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Lifelong Career Support

AI bootcamp for Real World Applications

IN-PERSON/REMOTE LIVE INSTRUCTION or ONLINE INSTRUCTION
Master the skills to build and launch comprehensive AI solutions, and effectively leverage the capabilities of AI.
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Immerse yourself in accelerated learning and launch your artificial intelligence career through Full-time/In-Person/Remote-Live Learning or Part-time/Online Interactive Learning!
This bootcamp is designed to equip you with the dominant skills and tools for artificial Intelligence.

AI bootcamp for Real-World Applications - Online

Interactive Distance Learning: Online Learning, 1-on-1 Mentor, 2 or 3 Months, Full/Part Time
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Spring-mid Quarter (Full-time)
March 24
Mar 31, 2025 - May 23, 2025
$9,995
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Spring-mid Quarter
March 24
Mar 31, 2025 - May 23, 2025
$9,995
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Spring-mid Quarter (Part-time)
March 24
Mar 31, 2025 - Jun 20, 2025
$9,995
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Spring-mid Quarter
March 24
Mar 31, 2025 - Jun 20, 2025
$9,995
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Summer Quarter (Full-time)
May 5
May 12, 2025 - Jul 11, 2025
$9,995
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Summer Quarter
May 5
May 12, 2025 - Jul 11, 2025
$9,995
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Summer Quarter (Part-time)
May 5
May 12, 2025 - Aug 8, 2025
$9,995
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Summer Quarter
May 5
May 12, 2025 - Aug 8, 2025
$9,995
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Summer-mid Quarter (Full-time)
June 16
Jun 23, 2025 - Aug 15, 2025
$9,995
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Summer-mid Quarter
June 16
Jun 23, 2025 - Aug 15, 2025
$9,995
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Summer-mid Quarter (Part-time)
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Jun 23, 2025 - Sep 12, 2025
$9,995
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Jun 23, 2025 - Sep 12, 2025
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Program Highlights
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Launch your career in AI

This immersive program is designed to prepare students for a career in AI, while equipping them with the knowledge and skills required to use industry tools and apply them in real-world applications.
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Our Data Science Bootcamps have been rated as "Best Data Science Bootcamp" and "Best Online Bootcamp", "Best Data Science Online Bootcamp" by Switch-Up, CourseReport and Forbes

A Complete Curriculum
Rich Curriculum Content

The only bootcamp that teaches Deep Learning, Large Language Models, Local language Model, AI agent workflow, Cloud computing, and Vector Databases for building real-world applications.

Project-oriented
Project-Oriented

Four application projects with real-world datasets and business considerations; the capstone project is often sponsored by companies in New York City.

Cutting Edge
Industry-Informed

Curriculum designed and updated with input and advice from business leaders and experts in AI and machine Learning field.

Career services
Career services

Lifelong career support, including one-on-one resume reviews, interview prep, and exclusive access to networking opportunities with our hiring partners

Engaging community
Engaging Community

A live and evolving AI/ML/DS/DE/DA community with instructors, students, alumni, and data science professionals across all industries.

Options to Meet Your Need

NYC Data Science Academy offers different options of in-person immersive learning and fully online or interactive distance learning to meet individual student needs. Although the bootcamp curricular contents are the same, students can choose a mode of instruction that best suits their needs and their unique learning style.

* Check specific requirements with Admissions Officer.

In-Person* or Remote Live Learning

(Either In-Person or Remote Live Learning)
Daily in-person learning or live learning in Zoom
Set schedules for all learners
Real-time interactions in the same environment
Synchronous networking opportunities
Community for immersive experience

Interactive Distance Learning

Access instructions at your own convenience
Plan your own schedules to meet learning objectives
Asynchronous interactions with instructors and staff
One-on-one support by academic mentors
Longer time to master curricular competencies
In-person/Remote live
AI bootcamp
4 Weeks (Full-time)
Commit 40+ hours/week
Interactive Distance Learning
AI Bootcamp
8 Weeks (Full-time)
Commit 20+ hours/week
Interactive Distance Learning
AI Bootcamp
12 Weeks (Part-time)
Commit 13+ hours/week

What You Will Learn

In the 160-hour "AI Bootcamp for Real World Applications", students will take four modules as listed below and learn the major tools and methods for performing AI, DevOps and apply them to one capstone project to solve real-world business problems.

At the foundation level of the program, students learn to employ and understand the current landscape of AI, especially the Generative AI, and be able to formulate potential business applications to solve specific problems.

