NYC Data Science Academy Review: an Industry-Embraced Program

July 30, 2021
Reviews
" alt="" />

Make Your Dollar Go Further

Earn up to 50,000 MR points (valued at $1,000 - $1,500) with the American Express Cobalt Card, a low-fee card that offers 5x the points on all food and drinks.

Bonus: $100 USD hotel credit

NYC Data Science Academy is a tech school that offers a full and part-time program that can be taken either in-person or online. The school is located in New York City that grants the opportunity to learn and accelerate your career in data science. With this purpose, NYC Data Science Academy offers seven courses divided into four types:

  • Introductory:
    • Introductory Python.
  • Beginner:
    • Data Science with Python: Data Analysis and Visualization.
    • Data Science with R: Data Analysis and Visualization.
    • Big Data with Amazon Cloud, Hadoop/Spark and Docker.
  • Intermediate:
    • Data Science with Python: Machine Learning (Weekend Course).
  • Bootcamp:
    • Data Science Bootcamp.
    • Data Analytics Bootcamp.

The school also offered three other courses that touched on Tableau, machine learning with R, and deep learning with Tensorflow that are currently unavailable but might come back at some point:

The school doesn't offer scholarships, but they do have two payment plans through a partnership with Climb and Ascent, plus early-bird discounts.

If you want to know more about NYC Data Science Academy, you can click on the button below:

NYC Data Science Academy is a tech school located in New York City. It offers seven programs that cover data science and data analytics. NYC Data Science Academy provides accelerated data science training programs and courses that prepare people for employment opportunities across all industries as data science professionals.

NYC Data Science Academy prepares its students to use data science tools and apply them to real-world situations. Since its founding in late 2013, the Academy has helped several thousand people become data science professionals and developed their career in a fast-growing career field.

Who Is NYC Data Science Academy For?

NYC Data Science Academy helps people who want to start coding and also those with tech backgrounds learn and update their skills in data science in New York City.  Students don’t need to have any experience to join some of the more basic programs. To enroll in any of the school's bootcamps, however, you must have a tech background, and NYC Data Science Academy recommends that applicants have a Ph.D.

Programs can be full or part-time commitments and have access to an online program.

Features & Benefits of NYC Data Science Academy

Here are a few of the most important features and benefits of NYC Data Science Academy:

Well-Aligned Incentives

NYC Data Science Academy offers some pretty great payment plans that seek to make your life a little bit easier while you study and even after you graduate. It aligns its goals with its students by making sure you don’t have to pay some or all of your tuition until you graduate. The school does this through partnerships with both Ascent and Climb.

No Experience Needed

Having the possibility to start learning a new skill from scratch at the hands of experienced instructors from day one is something that most people will appreciate. Many courses offer great value, but only if you already have a working knowledge of the basics. This can be particularly frustrating for someone who is trying to change careers but always hits a wall that won’t let them enroll due to lack of experience.

While you may take the proactive route and start researching all the information by yourself, this can be a bit daunting at times, especially if you don’t even know where to start. That’s when a course like this one, which lets you sign up with no previous experience at all, comes in handy. While you’re likely going to have to check some of the basics out yourself, you’ll have both the support and advice from your instructors, who are the best prepared to guide you along the way and to keep you on track.

In-Person Instruction

NYC Data Science Academy's in-person instruction creates an environment where students can concentrate better because there will be fewer distractions. This will grant them a better understanding and a higher chance of completing the course since they will also interact with instructors and peers.

A classroom also grants the opportunity to access more information since the student will be in the same place as their instructor and peers, making it easier to make themselves heard. This also makes it easier to make friends, problem-solve and build a network with people of different backgrounds.

Online Instruction

NYC Data Science Academy's online instruction allows students to learn from the comfort of their homes or anywhere they would like to study. It also provides students who live far away the chance to enroll without having to relocate, making the course available for a wider base of students. This type of learning also suits students with different learning methods, letting them learn more flexibly.

The online courses that NYC Data Science Academy offers can fit around students' lives and activities, allowing them to be more relaxed than in a classroom environment. This type of option will allow working people to pursue a new career path without taking the risk of leaving their old jobs until they're ready to land a new role in software development.

Personalized Mentorship

All courses have an instructor in charge of giving each student hands-on help whenever needed. They are in charge of explaining concepts and helping students with any problems they might run into during the week. Their job at NYC Data Science Academy is to make sure that students are always able to complete all class assignments successfully.

The school also provides one-on-one mentorship that helps students with any roadblocks they encounter and offers them post mock interview feedback sessions.

