PCA procedure
Ridge and Lasso regression
Cross-validation

Unit 4: Tree Models and SVMs
Decision trees
Bagging
Random forests
Boosting
Variable Importance
Hyperplanes and maximal margin classifier
Sort margin and support vector classifier
Kernels and support vector machines

Unit 5: Cluster Analysis and Neural Networks
Cluster analysis
K-means clustering
Hierarchical clustering
Neural networks and perceptrons
Sigmoid neurons
Network topology and hidden features
Back propagation learning with gradient descent
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 afterwards.

Subjects:
Machine Learning, R Programming

Data Science with Tableau

Cost: $1,590
Duration: 4 weeks
Locations: NYC
In-person Only
Course Description:

This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small.

Subjects:
Data Visualization

Deep Learning

Cost: $2,990
Duration: 5 weeks
Locations: NYC
In-person Only
Course Description:

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.

Subjects:
Python

Introductory Python

Cost: $1,590
Duration: 4 weeks
Locations: NYC
In-person Only
Course Description:

Overview
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.
Goals
This is a “short course” of 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. Due to the focused nature of this course, there will be no individual class projects but the instructors will be available to help students who are applying Python to their own work outside of class.
Syllabus

Unit 1: List manipulation
Simple values and expressions
Defining functions, using ordinary syntax and lambda syntax
Lists
Built-in functions and subscripting
Nested lists
Functional operators: map and filter
List comprehensions
Multiple-list operations: map and zip
Functional operators: reduce

Unit 2: Strings and simple I/O
Characters
Strings as lists of characters
Built-in string operations
Input files as lists of strings
Print statement
Reading data from the web
Using the requests package
String-based web scraping (e.g. handling csv files)

Unit 3: Control structures
Statements vs. expressions
For loops
Variables in for loops
if statements
Simple and nested if statements
Conditional expressions in lambda functions
While loops
break and continue

Unit 4: Data Analysis Packages
NumPy
Ndarray
Subscripting and slicing
Operations
Pandas
Data Structure
Data Manipulation
Grouping and Aggregation

Subjects:
Python

NYC Data Science Academy Reviews

Average Ratings (All Programs)

NYC Data Science Academy logo

4.88/5 (365 reviews)

Justin Ng
Graduated: 2019

12/22/2019

Course
Deep Learning

Overall

Curriculum

Job Support

"A Rare Experience"

I took Jon Krohn’s deep learning course in the fall/winter of 2019.

Jon is that rare communicator with the aptitude to succinctly explain rigorous and complex topics in a digestible fashion. His course is an ambitious undertaking, but Jon makes the subject... Read More

Xavier Granda
Graduated: 2019

12/17/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"Best decision I've taken"

The reason I picked this Bootcamp over the other's was that after talking to the admission office they seem to be really serious about what they were teaching and the people they admit to their program. In my case, I was looking for a challenging environment... Read More

The academy instructors are very well prepared and passionate about what they do. The only downside is that given only 12 weeks is hard to understand every single thing they are teaching, for example, one day you are learning Random Forest and the next day you are learning something different. But something that they planned really well and is really helpful is that since you are learning Machine Learning in R and Python when switching to the other language the theory remains the same only the code changed, so in a way, you are reviewing theory twice.

The whole experience was incredible, the people that attended my cohort were very smart people coming from different backgrounds like finance, physics, business, engineering, to name a few and from top universities like Harvard, Oxford, Cornell and also different countries which made the whole experience much more interesting. What I enjoyed the most was that working with these different people made you look at data from very different perspectives and in result have a richer analysis of a simple dataset.

After the Bootcamp feel ready to start looking for a data science job, I was already in a quantitive field but this program has really helped me gain that confidence I needed to have a career change.

Aaron Festinger
Graduated: 2019

12/16/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"A Great Boot Camp Experience"

NYC Data Science Academy was my first choice because of my interest in data science, and their reputation as the best data science program. They did not disappoint.

As a recently separated veteran with a master's degree in physics, I was looking for a... Read More

NYCDSA is exactly as advertised: an intensive program on data science methodology aimed at students from a wide variety of backgrounds. With my background in physics, math, and C++ and Java coding, I felt that the Python and R syntax taught at NYCDSA was not too difficult. Other students were less prepared but often did okay anyhow. The course began with Python and R syntax for handling and visualizing data, and then continued with machine learning methods in both languages as well. SQL and Docker were also covered, but rather briefly. At the end of the course, deep learning and Tensorflow were introduced, as well as Spark and Hadoop. I would have preferred that more time be dedicated to those topics, but this is a three-month boot camp, so time is limited. Nevertheless, given the allotted time, I feel that I have achieved an incredible amount, and I'm very happy with my experience. Boot camp is not cheap in either time or money, but this one was well worth both. I intend to continue building on the knowledge, experience, portfolio, and network that I have accumulated at NYCDSA as a professional data scientist.