Beyond the foundational level, students study Deep Learning such as PyTorch, CNN, RNN, Transformers and carry out research projects that involve advanced AI methods and strategies. The program also exposes students to concepts and practices in Language Models, Prompt engineering, Vector Databases and Embeddings, Retrieval Augmented Generation, Etc.

Finally, students will learn how to productionalize their ideas using all the newly acquired skills to deploy AWS web applications to solve an end-to-end solution.

We cover API such as OpenAI, Huggingface, Meta AI, but not limited to these technologies:

  • - OpenAI is a research organization focused on developing and promoting friendly AI for the betterment of humanity.
  • - Hugging Face is an open-source provider specializing in natural language processing and transformers for a variety of AI applications.
  • - Meta AI, formerly known as Facebook AI Research, is a research division dedicated to advancing the field of artificial intelligence across a range of technologies and applications.
  • - Prompt engineering is the practice of designing and refining inputs to effectively communicate with AI models to generate desired outputs.
  • - Vector databases and embeddings are specialized storage and retrieval systems for high-dimensional data vectors used in machine learning and similarity searches.
  • - Retrieval Augmented Generation is an approach in natural language processing that enhances language models by integrating external knowledge retrieval into the generation process.
Prerequisites
AIBS516 Productionalization
AIBC502 Generative AI for Everyone
AIBC506 Deep Dive in Deep Learning
AIBC511 Deep Learning and Language Model
Apply Now
Download Curriculum
Prerequisites

Python: Participants should have a solid understanding of the Python programming language, including knowledge of data structures, control flow, functions, and libraries commonly used in data analysis and machine learning, such as NumPy, Pandas, and Scikit-learn.

Data Analysis and Machine Learning: Familiarity with data analysis concepts, exploratory data analysis (EDA), and machine learning algorithms is essential.

Deep Learning Basics: Basic knowledge of deep learning concepts is not required but recommended. We will teach you from scratch.

AIBS516 Productionalization
  • Amazon Web Services (AWS)
    • Introduction
    • Making a Postgres Vector Database
    • Creating a Serverless API
    • Deploy a Web UI
  • Product Building Blocks
    • Putting the User/Problem First
    • Efficiency and Time-Saving
    • Document Understanding and Deriving Insights
    • Marketing, Advertising, and Sales
    • Creativity Enhancing Tools

Capstone Project: Build and Deploy a Full Stack AI Web Application

AIBC502 Generative AI for Everyone
  • Introduction to Generative AI
    • Perception vs. generation
    • Notable examples
  • Types of Generative Models
    • Autoregressive Models
    • Autoencoders
    • Generative Adversarial Networks (GANs)
    • Transformers
    • Diffusion Models
    • The rise of prompting
  • Overview of Building and Training Generative Models
    • Gathering Training Data
    • Creating a Basic Model
    • Defining a Training Loop
    • Scaling up
  • Language, Image, Video, Text, and Music Generation using AI
    • Large Language Models
    • Multimodality, Language/Vision Models
    • Text to Image Models
    • From images to Videos
    • 3D Scenes and Animation
    • Approaches to Music
  • Opportunities for Generative AI
    • Opportunities for new products
    • Improving efficiency
  • Implications of Generative AI

Project 1: Article on why Generative AI Matters to You

AIBC506 Deep Dive in Deep Learning
  • Introduction to Deep Learning with PyTorch
    • Tensors
    • Nonlinearities
    • Modules
    • Dataset and DataLoader
    • Gradients and Backpropagation
    • Optimizers
    • Training a simple Multilayer Perceptron (MLP)
  • Convolutional Neural Networks (CNNs)
    • Motivation: Computer Vision
    • Traditional and Learned Kernels
    • Pooling
    • VGG and Resnet
  • Recurrent Neural Networks (RNNs)
    • Motivation: Sequential Data
    • Backpropagation Through Time
    • LSTM and GRU
  • Transformers
    • The Attention Mechanism
    • Expressiveness and Complexity
    • Comparison to RNNs

Project 2: Train and Test a Deep Learning Model

AIBC511 Deep Learning and Language Model
  • Working with APIs
    • REST API basics
    • Working with the OpenAI REST/Python client
    • Working with Huggingface's Python client
  • Prompt engineering
    • System Prompts, Tasks, and Personas
    • Zero/Few Shot Prompting
    • Chain of Thought
    • Function Calling and Tool Use
    • Receiving Structured Output
    • Strategies for Handling Context Limits
  • Vector Databases and Embeddings
    • What are Embeddings?
    • Vector Index and Approximate Nearest Neighbor (ANN) Search
    • Vector Database
    • Semantic Search
    • Retrieval Augmented Generation (RAG)
    • Semantic Routing
  • Agents
    • Solo
    • Debate
    • Collective
    • Chat Bots
  • Fine Tuning
    • What is Fine Tuning?
    • Types of Fine Tuning
    • Parameter Efficient Fine Tuning (PEFT)

Project 3: Create a Task-Specific LLM Agent

Prerequisites
Prerequisites
AIBS516 Productionalization
AIBC502 Generative AI for Everyone
AIBC506 Deep Dive in Deep Learning
AIBC511 Deep Learning and Language Model

Python: Participants should have a solid understanding of the Python programming language, including knowledge of data structures, control flow, functions, and libraries commonly used in data analysis and machine learning, such as NumPy, Pandas, and Scikit-learn.

