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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

(330 reviews)
    7/22/2021
  • Liam McDermott | Data Scientist
  • 12-Week Data Science Bootcamp
  • Graduated: 2021

Overall Score

Curriculum

Job Support

"Fantastic!"

I am an unlikely Data Scientist as my background is in derivative trading and data platform sales. For many years, I did not consider myself technical at all. As time progressed, it became clear to me that the world was passing me by and I decided to... Read More

Comment
    7/6/2021
  • Casey Hoffman | Associate Data & Analytic Modeler
  • 12-Week Data Science Bootcamp
  • Graduated: 2021

Overall Score

Curriculum

Job Support

"Absolutely worth it!"

I graduated from the 12-week remote live boot camp in April 2021. I can't speak highly enough of this program. A bit about my background: BA&MA in social science (psychology), I had a lot of stats knowledge but very minimal programming experience. I had... Read More

Comment
    5/31/2021
  • Steven Lantigua
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Worth It"

I graduated from the 12-week remote live program in December 2020 and I highly recommend it. The program is intensive but well worth the challenge. Prior to NYCDSA, I had just finished my undergrad and was looking to break into the data science field.... Read More

Comment
    5/26/2021
  • Patrice Kontchou | Data Scientist
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Excellent Program"

I remembered a year ago going from websites to websites, reading reviews after reviews, looking for the best school for Data Science Bootcamp. I finally stopped at NYC Data Science Academy and one of the main reasons I decided to go with NYC was because... Read More

Comment
    5/10/2021
  • Richa vala | Solution Analyst
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Great Instructors, Excellent Curriculum"

I wanted to make a successful pivot to a data science career. I began my journey by doing some self-learning through many online resources. Finally, to give structure to my learning, I enrolled in the Fall session of 2020 at NYC Data Science Academy.... Read More

Comment
    5/4/2021
  • Lucas K
  • 12-Week Data Science Bootcamp
  • Graduated: 2021

Overall Score

Curriculum

Job Support

"Intense immersive bootcamp that prepares students"

Before joining the Bootcamp program, I worked in finance as a Risk Analyst and had little experience with Python and SQL. NYCDSA prepared me to polish my programming skills in Python, SQL and R and to developing Data Science skills fundamentals that made... Read More

Comment
    4/29/2021
  • Martin Kihn | SVP
  • 12-Week Data Science Bootcamp
  • Graduated: 2021

Overall Score

Curriculum

Job Support

"Great overview of a wide range of topics at manageable pace for someone working full-time"

I took the online part-time bootcamp because I was working full-time as a software product manager, yet wanted to improve my coding and statistics knowledge. I had some background in Python but had a lot of gaps in my knowledge of theory and no exposure... Read More

Comment
    4/18/2021
  • Shameer Sukha | xVA Management Group, CIBC
  • 12-Week Data Science Bootcamp
  • Graduated: 2021

Overall Score

Curriculum

Job Support

"Elite program for an elite experience"

After 18 years of specialist experience, I was starting to develop a FOMO with Data Science. After some research and recommendation from a friend who completed this program I decided to enroll. No doubt, this will change my life. It was liberating and... Read More

Comment
    4/17/2021
  • Sita Thomas | Data Analytics Manager
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"A solid balance of industry expertise and genuine drive to help students succeed"

I did the Interactive Distance Learning (IDL) program in 2020, so keep in mind that my experience was strongly influenced by the COVID-19 pandemic. I am a career switcher from the health services industry - this bootcamp got me into the tech industry... Read More

Comment
    4/14/2021
  • Xuyuan Zhang
  • 12-Week Data Science Bootcamp
  • Graduated: 2020

Overall Score

Curriculum

Job Support

"Excellent and challenging experience"

Before joining the program, I was a student majoring in Business Analytics. I had a hard time finding a data analyst position and therefore I was thinking maybe I was not qualified for a data-related job yet. My friend recommended this Bootcamp to me... Read More

Comment

NYC Data Science Academy's average rating is 4.89 out of 5.0 based on 330 review(s).

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