Project-based Curriculum

A project-based curriculum is a feature that not all courses offer. What this means is that NYC Data Science Academy’s curriculum is built around real projects that you’ll be working on all throughout the length of the course. Not only does that imply that you’ll learn by doing.

For someone who just started out in the world of coding, having a set of skills to show that goes along with the newly crafted resume can prove invaluable when it comes to finding a job in the tech industry or a related field.

NYC Data Science Academy's Course List

Students who enroll in some of NYC Data Science Academy's programs get around 400 hours worth of training in data science and data analytics. The school offers instruction from experienced industry professionals. 

NYC Data Science Academy offers 7 different programs divided into four categories:

  • Introductory:
    • Introductory Python.
  • Beginner:
    • Data Science with Python: Data Analysis and Visualization.
    • Data Science with R: Data Analysis and Visualization.
    • Big Data with Amazon Cloud, Hadoop/Spark and Docker.
  • Intermediate:
    • Data Science with Python: Machine Learning (Weekend Course).
  • Bootcamp:
    • Data Science Bootcamp.
    • Data Analytics Bootcamp.

In the following table, you'll find an overview of the basic features and cost of these 7 courses.

CourseCourse TypeCourse LengthTuition
Introductory PythonPart-Time
In-Person
4 Weeks
(4 hours/week)
$1,590
Data Science with Python: Data Analysis and VisualizationPart-Time
In-Person
5 Weeks
(4 hours/week)
$1,590
Data Science with R: Data Analysis and VisualizationPart-Time
In-Person & Online
5 Weeks
(7 hours/week)
$2,190
Big Data with Amazon Cloud, Hadoop/Spark and DockerPart-Time
In-Person
6 Weeks
(5 hours/week)
$2,990
Data Science with Python: Machine LearningPart-Time
In-Person & Online
5 Weeks
(4 hours/week)
$1,990
Data Science BootcampFull and Part-Time
In-Person & Online
4-6 Months Online, Full/Part-Time
(Live: 40+ hours/week)
$17,600
Data Analytics BootcampPart-Time
Online
3 Months $9,995

Here’s a breakdown of all of these programs:

Introductory Python

LocationIn-Person – New York City
Class Size40
Start DatesAug 2nd – Sep 1st, 2021
Time CommitmentPart-Time
4 hours per week.
Course Length4 Weeks
Cost of Tuition$1,590

This introduction to Python class is designed for people who know how to work with a computer, with no programming background, but who want to learn basic Python programming. The course is targeted at those who want to learn “data wrangling” – manipulating downloaded files and making them amenable to analysis. NYC Data Science Academy concentrates on the language's basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web.

Skills You Will Learn

The Introductory Python course focuses on the following skills:

  • MySQL, a fully managed database service used to deploy cloud-native applications.
  • Data Science, an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data.
  • Data Visualization, interdisciplinary field that deals with the graphic representation of data and is a particularly efficient way of communicating when the data is numerous as for example, a Time Series.
  • Data Analytics, also known as Data Analysis, is the process of cleaning, inspecting, transforming, and modelling data to find useful information, notifying conclusions, and advising for decision-making.
  • Data Structures, a data organization, management, and storage format that enables efficient access and modification.
  • Algorithms, a finite sequence of defined, computer-implementable instructions designed to solve problems or to perform a computation.
  • Loop Testing, also known as Loops, is a type of software testing type that is performed to validate the control structures.
  • NumPy, a library for Python that supports creating large multidimensional arrays and vectors, along with a large collection of high-level mathematical functions to operate on them.
  • Pandas, a fast, powerful, flexible and easy to use open-source data analysis and manipulation tool built on top of Python

Coding Languages You Will Learn

The core coding languages you will learn in the Introductory Python course are:

  • Python, a free software environment and programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing.

Modules You Will Go Through

This course is divided into 4 units. Here is a breakdown of the topics that will be covered:

List manipulationSimple values and expressions. Defining functions using ordinary syntax and lambda syntax. Lists: Built-in functions and subscription, Nested lists. Functional operators: map and filter. List Comprehensions. Multiple-list operations: map and zip. Functional operators: reduce.
Strings and simple I/OCharacters. 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).
Control structuresStatements 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.
Data Analysis PackagesNumPy. Ndarray. Subscripting and slicing, Operations: Pandas, Data Structure, Data Manipulation, Grouping and Aggregation.