Data Analysis and Machine Learning: Familiarity with data analysis concepts, exploratory data analysis (EDA), and machine learning algorithms is essential.

Deep Learning Basics: Basic knowledge of deep learning concepts is not required but recommended. We will teach you from scratch.

  • Amazon Web Services (AWS)
    • Introduction
    • Making a Postgres Vector Database
    • Creating a Serverless API
    • Deploy a Web UI
  • Product Building Blocks
    • Putting the User/Problem First
    • Efficiency and Time-Saving
    • Document Understanding and Deriving Insights
    • Marketing, Advertising, and Sales
    • Creativity Enhancing Tools

Capstone Project: Build and Deploy a Full Stack AI Web Application

  • Introduction to Generative AI
    • Perception vs. generation
    • Notable examples
  • Types of Generative Models
    • Autoregressive Models
    • Autoencoders
    • Generative Adversarial Networks (GANs)
    • Transformers
    • Diffusion Models
    • The rise of prompting
  • Overview of Building and Training Generative Models
    • Gathering Training Data
    • Creating a Basic Model
    • Defining a Training Loop
    • Scaling up
  • Language, Image, Video, Text, and Music Generation using AI
    • Large Language Models
    • Multimodality, Language/Vision Models
    • Text to Image Models
    • From images to Videos
    • 3D Scenes and Animation
    • Approaches to Music
  • Opportunities for Generative AI
    • Opportunities for new products
    • Improving efficiency
  • Implications of Generative AI

Project 1: Article on why Generative AI Matters to You

  • Introduction to Deep Learning with PyTorch
    • Tensors
    • Nonlinearities
    • Modules
    • Dataset and DataLoader
    • Gradients and Backpropagation
    • Optimizers
    • Training a simple Multilayer Perceptron (MLP)
  • Convolutional Neural Networks (CNNs)
    • Motivation: Computer Vision
    • Traditional and Learned Kernels
    • Pooling
    • VGG and Resnet
  • Recurrent Neural Networks (RNNs)
    • Motivation: Sequential Data
    • Backpropagation Through Time
    • LSTM and GRU
  • Transformers
    • The Attention Mechanism
    • Expressiveness and Complexity
    • Comparison to RNNs

Project 2: Train and Test a Deep Learning Model

  • Working with APIs
    • REST API basics
    • Working with the OpenAI REST/Python client
    • Working with Huggingface's Python client
  • Prompt engineering
    • System Prompts, Tasks, and Personas
    • Zero/Few Shot Prompting
    • Chain of Thought
    • Function Calling and Tool Use
    • Receiving Structured Output
    • Strategies for Handling Context Limits
  • Vector Databases and Embeddings
    • What are Embeddings?
    • Vector Index and Approximate Nearest Neighbor (ANN) Search
    • Vector Database
    • Semantic Search
    • Retrieval Augmented Generation (RAG)
    • Semantic Routing
  • Agents
    • Solo
    • Debate
    • Collective
    • Chat Bots
  • Fine Tuning
    • What is Fine Tuning?
    • Types of Fine Tuning
    • Parameter Efficient Fine Tuning (PEFT)

Project 3: Create a Task-Specific LLM Agent

Apply Now
Download Syllabus

Outlooks and Outcomes

25%↑
Fast-growing career field
Operations Research Analysts : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics (bls.gov)
$94,280
Median Annual salary
Operations Research Analysts : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics (bls.gov)

