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NYC Data Science Academy

Courses: 12-Week Data Science Bootcamp, Big Data with Hadoop and Spark, Data Science with Python: Data Analysis and Visualization, Data Science with Python: Machine Learning, Data Science with R: Data Analysis and Visualization, Data Science with R: Machine Learning, Data Science with Tableau, +2 more.
Deep Learning, Introductory Python

Locations

Online, NYC

About NYC Data Science Academy

Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, and corporate... Read More

The program delivers a combination of lectures and real-world data challenges to its students and is designed specifically around the skills employers are seeking, including R, Python, Hadoop, Spark and much more. By the end of the program, students complete at least four real-world data science projects to showcase their knowledge to prospective employers. Students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations. 93% of NYC Data Science Academy students are hired within six months of graduation. For more information visit http://nycdatascience.com.

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Courses

12-Week Data Science Bootcamp

  • Cost: $17,600
  • Duration: 12 weeks
Locations: Online, NYC
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
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
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
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
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
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
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
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
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)

Overall
Curriculum
Job Support

4.89/5

(311 reviews)
    10/3/2020
  • Swarup Malli
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Fantastic instructors and thorough curriculum"

I'm a software engineer with work experience in Business Intelligence. I always felt data science was a logical extension of business intelligence. I had been planning to transition into the data science space by joining an immersive data science Bootcamp.... Read More

My experience has been a positive one. I was able to learn a lot of data science-related subjects in a short span of 3 months.
Thanks to the instructors, who were always there for the students. They really cared about the students and are well versed in the subjects they teach. The career services are very efficient, leveraging a large community of alumni.
I would recommend this Bootcamp to professionals who are looking to break into the field of data science.

Comment
    10/2/2020
  • Simon D Yates
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Excellent Teaching Staff and Thorough Curriculum"

I attended the 12-week Data Science Bootcamp in May 2020 and gained a great deal from the experience. I came in with a background in mathematics, but with limited experience programming in Python. By the end of the bootcamp I was very comfortable using... Read More

What I thought was exceptional about the course was the quality of the instructors. These ranged from mathematicians with a distinguished track record in academia, to hands-on software engineers with experience working with complex systems. In all cases the instructors were encouraging and generous with their time.

The bootcamp has an extensive pre-work section and I'd advise any attendees to complete this thoroughly. 12 weeks is a short time to cover the amount of material that's on the curriculum, so building basic coding skills before the start will be very beneficial.

All-in-all I felt the bootcamp delivered exactly the skills that I was looking to build. I'd recommend it highly to anybody who wants a serious foundation in the subject.

Comment
    10/1/2020
  • Elina Egiazarova
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Outstanding Data Science Training"

I attended the 12-week data science program at NYC Data Science Academy and it was a fantastic experience. It was meant to be in-person but the classes were moved online due to pandemic. The transition was seamless even though we were the first on-site... Read More

You will learn Python, R, SQL, different machine learning algorithms as well as how to work with data. But that's not all: what I particularly liked was how strongly they emphasised the importance of presentation skills and the ability to tell a story behind your numbers.

Their devotion to making sure that they prepare you well for a career in data science is quite remarkable. Their career services are outstanding - you will have regular meetings to discuss your resume, prepare for interviews, network with alumni.

My background is in applied mathematics and quantitative finance but the curriculum is so intensive that I had to work 12 hours a day to meet all the deadlines. But - and that's what makes NYCDSA truly first-class - it will never feel like hard work because you will be surrounded by people who are not just very good at what they do: they are passionate about data science and their enthusiasm will inevitably translate into you enjoying it, too.

So if you are interested in a career in data science - NYCDSA is an excellent place to start. But even if you are considering data science but not sure whether you'll like it or not - try to get into NYC Data Science Academy: not only will you learn a lot but you'll actually love data science and will want to make it your life's work.