Data Science with Python: Data Analysis and Visualization

LocationIn-Person
New York City, Online
Class Size20
Start DatesAug 1st – Aug 29th, 2021
Time CommitmentPart-Time
4 hours per week.
Course Length5 Weeks
Cost of Tuition$1,510.50 – $1,590

This class is a comprehensive introduction to data science with Python. It's targeted at people who have some basic programming knowledge and want to take it to the next level. The program introduces how to work with different data structures in Python and covers data analytics and visualization modules most popular tools, including numpy, scipy, pandas, matplotlib, and seaborn. The school uses Ipython notebook to demonstrate the results and change codes interactively.

Skills You Will Learn

The Data Science with Python: Data Analysis and Visualization course focuses on the following skills:

  • Data Science, method of extracting insights from data.
  • Data Visualization, a field that deals with the graphic representation of data.
  • Data Analysis helps individuals and organizations make sense of data.
  • Data Structures, organization format that enables efficient access and modification of data.
  • Algorithms, a sequence of instructions to solve problems.
  • NumPy, a library for Python.
  • SciPy, a Python-based ecosystem of open-source software for mathematics, science, and engineering.
  • Seaborn, a Python data visualization library based on matplotlib that provides a high-level interface for drawing informative statistical graphics.
  • Matplotlib, a comprehensive library for creating static, animated, and interactive visualizations in Python.
  • Pandas, an open-source data analysis and manipulation tool.

Coding Languages You Will Learn

The core coding languages you will learn in the Data Science with Python: Data Analysis and Visualization course are:

  • Python, a free software environment and programming language.

Modules You Will Go Through

This course is divided into 5 units. Here is a breakdown of the topics that will be covered:

Introduction to PythonPython is a high-level programming language. You will learn the basic syntax and data structures in Python. We demonstrate and run codes within the 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
Explore Deeper with PythonPython 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.
Scientific Computation ToolThere 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.
Data VisualizationPython 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.
Data manipulation with PandasPandas provide 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.

Data Science with R: Data Analysis and Visualization

LocationNew York City, Online
Class Size15
Start DatesJul 31st – Aug 28th, 2021
Time CommitmentPart-Time
7 hours per week.
Course Length5 Weeks
Cost of Tuition$2,190

This is a 35-hour program designed to provide an introduction to R. Students will learn how to load, save, and transform data as and also how to write functions, generate graphs, and fit basic statistical models with data. In addition to the theoretical framework in which students learn the process of data analysis, this class focuses on practical tools needed in data analysis and visualization. By the end of it, students will have mastered the essential skills of processing, manipulating and analyzing data of various types, allowing them to create advanced visualizations, generate reports, and document their codes.

Skills You Will Learn

The Data Science with R: Data Analysis and Visualization course focuses on the following skills:

  • Data Science, method of extracting insights from data.
  • Application Programming Interface, also known as API, is an interface that defines interactions between multiple software applications or mixed hardware-software intermediaries.
  • Data Analysis, helps individuals and organizations make sense of data.
  • Data Structures, organization format that enables efficient access and modification of data.

Coding Languages You Will Learn

The core coding languages you will learn in the Data Science with R: Data Analysis and Visualization course are:

  • R, a free software environment and programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing.
  • Ggplot2, an open-source data visualization package for the statistical programming language R.

Modules You Will Go Through

This course is divided into 5 units. Here is a breakdown of the topics that will be covered:

Basic Programming with RIntroduction to R, 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
Basic Data ElementsData transformation (Reshape, Split, Combine), Character manipulation, String manipulation, Dates and timestamps, Web data capture, API data sources, Connecting to an external database.
Manipulating Data with “dplyr”A subset, transform and reorder datasets, Join datasets, Groupwise operations on datasets.
Data Graphics and Data VisualizationCore ideas of data graphics and data visualization, R graphics engines (Base, Grid, Lattice, ggplot2) and big data graphics with ggplot2.
Advanced VisualizationCustomized graphics with ggplot2 (Titles, Coordinate systems, Scales, Themes, Axis labels, Legends) and 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).

Big Data with Amazon Cloud, Hadoop/Spark and Docker

LocationIn-Person – New York City
Class Size10
Start DatesAug 10th – Sep 16th, 2021
Time CommitmentPart-Time
5 hours per week.
Course Length6 Weeks
Cost of Tuition$2,990

This 6-week program provides a hands-on introduction to the Hadoop and Spark ecosystem for Big Data technologies. The course covers key components of Apache Hadoop like HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python, so the class begins with a review of Python concepts. The course's format is interactive, and students will need to bring laptops to class.