Networking opportunities with alumni and hiring managers

Networking Opportunities
Apply Now
Schedule a Call
Apply Now
Schedule a Call

Featured Alumni

Tyler Kim
Tyler Kim
Data Scientist
Kiss Products, Inc.
Kiss Products, Inc.
Royce Ho
Royce Ho
Software Engineer
Amazon
Amazon
Domingos Lopes
Domingos Lopes
Software Engineer
Google
Google
Kweku Ulzen
Kweku Ulzen
Seinor Data Scientist
Neilsen
Neilsen
Elsa Vera Amores
Elsa Vera Amores
Data Scientist
JP Morgan Chase
JP Morgan Chase
Sheetal Darekar
Sheetal Darekar
Data Scientist
Verizon
Verizon
Katie Critelli
Katie Critelli
Data Scientist
Deutsche Bank
Deutsche Bank
Mikhail Stukalo
Mikhail Stukalo
Principal Data Scientist, AI Investment Management
Fidelity Investments
Fidelity Investments
Youngmin Paul Cho
Youngmin Paul Cho
Data Scientist
Mars
Mars
Sean Kickham
Sean Kickham
Data Science Manager
PwC
PwC
Tyler Kim
Tyler Kim
Data Scientist
Kiss Products, Inc.
Kiss Products, Inc.
Royce Ho
Royce Ho
Software Engineer
Amazon
Amazon
Domingos Lopes
Domingos Lopes
Software Engineer
Google
Google
Kweku Ulzen
Kweku Ulzen
Seinor Data Scientist
Neilsen
Neilsen
Elsa Vera Amores
Elsa Vera Amores
Data Scientist
JP Morgan Chase
JP Morgan Chase
Sheetal Darekar
Sheetal Darekar
Data Scientist
Verizon
Verizon
Katie Critelli
Katie Critelli
Data Scientist
Deutsche Bank
Deutsche Bank
Mikhail Stukalo
Mikhail Stukalo
Principal Data Scientist, AI Investment Management
Fidelity Investments
Fidelity Investments
Youngmin Paul Cho
Youngmin Paul Cho
Data Scientist
Mars
Mars
Sean Kickham
Sean Kickham
Data Science Manager
PwC
PwC
More alumni

Sample Portfolio

Intrinsica.ai
GoDental.ai

Get Hired Along the Way

Students are provided extensive job placement assistance ranging from individualized resume support and interview guidance to access to our network of hiring partners and events.
Customized resume support, LinkedIn profile review, elevator pitch workshops and career guidance
Three rounds of personalized resume review, LinkedIn profile review, and career guidance sessions
Mock coding challenges, technical and behavioral 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
Three rounds of personalized resume review, LinkedIn profile review, and career guidance sessions
Mock coding challenges, technical and behavioral 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

Choose The Path That's Right For You

Choose The Path That's Right For You Choose The Path That's Right For You
AI bootcamp for building real-world applications AI bootcamp for building real-world applications - Online Data Science with Machine Learning Bundled Professional Development Courses Professional Development Courses
Commitment Full-time
Remote Live/In-person
Full-time / Part-time
Online
Online Part-time Remote Live/In-person
Duration 4 Weeks
Only on Weekdays
8 Weeks (Full-time)
12 Weeks (Part-time)
12 Weeks Combination of professional development courses 4 - 6 weeks
Weekdays or Weekends
Career Support / / /
Cost $9995 $9995 $9995 Starts at $4500 Starts at $1500
Financing Options Maybe Maybe / / /
Choose The Path That's Right For You Choose The Path That's Right For You
Choose The Path That's Right For You

Learn From the Best

Cole Ingraham
Cole Ingraham
Lead AI instructor
Dr. Cole Ingraham has been teaching and working with music, game design, and machine learning for decades. He started learning software engineering and data science by studying music composition and computer animation. As an educator, he taught various instruments, writing music, and programming to students from elementary school through university and beyond. His career as the chief scientist and musician has led him to design Amper Music, the world's leading AI music composition platform from 2015-2020. Later, Cole joined Shutterstock as Director of AI and BlueCore as Director of Data Science. Cole has worked in the industry building generative AI models for Fortune 500 companies and giving consulting services to investment firms and top leaderships in a big array of industries on how to build and invest in a future of Generative AI. He is our Lead AI instructor and in charge of Generative AI and Large Language Model bootcamps and courses.
Kyle Gallatin
NYC Data Science Mentor
Kyle Gallatin is currently a software engineer on the machine learning platform team at Etsy. In this role, Kyle is redesigning existing ML systems with a focus on ML model training, real-time model serving, MLOps processes, and model governance. Kyle spends his free time teaching and volunteering within the ML space. He also writes articles for technical publications on ML engineering, MLOps, and infrastructure.
Vivian Zhang
Chief Technology Officer and School Director
Vivian is the CTO and School Director of NYC Data Science Academy and CTO of SupStat. She is an adjunct professor at Stony Brook University and founded the NYC Open Data Meetup, which is 4000 strong. She has many years of practical experience in data technologies and the analytics, and has expertise in multiple programming languages including R, Python, Hadoop, and Spark. Vivian was ranked in "9 Women Leading The Pack In Data Analytics" by Forbes in August 2016. She enjoys meeting people and enjoys sharing her experiences with young professionals and students.
Cole Ingraham Kyle Gallatin Vivian Zhang