Comment
    9/25/2020
  • Robert K.N. Atuahene | Data Scientist
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Oustanding intro to data science and machine learning"

Without question, this is an outstanding Bootcamp for those looking to break into the field of data science. The curriculum is structured to help students master the essential vocabulary in Python (and R) needed to gather, clean, analyze and visualize... Read More

Comment
    7/14/2020
  • William Ponsonby | Business Analyst
  • 12-Week Data Science Bootcamp
  • Graduated: 2019

Overall Score

Curriculum

Job Support

"Fantastic experience!"

NYCDSA is a fantastic bootcamp. I picked them on the recommendation of a former student and it didn't disappoint! It was a seriously tough 3 months, I had never coded before I started the course pre-work having just finished a humanties degree and they... Read More

You get out what you put in to this course. I was often pretty exhausted and increasingly had no weekends! It was no picnic but definitely worth it as I now have a job at a consultancy specialising in data.

I think the best aspects of the course were that you could ask anyone for help, both students and teachers, plus the nature of the project work where you were also judged on your presentational skills.

Comment
    7/11/2020
  • Sammy Dolgin
  • 12-Week Data Science Bootcamp
  • Graduated: 2019

Overall Score

Curriculum

Job Support

"Supportive and Dedicated Community"

Very fast-paced, intense experience that gave a vigorous run-through of the Computer Science, Mathematical, Statistical, and Business-oriented skills necessary to break into Data Science. You won't graduate an expert, but you'll at have at least a baseline... Read More

Comment
    7/8/2020
  • Kailun Cheng
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Project-based Learning is a Must for Data Science"

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NYC Data Science Academy
Avg Rating:4.84 ( 298 reviews )
About
Courses
Reviews
News
About
NYC Data Science Academy offers 12-week data science bootcamps in New York City. In these... Read More

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.

RECENT NYC DATA SCIENCE ACADEMY REVIEWS: RATING 4.84
Project-based Learning is a Must for Data Science

Rewarding and Advantageous

12-Week Data Science Bootcamp

all (298) reviews for NYC Data Science Academy →
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Courses
12-Weeks In-Person Data Science Bootcamp
MySQL, Data Science, Git, R, Data Visualization, Hadoop, Spark, Linux, Data Analytics , SQL, Python, Machine Learning
In PersonFull Time420 Hours/week12 Weeks
Start Date None scheduled
Cost $17,600
Class size 50
Location New 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
Getting in
Big Data with Amazon Cloud, Hadoop/Spark and Docker
Data Science, Hadoop, Spark, Data Structures, Python, Cloud Computing
In PersonPart Time5 Hours/week2 Weeks
Start Date None scheduled
Cost $2,990
Class size 10
Location New 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
Getting in
Data Science with Python: Data Analysis and Visualization (Weekend Course)
Data Science, Data Visualization, Data Analytics , Data Structures, Algorithms, Python
In PersonPart Time4 Hours/week6 Weeks
Start Date None scheduled
Cost $1,590
Class size 20
Location New 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
Getting in
Data Science with Python: Machine Learning (Weekend Course)
Data Science, R, Artificial Intelligence, Machine Learning
In PersonPart Time7 Hours/week5 Weeks
Start Date None scheduled
Cost $1,990
Class size 10
Location New 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
Getting in
Data Science with R: Data Analysis and Visualization (Weekend Course)
Data Science, R, Data Visualization, Data Analytics , Data Structures
In PersonPart Time7 Hours/week6 Weeks
Start Date None scheduled
Cost $2,190
Class size 15
Location New 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
Getting in
Data Science with R: Machine Learning (Weekend Course)
Data Science, R, Machine Learning
In PersonPart Time7 Hours/week6 Weeks
Start Date None scheduled
Cost $2,990
Class size 40
Location New 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
Getting in
Deep Learning with Tensorflow (Weekends and In-Person Only)
Data Science, Game Development, Artificial Intelligence, Python, Machine Learning
In PersonPart Time6 Hours/week6 Weeks
Start Date None scheduled
Cost $2,990
Class size 15
Location New 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
Getting in
Full-time Online Data Science Bootcamp
MySQL, Data Science, Git, R, Data Visualization, Hadoop, Spark, Linux, Data Analytics , SQL, Python, Machine Learning
OnlineFull Time28 Hours/week12 Weeks
Start Date None scheduled
Cost $17,600
Class size 25
Location Online
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
Getting in
Introductory Python (Evenings)
MySQL, Data Science, Data Visualization, Data Analytics , Data Structures, Algorithms, Python
In PersonPart Time5 Hours/week2 Weeks
Start Date None scheduled
Cost $1,590
Class size 40
Location New 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
Getting in
Part-time Online Data Science Bootcamp
Data Science, R, Data Visualization, Spark, Virtualization, Data Analytics , Data Structures, Artificial Intelligence, SQL, Python, Machine Learning
OnlinePart Time28 Hours/week26 Weeks
Start Date None scheduled
Cost $17,600
Class size 25
Location Online
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.
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NYC Data Science Academy Reviews
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Project-based Learning is a Must for Data Science - 7/8/2020
Kailun Cheng User PhotoKailun Cheng • Graduate • Course: 12-Weeks In-Person Data Science Bootcamp • Campus: New York City • Verified via LinkedIn
Overall Experience:Curriculum:
Instructors:Job Assistance:
I have a biomedical engineering background, and I enjoy solving complex problems using quantitative methods. I had decided to venture into the field of data science and machine learning because I realized that there are many processes that are insufficiently modeled in a deterministic way. When I graduated with a Master's degree in Data Science, I had acquired many tools and methods in disciplines such as computer science and applied math; however, I was lacking some experience actually "doing" data science, especially in working with real-life data. NYCDSA gave me the opportunity to obtain hands-on experience in the model building process. I also had a great time collaborating with other fellows, instructors, and mentors on challenging projects, which further expanded my skill set in proper data collection, data retrieval, and data visualization. Overall, NYCDSA fosters an enriching and inclusive environment for students of all different backgrounds to gain valuable experience that's needed for a data-driven career.