Skills You Will Learn

The Big Data with Amazon Cloud, Hadoop/Spark and Docker course focuses on the following skills:

  • Data Science, method of extracting insights from data.
  • Hadoop, an open-source software framework for storing data.
  • Apache Spark, also known as Spark, an open-source, distributed processing system used for big data workloads ensuring fast queries against data of any size.
  • Data Structures, organization format that enables efficient access and modification of data.
  • Linux, the most popular open-source operating system there is. It is similar to Unix and forms the basis of the Android mobile operating system, also one of the most popular in the world.
  • Hadoop, an open-source software framework for storing data.
  • Amazon Web Services, also known as AWS, a secure cloud services platform offering computing power, database storage, content delivery and other functionality to help businesses scale and grow.

Coding Languages You Will Learn

The core coding languages you will learn in the Big Data with Amazon Cloud, Hadoop/Spark and Docker course are:

  • Python, a free software environment and programming language.
  • SQL, a domain-specific programming language designed for managing data held in a relational database management system or for stream processing in a relational data stream management system.

Modules You Will Go Through

This course is divided into 5 units. Here is a breakdown of the topics that will be covered:

Introduction to Hadoop1. Data Engineering Toolkits (Running Linux using Docker containers, Linux CLI command and bash scripts, Python basics).
2. Hadoop and MapReduce (Big Data Overview, HDFS, YARN, MapReduce).
MapReduce3. MapReduce using MRJob 1 (Protocols for Input & Output, Filtering).
4. MapReduce using MRJob 2 (Top n, Inverted Index, Multi-step Jobs).
Apache Hive5. Apache Hive 1 (Databases for Big Data, HiveQL and Querying Data, Windowing And Analytic Functions, MapReduce Scripts).
6. Apache Hive 2 (Tables in Hive, Managed Tables and External Tables, Storage Formats, Partitions and Buckets).
Apache Pig7. Apache Pig 1 (Overview, Pig Latin: Data Types, Pig Latin: Relational Operators).
8. Apache Pig 2 (More Pig Latin: Relational operators, More Pig Latin: Functions, Compiling Pig to MapReduce, The Parallel Clause, Join Optimizations).
Apache Spark and AWS9. Apache Spark – Spark Core (Spark Overview, Running Spark using Databricks Notebooks, Working with PySpark: RDDs, Transformations and Actions).
10. Apache Spark – Spark SQL (Spark DataFrame, SQL Operations using Spark SQL).
11. Apache Spark – Spark ML (ML Pipeline using PySpark).
12. Amazon Elastic MapReduce (Overview, Amazon Web Services: IAM, EC2, S3, Creating EMR Cluster, Submitting Jobs, Intro to AWS CLI).

Data Science with Python: Machine Learning

LocationNew York City, Online
Class Size10
Start DatesAug 1st – Aug 29th, 2021
Time CommitmentPart-Time
4 hours per week.
Course Length5 Weeks
Cost of Tuition$1,990

Through this 20-hour Machine Learning with Python course, students learn basic machine learning methods and Python modules (especially Scikit-Learn). Students will learn 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 completing this course, students will be able to explain the principles of machine learning and use these methods to analyze complex datasets and make predictions in Python.

Skills You Will Learn

The Data Science with Python: Machine Learning (Weekend Course) course focuses on the following skills:

  • Data Science, method of extracting insights from data.
  • NumPy, a library for Python.
  • Scikit-learn, a free software machine learning library that works with Python.
  • Bootstrap, a front-end development framework that helps build fully responsive websites quickly.
  • Artificial Intelligence, also known as AI, is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
  • Machine Learning, the study of computer algorithms that improve automatically through experience and by the use of data.

Modules You Will Go Through

This course is divided into 5 units. Here is a breakdown of the topics that will be covered:

Introduction and RegressionWhat is Machine Learning, Simple Linear Regression, Multiple Linear Regression, Numpy/Scikit-Learn Lab.
Classification ILogistic Regression, Discriminant Analysis, Naive Bayes, Supervised Learning Lab.
Resampling and Model SelectionCross-Validation, Bootstrap, Feature Selection, Model Selection and Regularization lab.
Classification IISupport Vector Machines, Decision Trees, Bagging and Random Forests, Decision Tree and SVM Lab.
Unsupervised LearningPrincipal Component Analysis, Kmeans and Hierarchical Clustering, PCA and Clustering Lab.

Data Science Bootcamp

LocationNew York City, Online
Class Size50
Start DatesAug 16th, 2021 – Dec 3rd, 2021 Full-Time
Aug 16th, 2021 – Feb 4th, 2022 Part-Time
Time CommitmentFull and Part-Time Live: 40+ hours per week.
Full-Time Interactive: 30-40 hours per week.
Part-Time Interactive: 20-30 hours per week.
Course Length12 Weeks In Person, Full Time
4-6 Months Online, Full/Part-Time
Cost of Tuition$17,600

In NYC Data Science Academy's Data Science with Machine Learning bootcamp, students get a 400-hour training (divided into prework, seven modules and a capstone project) where they learn the major tools and methods for performing data analyses, applying them to various projects within the data science field.