Connect with Academic Mentors From Industry

Sumanth Reddy
Sumanth Reddy
Sumanth has been a Senior Data Engineer at Draft King for six years and graduated after our boot camp. Sumanth genuinely enjoys complex analysis of dynamic systems. As a former professional poker player, he has made countless analytical decisions under pressure and is intimidated by challenging questions. With undergraduate backgrounds in physics and economics, Sumanth is self-driven to find the answers to stimulating questions about our universe. He is a true team player, always concerned about the people around him, and it is gratifying to make sure everyone is working well together. He recently quit his job to focus on new interests, including mentoring our students and working on a new course at NYC Data Science Academy called “A Manager’s Guide for Data Science Professionals” and graduate school application. He has tons of real-world work experience.
Sining Chen
Principal Data Scientist
Sining is Principal Data Scientist at NYC Data Science Academy. She is also an adjunct professor at Columbia University. She was the director of Data Science at Warner Music Group. She Led and oversaw the development of core algorithms with methodological soundness and business impact: e.g. demand forecasting of cultural products, optimization, marketing research, knowledge graph, NLP tools, deep learning tools; and engineering efforts in bringing machine learning algorithms to production as enterprise-level tools.
Her academic credentials include a Ph.D. from Duke University in Statistics and Decision Sciences and an extensive career in data science, marked by Director of Data Science at Warner Music Group, Technical Staff at Bell Laboratories, Associate Professor at Rutgers University, and Assistant Professor at Johns Hopkins University.
Stella Kim
NYC Data Science Mentor
Stella Kim is a highly analytical and motivated individual interested in using AI to reshape business strategies to make data-driven, customer-centric decisions. She is currently working as a data scientist in the telecommunications industry under their Corporate Finance division. She holds a Master's in Biotechnology, has Ph.D. experience in Cancer Biology and Genomics and has worked as a data scientist in the biopharmaceutical industry. She is proficient in Python, R, and SQL, and is skilled in data analytics, visualization, machine learning, and statistical methodology.
Tristan Dresbach
Instructor
Tristan Dresbach is a data scientist with a BA in economics and a proven track record of using data to drive significant and tangible business results. She has hands-on experience in web-scraping, data visualization, supervised and unsupervised predictive modeling, as well as optimization.
David Corrigan
Instructor
David is assistant director at Sema4, a precision medicine company! Previously Researcher and data scientist with a doctorate degree in Microbiology and Immunology from Columbia University (2018). Enrolled in an immersive, 12-week Data Science Bootcamp (NYC Data Science Academy) after graduation to refine and develop skills in data science.
Denis Nguyen
NYC Data Science Mentor
Denis is a Bootcamp graduate and has been working in the analytics space for over 2 years. With a growing interest in business and management, he obtained his MBS in Analytics from Rutgers University and works on performance improvement through workflow automation. He enjoys sparking creativity in students and helping them think about how to view data to garner insights. In his free time, Denis enjoys exploring nature and learning about various topics on YouTube. The success of students is Denisu2019 priority and he looks forward to working with you.
Jonathan Presley
Instructor
Jonathan is an award-winning public health professional with a certification in data science dedicated to data-based decision-making and efficiency. With nine years of combined experience in scientific research, biotechnology, education, and healthcare, he has developed a variety of skills in research, policy, program development and implementation, monitoring and evaluation, capacity building, and analytics. Jonathan's curiosity and learn-by-doing approach - instilled by his alma mater, Cal Poly, San Luis Obispo - has pushed him to exercise his leadership and innovative spirit domestically and abroad, whether it be studying international health systems in Denmark, volunteering with clinical lab scientists in Bolivia, or developing new health programs in San Luis Obispo.
Sumanth Reddy Sining Chen Stella Kim Tristan Dresbach David Corrigan Denis Nguyen Jonathan Presley

Start your Application

1

Apply for the Program

We suggest applicants to have a master’s degrees or Ph.D.s in Science, Technology, Engineering or Mathematics, or equivalent experience. Bachelor's or non-STEM degrees will also be considered.

2

Talk to an Admission Officer

After reviewing your application, our team will invite you to schedule a video interview. The interview serves as a chance to connect and better understand your background and career goals.

3

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 to complete the assessment.

4

Welcome on Board!

Our admissions process is highly competitive, rigorous and may take up to 7-10 business days. We encourage students to apply early as limited seats are offered and filled on first-come-first-serve basis.