Regarding the job prospects post Bootcamp, I find it challenging in finding a suitable position in this ever-changing career field coupled with the tough economic situation. However, I do think that NYCDSA is a great guide in pointing me in the right direction by providing the appropriate resources. After graduation, a couple of recruiters reached out to me, and I was able to pass rounds of interviews with the knowledge I gathered from the projects I did in the boot camp. Ultimately, I'm able to receive a full-time data scientist position offer about three months post-graduation.

Comment
    7/2/2020
  • David Libin
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"A bad choice overall"

This boot-camp was a waste of time and money. They gave me poor information and wasted my time. I am quiet angry with them for this. Don't register with them. Also there representatives are the ones which promise things they cannot finish. In a sense... Read More

Comment
Official Response from NYC Data Science Academy

Hi David, as we discussed on the phone last week, you were not enrolled in our Data Science Bootcamp. In fact you were enrolled in one of our professional development courses called "Introductory Python" (20 hours in length). After speaking with you,... Read More

    6/30/2020
  • Dmitri Levonian
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Great intensive bootcamp"

NYCDSA bootcamp curriculum is intensive, deep, and diversified. It covers both the math behind the data science and the practical platforms/languages/tools (Python, R, SQL) most widely used in the industry. Their career services are focused and efficient,... Read More

Comment
    6/29/2020
  • jae ko
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Rewarding & Advanteous"

I recently graduated from NYC Data Science Academy in March of 2020. Before I began my cohort, my background was in finance and was a novice in the data science/coding world. Though challenging and difficult at times the staff, instructors, and the TA's... Read More

The curriculum not only covers coding and data science but the individual & group projects, presentations, and the helpful feedback is very advantageous in the real world. Also, I really appreciated how NYCDCA went what of their way in little things that can make a big difference such as: head shots, resume reviews, interview preps, strategy in job hunting etc...They did an excellent job of covering all topics and encompassing everything that comes with either career change or building up your current career.

From my experience, I only have positive things to say about the staff, instructors, curriculum, job support, and the community at NYC Data Science Academy and highly recommend NYCDSA to anyone who is interested in data science.

Comment

NYC Data Science Academy's average rating is 4.89 out of 5.0 based on 311 review(s).

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