At the basic level of this program, students learn to use R and Python for industry-related projects and present research results effectively. Beyond the basic level, students study machine learning with Python and deploy research projects that involve advanced data science methods and strategies. Exposing students to concepts and practices in deep learning and big data.

Skills You Will Learn

The Data Science Bootcamp course focuses on the following skills:

  • GitHub, a cloud-based hosting service that lets you manage Git repositories.
  • NumPy, a library for Python.
  • SciPy, a Python-based ecosystem.
  • Pandas, an open-source data analysis and manipulation tool.
  • Matplotlib, a comprehensive library.
  • Seaborn, a Python data visualization library.

Coding Languages You Will Learn

The core coding languages you will learn in the Data Science Bootcamp course are:

  • Python, a free software environment and programming language.
  • R, a programming language for statistical computing.
  • SQL, a database management programming language.

Modules You Will Go Through

This course is divided into prework, seven modules and a capstone project. Here is a breakdown of the topics that will be covered:

PreworkPrerequisite online coursework includes a total of forty hours of work and over two hundred exercises. The Prework will prepare students to work with both R and Python as well as revisit basic concepts in linear algebra, calculus, and statistics.
– Mathematics/Statistics: Refresh your memory in linear algebra and statistics.
– Calculus: Exercise basic calculus techniques for data.
– Conda Installation: Kick off your Python journey with a beginner-friendly setting!
– Python: Designed for people who are new to programming.
– R: Learn R to process and analyze data.
Data Science ToolkitThe Unix environment is widely used in the data science field. Being familiar with the common tools is important in order to carry out further data analysis. This course enables students to communicate with the computers via the command line environment. It also introduces the SQL database, a traditional database that has been widely used in the enterprise setting, as well as GitHub, a file-sharing platform generally used by programmers for version control.
Data Analytics with PythonThis course introduces students to data analysis with the Python programming language. Students learn to work with different data structures in Python and the most popular data analytics and visualization packages such as numpy, scipy, pandas, matplotlib, and seaborn. Ultimately, students will use effective Python code and packages to solve problems; extract, transform, load, and analyze data to gain insights; and communicate the analyses, aided by appropriate visualizations. Students are required to complete a project incorporating these practices, culminating in a presentation of derived insights.
Data Analytics with RThis course is designed to provide a comprehensive introduction to the R programming language for data analysis. Students will learn to load, save, and otherwise wrangle data with effective use of functions in R and relevant libraries, including those within the tidyverse collection. Students will practice deriving insights from data using common statistical techniques, including hypothesis testing and basic statistical modelling; effective visualization; and other frequently used techniques within data analysis. Further, students will learn to successfully communicate their insights, including creating reports with tools like knitr. Students are required to complete a project demonstrating the ability to analyze data in R.
Business Cases in Data ScienceThis course was designed to help students place data analytics and data science work in the real-world context of business operations across industries. Students will be presented with various business cases in which datasets were explored to gain insights to guide and/or enhance business operations. They will also be required to take given business cases and conceptualize viable project approaches with defined objectives, selected tools and methods, and expected deliverables.
Machine Learning IThis course introduces students to Supervised Machine Learning from both a theoretical and practical perspective. Students will learn the theoretical foundations and mathematical structure behind several important, classical models; design a reproducible machine learning pipeline, including a selection of an optimal model within a given context; and demonstrate the soundness and effectiveness of the final model, with a particular focus on the value of the model for extracting insights from data. Throughout the course, students will see both linear models for regression and classification, Bayesian classifiers, and time series.
Machine Learning IIThis course continues from Machine Learning I to expand the students' arsenal of machine learning algorithms along with their underlying theoretical foundations and implementations in Python. Going further into Supervised Machine Learning, students will learn tree-based models, including Bagging Trees and Random Forest, Gradient Boosting; and Support Vector Machines. Moving into Unsupervised Machine Learning, students will learn techniques of Clustering, including KMeans and Hierarchical approaches, and Matrix Factorization, including Principal Component Analysis and Latent Dirichlet Allocation. Throughout the course, students will adhere to best practices in choosing, tuning, and critiquing their models. Finally, students will be required to complete one machine learning project, in which they will demonstrate their machine learning acumen to distill deeper insights into data.
Data Science: Advanced TopicsThis course introduces students to more advanced data science practices, including Scalability and Deep Learning. On the scalability side, students will gain an overview of contemporary topics such as when to move from the desktop to a database, big data technologies and cloud computing. On the deep learning side, students will learn the basic mathematical construct of deep learning models, understand where deep learning has and has not found success, as well as gain an overview of several important model architectures. Along the way, students will be given examples of where the material they have learned throughout the curriculum compare and manifest in industry.
Capstone ProjectThe capstone project is designed for students to employ the major data science concepts, tools, and methods they have learned in the program to solve a business operational problem with real data sets from a real business entity. Students are presented with data sets and potential problems to solve. Students are then required to form project teams, develop a project proposal for instructor review and approval, and execute the project. When the project is completed, each project team is required to present the project findings and share the business insights obtained from the research.