Upcoming Cohorts

Spring-mid Quarter
Full-time
March 31, 2025 - May 23, 2025
Spring-mid Quarter
Part-time
March 31, 2025 - June 20, 2025
Summer Quarter
Full-time
May 12, 2025 - July 11, 2025
Summer Quarter
Part-time
May 12, 2025 - August 8, 2025
Summer-mid Quarter
Full-time
June 23, 2025 - August 15, 2025
Summer-mid Quarter
Part-time
June 23, 2025 - September 12, 2025
Spring-mid Session
March 31, 2025 - May 23, 2025
Spring-mid Session
March 31, 2025 - June 20, 2025
Summer Session
May 12, 2025 - July 11, 2025
Summer Session
May 12, 2025 - August 8, 2025
Summer-mid Session
June 23, 2025 - August 15, 2025
Summer-mid Session
June 23, 2025 - September 12, 2025
Apply Now
Schedule a Call

Tuition & Finance

Tuition Total
$17,600
full tuition payment
Payments can be made either by cash, credit card, check, or wire-transfer
Reserve Seat
Third-Party Financing Options
We partner with Ascent Fund and Climb Credit to offer third-party financing options to interested students.
Ascent Fund offers fixed interest rates on 3- and 5-year loans, regardless of current income, employment, or educational background. Climb Credit offers fixed interest rate loans for various types of credit, including students with no credit. International students are eligible to apply with a qualified co-borrower.
$397.88
per month for 60 months.
Borrow up to an additional
$7,500 for cost of living
$375 - $439*
per month for 60 months
Available to foreign citizens with a
U.S. citizen or resident co-signer
* range varies based on approval interest rate
Merit Scholarships:
Women in Data Science Scholarship
Post-Doctoral Student Scholarship
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What Our Alumni Say About Us

Sofia Wang David Corrigan Dean Goldman Michael Chuang David Steinmetz
Sofia Wang
Business Intelligence Analyst - The Trade Desk
Upon graduation, there are helpful resources on landing the next job - mock interviews, career advising, and networking.
David Corrigan
Data Scientist II - Sema4
You will learn far more efficiently here than you would on your own. There is simply too much material to learn in such a short time – without knowledgeable instructors who know how to plan and execute a curriculum, you will take much longer to master the same amount of material.
Dean Goldman
Data Engineer - Samsung Ads
Within a few weeks after graduating, I was offered my first full-time job as a data engineer. (I do not come from a CS, math or stats background). NYC Data Science Academy is very successful at getting their students fluent in the tools and technologies of data science, and prepared for finding great jobs in the field.
Michael Chuang
Data Analyst - Facebook
The course covered broad topics in data science with enough depth to be applied in the real world, so I now feel empowered to further my learning and tackle even harder data/tech problems after the camp.
David Steinmetz
Machine Learning Data Engineer - Capital One
The opportunity to network was incredible. You are beginning your data science career having forged strong bonds with 35 other incredibly intelligent and inspiring people who go to work at great companies. The value of those friendships and the ability to create a strong network at the beginning of your data science career will become evident a couple of years down the road.
Read more alumni testimonials

Upcoming Events

Campus Location

Campus Location
500 8th Ave Suite 905, New York, NY 10018
Nearby Subways
1 2 3 34th, Penn Station
A C E 34th, Penn Station
N Q R B D F M 34th, Herald Square
Detailed Directions

Student Life on Campus

9 AM
Map Marker
10 AM
11 AM
12 PM
1 PM
2 PM
3 PM
4 PM
5 PM
6 PM
7 PM
8 PM

9:30 AM - 10:00 PM

Students get settled in, have group discussions or light review sessions with each other.

10:00 AM - 12:30 PM
Morning Lectures

The lectures are conducted in modern classrooms equipped with live web-streaming technology. Students have 24/7 building access, free high-speed internet, pantry with breakfast cereal, tea, coffee, granola bars, fresh fruits and more. The detailed daily schedule is shared with students during the orientation.

12:30 PM - 2:00 PM
Lunch Break

Students visit restaurants in Midtown or spend time in our lounge. Our pantry has two refrigerators, microwaves, coffee-makers, SodaStream, and cutlery, as well as a comfortable seating area furnished with sofas, massage chairs, conference tables, and charging stations for students to relax or network with peers.

2:00 PM - 4:30 PM
Hack Session

Students spend time in the afternoon reviewing what they learned from the morning, solving doubts with their dedicated TAs/instructors, or discussing group projects. We also have private rooms for group discussions, group study sessions or interview preparation, equipped with whiteboards and video-conferencing facilities.