Dylan Dempsey
Graduated: 2019

12/15/2019

Course
Data Science with Python: Machine Learning

Overall

Curriculum

Job Support

"Excellent weekend class for ML in python."

Very solid class with an excellent professor Ryan Courtney. We covered all the bases and the professor was very careful to make sure that everyone was being brought along with the course material but still went out of his way to challenge us. Classic... Read More

Michael Dollar
Graduated: 2019

12/11/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"NYCDSA was a fun, intense, and effective restructuring of skills in order to start a new career."

I needed the shortest path to pivot from a research position in physics to a career in data science. I considered grad school, but I have a family, and I need to be there for my kids during their developmental years. NYC Data Science Academy offered an... Read More

Youngmin Cho
Graduated: 2019

11/18/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"Amazing Experience. Highly Recommend."

I went to this bootcamp on the summer of 2019 and it was an amazing experience!

The lecturers and TA's are knowledgeable and caring to teach what is required to become a data scientist. The program is very well-managed and admissions genuinely cares about... Read More

Curriculum is also outstanding for a short 12 week of time.
You will have opportunity to learn both R and Python.
(Of course, D3 / Django and more python may sound appealing comparing to other bootcamps. Trust me, learning R is very important. You will be able to review a lot of Math/Stats in DS and R is still heavily used in the industry)

You will get from how much you put in. Work hard and you will learn a lot to become successful in this field. Also this is a good environment to build outstanding network with intelligent individuals.

This is a good place to start. You can go a lot of different directions with the knowledge you gain here.

Highly recommend!

Chung-Hsuan Huang
Graduated: 2019

11/16/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"Excellent experience! Highly recommend!"

I did some research on data science online (Course report and switchup) before I made a decision. I chose NYCDSA because they covered both Python and R, their instructors are knowledgeable and professional, and comprehensive curriculum. Especially I have... Read More

NYC Data Science Academy did a great job of helping me acquire the necessary skills in Data Science before, during, and after the bootcamp. The pre-bootcamp materials helped me get familiar with the essential R/Python coding skills and speeded up the learning curve during the bootcamp. I highly recommend whoever wants to join a bootcamp should get prepare before the bootcamp, and NYCDSA does a great job in providing necessary materials in R/Python coding.

During the bootcamp, the course work is very intensive. Although I already have coding experience and mathematical background, I still need to spend at least 12 hrs a day to study and finish homework and projects. However, the instructors and TAs are doing their best to ensure everyone can get the most out of the courses. As a remote student, I can't meet them in person, and they are still very responsive through messages or video meetings. During the bootcamp, I finished four projects to showcase my skills. One of them is solving the real business problem from the company, which is valuable when I was looking for a job. The academy also teaches you how to express your projects during the interview.

They have a robust professional network and hold a hiring event one week after each cohort. You will have the opportunity to expand your network and show your skills to various companies that are looking for data scientists or data analysts. They also dedicated to supporting you find a great job. Vivian did a fantastic job helping me with my interview processes and giving me valuable feedback.

Overall, I have a very positive experience with NYCDSA. I was suspicious if a bootcamp can make me transit into a data scientist when I looked for a data scientist bootcamp. However, I found a great job a month after I finished the bootcamp. This program is worth the hard work I put. I am glad that I made this decision a half year ago.

zhuoyi liu
Graduated: 2019

11/2/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"Amazing bootcamp"

I graduated in end of June(Cohort 17). Right now I'm a Data engineer at top tech firm in China.
The bootcamp materies encompass big data, machine learning, and data analysis tool such as Python, R, SQL.
This include all the to-go package to be a Data... Read More

Hong Yang Wang
Graduated: 2019

10/24/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"Amazing Experience"

I graduated in end of March(Cohort 16).
Before having the bootcamp, I have no coding or computer science related background. I thought I would fail and not able to get into the industry.
However, after I took the bootcamp, I learned a lot of coding and... Read More

Yuce Dincer
Graduated: 2019

10/24/2019

Course
12-Week Data Science Bootcamp

Overall

Curriculum

Job Support

"Superb Experience"

After spending 9 years in business sales, I wanted to go back to my technical roots and started looking into data science bootcamps that could help me make a career transition. I looked into all the major schools within the data science bootcamps and... Read More

One thing that I was very satisfied about at the bootcamp was the hands on help that I got whenever I needed it. The entire school staff wants you to succeed and achieve your goals and they do everything they can do make that happen. Can’t thank them enough for having my back in this journey.

As far as job hunting, the academy provides ample material on how to get ready for the job search process from resume reviews to interview prep to bringing employers in the school for interviews. There are always exclusive job postings on the internal website and the school has an incredible network, so it’s always been easy getting introductions to school alumni or key executives from target companies. I ended up receiving 2 offers, and joined a major tech company as a data scientist.

NYCDSA is highly recommended! It is worth the time and the money in gold!

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