Data Analytics Bootcamp

LocationNew York City, Online
Class Size50
Start DatesAug 16th, 2021 – Nov 5th, 2021
Time CommitmentPart-Time
Course Length3 Months
Cost of Tuition$9,995

Data Analysis Bootcamp is a fast-paced training program in which students learn major tools and methods to perform data analyses and apply them to various projects. Students learn to use R and Python for data analysis projects and also learn how to present their research results effectively.

Upon graduation, students receive a Certificate of Completion from the school, which is licensed to operate as a Private Career School by the New York State Education Department through its Bureau of Proprietary School Supervision.

Skills You Will Learn

The Data Analytics Bootcamp course focuses on the following skills:

  • GitHub, hosting service that lets you manage Git repositories.
  • NumPy, a library for Python.
  • SciPy, a Python-based ecosystem.
  • Pandas, an open-source data analysis and manipulation tool.
  • Matplotlib, a comprehensive library.
  • Seaborn, a Python data visualization library.

Coding Languages You Will Learn

The core coding languages you will learn in the Data Analytics Bootcamp course are:

  • Python, a free software environment and programming language.
  • R, a programming language for statistical computing.
  • SQL, a database management programming language.

Modules You Will Go Through

This course is divided into 6 modules. Here is a breakdown of the topics that will be covered:

PreworkPrerequisite online coursework includes a total of forty hours of work and over two hundred exercises. The Prework will prepare students to work with both R and Python as well as revisit basic concepts in linear algebra, calculus, and statistics.
Data Science ToolkitThe Unix environment is widely used in the data science field. Being familiar with the common tools is important in order to carry out further data analysis. This course enables students to communicate with the computers via the command line environment. It also introduces the SQL database, a traditional database that has been widely used in the enterprise setting, as well as GitHub, a file-sharing platform generally used by programmers for version control.
Data Analytics with PythonThis course introduces students to data analysis with the Python programming language. Students learn to work with different data structures in Python and the most popular data analytics and visualization packages such as numpy, scipy, pandas, matplotlib, and seaborn. Ultimately, students will use effective Python code and packages to solve problems; extract, transform, load, and analyze data to gain insights; and communicate the analyses, aided by appropriate visualizations. Students are required to complete a project incorporating these practices, culminating in a presentation of derived insights.
Data Analytics with RThis course is designed to provide a comprehensive introduction to the R programming language for data analysis. Students will learn to load, save, and otherwise wrangle data with effective use of functions in R and relevant libraries, including those within the tidyverse collection. Students will practice deriving insights from data using common statistical techniques, including hypothesis testing and basic statistical modelling; effective visualization; and other frequently used techniques within data analysis. Further, students will learn to successfully communicate their insights, including creating reports with tools like knitr. Students are required to complete a project demonstrating the ability to analyze data in R.
Business Cases in Data ScienceThis course was designed to help students place data analytics and data science work in the real-world context of business operations across industries. Students will be presented with various business cases in which datasets were explored to gain insights to guide and/or enhance business operations. They will also be required to take given business cases and conceptualize viable project approaches with defined objectives, selected tools and methods, and expected deliverables.
Data Analytics Capstone ProjectThe capstone project is designed for students to employ the data analytics concepts, tools, and methods they have learned in the bootcamp to solve a business operational problem with real data sets from a real business entity. Students are presented with data sets and potential problems to solve. Students are then required to form project teams, develop a project proposal for instructor review and approval, and execute the project. When the project is completed, each project team is required to present the project findings and share the business insights obtained from the research.

Job Placement & Career Support

NYC Data Science Academy makes sure to surround you with people who will support students every step of the way and whose main focus is their success. Along with them, they will also be working hand to hand with cohort classmates to solve the problems given to them throughout the course. Here's what the school does to help:

  • Three rounds of personalized resume review, LinkedIn profile review, and career guidance sessions.
  • Mock coding challenges, technical and behavioural interview assistance.
  • 1-on-1 post-interview review and feedback sessions with Career Advisors.
  • Life-long access to hiring and networking events with industry professionals.
  • Access to NYC Data Science Academy's alumni network and industry connections.
  • Complimentary access to meetups, workshops and alumni presentations to foster industry relationships.