4:30 PM - 5:30 PM
Wrap up session

NYC Data Science Academy hosts open presentations for bootcamp students. This allows students to practice their skills and present their project in front of students, instructors and career counselors. Various free sessions on interview training, job support, alumni networking, etc. are also organized.

5:30 PM - 8:30 PM
Events

On some evenings, we organize free meetups or talks planned around the latest data science techniques. They help in networking, optimizing open data resources and providing excellent on-job training. We also organize “happy hours” to help students destress, network and share feedback with NYC Data Science Academy family.

Frequently Asked Questions

What is the application process?

Interested applicants are encouraged to submit their bootcamp application as early as possible as the Academy has a competitive process due to the number of applications that are received and limited seats that are offered.

The application process consists of three steps:

  1. Applicants are required to submit an online application designed to help the Admissions team get a sense of each applicant’s educational and work background. Applicants are also asked to complete self-assessment questions about their familiarity and technical experience with various data science topics and tools. It should not take more than 10 to 15 minutes to complete the online application.
  2. After the application is reviewed, the applicant will be contacted by a member of the Admissions team to schedule a video interview. This interview serves as a chance for both parties to understand better if the bootcamp program is the best fit for the background and goals of the applicant.
  3. If advanced to the final step, the applicant is invited to complete a technical assessment. It contains technical challenges that determine the thought process and programming experience of the applicant. Each applicant has 48 hours to complete and submit the assessment. The team will then review and evaluate the responses and determine if the applicant is ready to join the bootcamp.

What do you offer in your 12-week "AI bootcamp for real-world applications- Online"?

The 12-week "AI bootcamp for real-world applications" is an interactive distance learning program. It is an accelerated training program in which students learn the major tools and methods for performing data analyses and apply them to various projects typically found in real-life business situations. Students learn to employ Large or local language models and deep learning for solving real-world problems and for presenting research results effectively.

How does the Online Bootcamp differ from the In-person/Remote Live Bootcamp?

While the curriculum for both online and in-person bootcamps are the same, the online bootcamp is designed to provide an interactive learning experience to students who prefer to learn online anytime, anywhere using any device.

What do you offer in your 4-week "AI bootcamp for real-world applications"?

The program is designed to equip participants with the essential skills and in-depth knowledge required to harness the power of AI effectively. By combining theory with extensive hands-on practice, this course ensures that participants gain a deep understanding of AI concepts and the ability to apply them to various domains.

Students will learn to master the skills to build and launch comprehensive AI solutions and effectively leverage the capabilities of AI.

Who is eligible to apply?

To enroll in the bootcamp, applicants must possess a minimum of a Bachelor’s Degree. Degrees in Math, Science or Technology are highly desirable. However, applicants with strong domain knowledge in an area that employs data scientists, and some background in either coding or statistics, will also be considered.

Under exceptional circumstances, an applicant without a baccalaureate degree may be considered for admittance into the bootcamp, in which case the applicant must meet the following requirements:

  • Proof of high school graduation
  • Proof of exceptional talent in computer programming
  • Evidence of domain knowledge in math and science
  • Pass the Academy’s Technical Assessment with a B or higher grade
  • Two letters of recommendation by relevant professionals

Prerequisites

Python: Participants should have a solid understanding of the Python programming language, including knowledge of data structures, control flow, functions, and libraries commonly used in data analysis and machine learning, such as NumPy, Pandas, and Scikit-learn.

Data Analysis and Machine Learning: Familiarity with data analysis concepts, exploratory data analysis (EDA), and machine learning algorithms is essential.

Deep Learning Basics: Basic knowledge of deep learning concepts is optional but recommended. This program will teach you from scratch.

What is the time commitment?

Our Immersive  AI Bootcamp for Real World Applications  (In-person or Remote-live) is a full-time program. Students must be able to commit 40 hours or more to studying and doing homework and projects. The typical class schedule is 9:30 a.m. - 6:00 p.m. during the week, and students are expected to spend more time out of class to complete project work. For online instruction bootcamps, the time commitment varies based on the delivery mode chosen by students.  AI Bootcamp for Real World Applications - Online offers full-time(8 weeks) and part-time (12 weeks) options.

Does NYC Data Science Academy help bootcamp students with job placement?

Yes. NYC Data Science Academy has partnered with diverse companies from small startups to large corporations to help place our students in positions just right for them. But a job is not guaranteed. Our job placement assistance includes:

  • Resume review, LinkedIn profile improvement, interview skills workshops
  • In-class Industry Experts speaker series
  • Hiring partner event series, including student presentations and a hiring partner networking gala party
  • Mock technical interview and coding tasks
  • Presentation of projects and networking with data science peers through our meetup events
  • Real-world consulting project opportunities offered by hiring companies
  • Company site visits
  • Access to post-graduation resources

Do online bootcamp students get job placement support?

Yes. NYC Data Science Academy has partnered with diverse companies from small startups to large corporations to help place our students in positions just right for them. But a job is not guaranteed. Our job placement assistance includes:

  • Resume review, LinkedIn profile improvement, interview skills workshops
  • In-class Industry Experts speaker series
  • Hiring partner event series, including student presentations and a hiring partner networking gala party
  • Mock technical interview and coding tasks
  • Presentation of projects and networking with data science peers through our meetup events
  • Real-world consulting project opportunities offered by hiring companies
  • Company site visits
  • Access to post-graduation resources

How is Online Data Science Bootcamp different from other Online Courses?

A Complete Curriculum

The Online  AI Bootcamp for Real World Applications     provides a comprehensive set of learning materials including lecture videos, slide decks, homework assignments with solutions and explanations, code reviews, jump-start sessions, and a selection of industry talks, online webinars and interactive meetups.

High Engagement and Completion Rate

Open online courses are often insufficient in acquiring a deeper understanding of challenging materials as the topics covered are usually elementary, with an average completion rate of 10%. Our online bootcamp ensures that students achieve the requisite level of mastery by progressing through our comprehensive set of learning materials.

Thorough Interaction and Support

We provide a combination of one-on-one tutoring and project mentoring through our dedicated meeting portal. Students can book up to 80 one-hour video meetings with leading data scientists from global companies.

Amazing Job Placement Assistance

Our career advisors work closely with every student to provide tailored support which includes coding reviews, interview preparation, resume workshops, and exclusive access to our hiring partner network.

How much statistics and programming are required for the program?

We work with every student individually to get their skills up to a level where they can start the bootcamp. We offer customized pre-work packages for accepted students. If students have limited statistical and programming background, there are some books and online courses that we recommend. Also for students who are already in the New York City area, they can take part-time courses for free(tuition deducted from bootcamp tuition within 9 months).

What is the cost for bootcamp?

The tuition for  AI Bootcamp for Real World Applications or AI Bootcamp for Real World Applications- Online is $9,995. 

The tuition for Data Science with Machine Learning or Data Science with Machine Learning - Online is $17,600. 

The tuition for Data Analytics Bootcamp or Data Analytics Bootcamp - Online is $9,995. 

A deposit of $5,000 is required after acceptance to secure your spot.

Do you provide financial assistance?

Funding opportunities are available through two different financing institutions: Ascent and Climb.

Ascent offers fixed interest rates on 3- and 5-year loans, regardless of current income, employment, or educational background. In addition to the cost of the program, they offer a living stipend up to $7,500 with tuition financing. 

Climb Credit offers fixed interest rate loans for both immersive and bundled part-time courses. Financing is available for various types of credit, including students with no credit. 95% of applicants will receive an instant decision after completing Climb's quick 5-minute application. International students are welcome to apply with a qualified co-borrower who is either a US citizen or permanent resident. Living expense stipends are also offered to those who qualify. 

Information for either can be found on their respective sites.

Are scholarships available?

Yes, you can find the details here.

Does NYC Data Science Academy provide visas?

For certain programs, we offer F or M visa. The majority of our students came to our program without visas from us.

Have More Questions?

For more questions, visit our frequently asked questions page or schedule a call with us. with our admissions team.

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Live Class on Zoom

Learn online in real-time from expert instructors and dedicated mentors alongside their peers through Zoom meetings.

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Community Interaction

Our online community ensures that students always feel supported. The bootcamp involves group projects, peer programming, and online communication with their classmates and instructors. We aim to foster a community where every student can learn from each other’s codes and build the skills needed in a collaborative environment.

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Online Learning Platform

Students can access their coursework, get project feedback from instructors and mentors, and interact with fellow students for a truly embedded learning experience. The platform is designed to foster collaboration between students, instructors, and mentors. With our responsive interface that works on any device, students are able to access their learning materials anytime, anywhere.

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Dedicated Meeting Portal for 1-on-1 Support

The online meeting portal allows students to schedule meetings with our dedicated mentors to discuss coursework, projects, and career advice. These mentors are professional data scientists working at global companies including PriceWaterhouseCoopers, Unilever, Vanguard, and National Grid.

NYC Data Science Academy

NYC Data Science Academy’s mission is to provide accelerated data science training programs that prepare people for employment as data science professionals and to offer continuing education courses for professional development.

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