Hiring Companies and Earnings

The following companies have hired NYC Data Science Academy graduates:

GoogleFacebookBloombergSpotify
Morgan StanleyCitibankGoldman SachsJPMorgan Chase & Co.
Capital OneBarclaysBooz | Allen | HamiltonNational Grid
Mount SinaiAetnaPfizerSamsung
CKMIBMNielsenMindshare
UptakeiHeartRADIOPublicisCasper

These are the percentile wage estimates for Data Scientists and Mathematical Science Occupations published by the US Bureau of Labor Statistics:

Percentile10%25%50% (Median)75%90%
Hourly Wage$25.46$34.51$47.23$62.68$79.44
Annual Wage$52,950$71,790$98,230$130,370$165,230

Costs, Payment Plans & Scholarships

#1 Costs

NYC Data Science Academy’s costs vary depending on when you pay and the program you choose. Here’s how much each of them costs:

ProgramEarly BirdNormal payment
Introductory Python$1,510.50 $1,590
Data Science with Python: Data Analysis and Visualization$1,510.50$1,590
Data Science with R: Data Analysis and Visualization$2,080.50$2,190
Big Data with Amazon Cloud, Hadoop/Spark and Docker$2,840.50$2,990
Data Science with Python: Machine Learning (Weekend Course)$1,890.50$1,990
Data Science Bootcamp$15,840$17,600
Data Analytics Bootcamp$8,995.50$9,995

#2 Payment Plans

NYC Data Science Academy offers 2 payment plans for the Data Science Bootcamp, Data Science with Machine Learning program and the Data Analytics Bootcamp, in partnerships with:

Ascent

Ascent's payment plans offer students taking any of the school's programs loans that range from $2,000 to $17,600.

  • Their Full Deferred Payment plan allows the student to pay $590.58 monthly after graduation. Total: $21,261.
  • Their Interest-Only Payments in School plan allows students to pay $100.10 monthly while in school and then $566.39 monthly after graduation. Total: $20,991.
  • Their Payment in School plan allows students to pay a $566.39 monthly fee from the start of the course. Total: $20,390.

All of these payments are split into 36 monthly installments, and depending on the plan you chose, the interest is higher. And the platform also offers up to an additional $7,500 loan for living expenses.

Climb

Climb Credit is a student loan company that focuses on financing career-building programs to allow students to get a high return of investment from the education they receive. Climb offers:

  • Quick online application that can be filled in 5 minutes with no impact on credit.
  • Affordable interest-only payments while in school and a few months after to ease a graduate’s job search.
  • High loan approvals include financing for students with no credit.
  • Instant decisions most of the time, allowing students to accept and e-sign their documents in a couple of clicks.
  • Friendly and responsive customer service.
  • Pay $375 to $439 per month for 60 months.

#3 Refund Policy

If at any point a student wishes to withdraw from a program, they must notify the school in writing. The date of withdrawal, for refund purposes, is the last date of physical attendance. The failure to notify the School's Director in writing of a withdrawal may delay the refund.

Any student requesting cancellation within seven days after signing the Enrollment Agreement -but before instruction begins- will receive a full refund. However, in the event of cancellation or termination by the school, refunds will be prorated based on the schedule below, with the student's last date of attendance in mind.

Data Science Bootcamp Program

Since this program is designed to be completed within 24 weeks, the total tuition ($17,600) is divided into two payments and charged $8,800.00 per pay period or quarter. The refund table for each payment period is as follows:

1st quarter
If termination occurs:School may keepStudent refund
Prior to or during the first week0%100%
During the second week22.3%77.7%
During the third week30.7%69.3%
During the fourth week39.0%61.0%
During the fifth week47.3%52.7%
During the sixth week55.7%44.3%
During the seventh week or beyond100%0%
2nd quarter
If termination occurs:School may keepStudent refund
Prior to or during the first week0%100%
During the second week25%75%
During the third week33.3%66.7%
During the fourth week41.7%58.3%
During the fifth week50%50%
During the sixth week58.3%41.7%
During the seventh week or beyond100%0%

Data Science Bootcamp Online Program

1st term of 16 weeks
If termination occurs:School may keepStudent refund
Prior to or during the first week0%100%
During the second week18.2%81.8%
During the third week24.4%75.6%
During the fourth week30.7%69.3%
During the fifth week36.9%63.1%
During the sixth week43.2%56.8%
During the seventh week49.4%50.6%
During the eighth week55.7%44.3%
During the ninth week or beyond100%0%

Mini Refund Policy

Applicable to the short (part-time) courses
If termination occurs:School may keepStudent refund
0-15% of the program0%100%
16-30% of the program25%75%
31-45% of the program50%50%
46-60% of the program75%25%
After 60% of the program100%0%

How To Apply To NYC Data Science Academy

Step 1: Go To Their Sign Up Page

You can start your enrollment process by visiting NYC Data Science Academy's website. You can do so by clicking the button below.

Step 2: Fill Out The Application Form

NYC Data Science Academy suggests applicants have a master’s degree or PhDs in science, technology, engineering, mathematics, or an equivalent experience, but Bachelor's or non-STEM degrees will also be considered.

Step 3: Talk to an Admission Officer

After reviewing your application, the school's team will invite you to schedule a video interview. This interview serves as a chance to connect and understand better your background and career goals.

Step 4: Technical Assessment

You will be asked to complete a series of technical questions that will assess your thought process and technical knowledge. You may use any programming language you know to complete the assessment.

NYC Data Science Academy User Reviews

Here are just a handful of verified NYC Data Science Academy reviews from a few of their members:

“I have nothing but positive things to say about the instructors, curriculum, job support, and community at NYC Data Science Academy! From day 1 I felt welcome and part of an extended family.”David Levy.

“If you are ready to learn and put effort, you will find plethora of material, guidance and support here. I have had a positive experience at NYC Data Science Academy . I learned, made friends, networked and progressed in my career.”Priya Srivastava.

“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.”Kailun Cheng.

“…The point of this is – is that job assistance is important. The material can be learned anywhere – other bootcamps, online, universities. But the major selling point for these operations is the job assistance and access to their professional network. And by that metric, NYC Data Science doesn't deliver.”Sam Nuzbrokh.

“…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.”Aaron Festinger.

“Overall, it was a great experience for me. If you are motivated, interested in data science and willing to put in your effort, the bootcamp will be a rewarding journey for you.”Yadi Li.

NYC Data Science Academy Alumni

In case those reviews haven’t convinced you, here are a few quick stories about NYC Data Science Academy members—the sort of people you’ll be rubbing shoulders with once you enter the program.

Before NYC Data Science AcademyAfter NYC Data Science AcademyDo They Recommend NYC Data Science Academy?
Michael was working as a Civil Engineer.Michael is now working as a Sr. Data Analyst at Farmer's Business Network, Ink.Couldn't have asked for a better outcome.
Simon was working as a Tutor.Simon is now working as a Software Engineer for the A.I. Team at American Water.I really liked the instructors here, especially Luke Lin and Zeyu Zhang, as they were so knowledgeable and patient. I also made many friends here, especially for someone who just moved to New York City.
Yuce was working as a Sr. Client Executive.Yuce is now working as a Data Scientist at IBM.NYCDSA is highly recommended! It is worth the time and the money in gold!

How To Contact NYC Data Science Academy

In the following table, you'll find the most important contact info so you can get in touch with NYC Data Science Academy in order to learn more, ask any questions you may have or sign up.

Phone917-383-2099
Email[email protected]
Social Media
Apply Now button
Mailing Address89 Washington Avenue, EBA 560. Albany, New York 12234

In Summary

NYC Data Science Academy is a tech school that offers four kinds of courses, Introductory, Beginner, Intermediate and Bootcamp. Some of the more basic courses don't need you to have any coding experience, but the bootcamps do require you to have a tech or math background. In fact, the school recommends this background to be a Ph.D.

The school doesn't offer any scholarships, but it does offer two payment plans through a partnership with Ascent and Climb. Along with that, it offers life-long career services, which are very useful.

If you want to know more about NYC Data Science Academy, you can click on the button below:

Canada's Best Credit Cards

Exceptional Value

Earn up to 40,000 Aeroplan Miles (valued at $800 - $1,200) with the American Express Aeroplan Card, our #1-rated card in Canada in 2021.

Bonus: Includes Buddy Pass

The Finer Things In Life

Earn up to 80,000 Membership Rewards points (valued at $1,600 - $2,400) with the American Express Platinum Card, plus a $200 annual travel credit.

Bonus: Airport Lounge Access

Low Fee, High Value

Earn up to 50,000 MR points (valued at $1,000 - $1,500) with the American Express Cobalt Card, a low-fee card that offers 5x the points on all food and drinks.

Bonus: $100 USD hotel credit