Attend the most comprehensive data science bootcamp in New York
At Flatiron School, we teach today’s in-demand tech skills, through our dynamic, immersive courses taught by experienced, passionate industry professionals online and on WeWork campuses around the world. But we don’t stop there — we pair an industry-leading curriculum with dedicated Career Services professionals who are committed to helping you find a job you love.
We’ve been building communities of learners since 2012. Build your network as well as your knowledge with a diverse, supportive group of peers committed to growth and change.
Our instructors have both industry and teaching experience and are backed by our Master Teachers and Learning Experience Designers to ensure you get the best possible support.
Change careers with confidence thanks to our Money-Back Guarantee. Work with a Career Coach and follow our proven-job search framework, and get a job within 6 months, or your money back (see terms).
Our grads land jobs in tech
For job-seeking Manhattan graduates included in the 2019 Jobs Report including full-time salaried roles, full-time contract, internship, apprenticeship, and freelance roles, and part-time roles during the reporting period (see full Jobs Report here).
For job-seeking Manhattan students who accepted full-time salaried jobs during the reporting period and disclosed their compensation. The average starting salary for students who took full-time contract, internship, apprenticeship, or freelance roles and disclosed compensation was $32/hr. Average pay for a part-time role was $33/hr (see full Jobs Report report here).
Where our graduates work
What you'll learn: data science & machine learning
Our Curriculum Designers make complex concepts accessible, providing you with a strong knowledge foundation of data science’s latest technologies. And with our focus on project work and learning how to learn, you’ll build the skills required to start a lifelong career as a professional data scientist.
Start thinking like a data scientist
Our program provides students with the tools and skills necessary to think and work as a data scientist. Working in a WeWork with our seasoned instructors, you’ll master a mix of software engineering and statistics, then apply your expertise in new and challenging domains. Our approach ensures both readiness for today’s job market and the skills required to stay relevant into the future.
Our first module introduces the fundamentals of Python for data science. You’ll learn basic Python programming, how to use Jupyter Notebooks, and will be familiarized with popular Python libraries that are used in data science, such as Pandas and NumPy. Additionally, you’ll learn how to use Git and GitHub as a collaborative version control tool. At the end of this module, you’ll be able to build a basic linear regression model and evaluate the results. Finally, we’ll conclude with a heavy focus on visualizations as a way to convert data to actionable insights.
Variables, Booleans and Conditionals, Lists, Dictionaries, Looping, Functions, Data Cleaning, Pandas, NumPy, Matlotlib/Seaborn for Data Visualization, Git/Github
In this module, you’ll learn about data structures, relational databases, ways to retrieve data, and SQL fundamentals for data querying for structured databases, as well as NoSQL (and MongoDB) for non-relational databases. Furthermore, we’ll cover the basics of HTML, XML, and JSON in order to access data from various sources using APls, as well as Web Scraping.
Data structures, Relational Databases, SQL, Object-Oriented Programming, NoSQL databases, MongoDB, JSON, HTML/XML, Accessing Data Through APIs, CSS Web Scraping
This is a basic module that introduces the fundamentals of probability theory, where you’ll learn about principles like combinations and permutations. You’ll continue with statistical distributions and learn how to create samples with known distributions. By the end of this course, you’ll apply your knowledge by running Monte Carlo simulations and A/B tests.
Combinatorics, Probability Theory, Statistical Distributions, Bayes Theorem, Naive Bayes Classifier, Sampling Methods, Monte Carlo Simulation, Hypothesis Testing, A/B Testing
We’ll cover how and when regression models can be used to transform data into insights. You’ll learn about both linear and logistic regression and the algorithm behind regression models. By the end of this module, you’ll be able to evaluate the results of regression models and extend them to interaction effects and polynomial features. To compare the performance of models built, you’ll dive deeper into model evaluation and the bias-variance trade-off.
Linear Algebra, Linear Regression and extensions, Polynomials, Interaction effects, Logistic regression, Optimization Cost Function, Gradient Descent, Maximum Likelihood Estimation, Time Series Modeling, Regularization, and Model Validation.
In Module 5, you’ll learn how to build and implement machine learning’s most important techniques and will take your first steps into classification algorithms through supervised learning techniques such as Support Vector Machines and Decision Trees. Additionally, you’ll learn how to build even more robust classifiers using ensemble methods like Bagged and Boosted Trees, as well as Random Forests. Next, you’ll move onto unsupervised learning techniques such as Clustering, and dimensionality reduction techniques like Principal Component Analysis.
Distance Metrics, K Nearest Neighbors, Clustering, Decision Trees, Ensemble Methods, Dimensionality Reduction, Pipeline Building, Hyperparameter Tuning, Grid Search, Scikit-Learn
In the final module, you’ll learn how to use regular expressions in Python and how to manage string values, analyze text, and perform sentiment analysis. Additionally, you’ll get an in-depth overview of deep learning techniques, densely connected neural networks for high-performing classification performance, convolutional neural networks for image recognition, and recurrent neural networks for sequence modeling. You’ll also learn about techniques to evaluate performance and to optimize and regularize model performance.
Neural Networks, Convolutional Neural Networks, Ngrams, POS Tagging, Text Vectorization, Context-Free Grammars, Neural Language Toolkit, Regular Expressions, Word2Vec, Text Classification
In our final project, you’ll work individually to create a large-scale data science and machine learning project. This final project provides an in-depth opportunity for you to demonstrate your learning accomplishments and get a feel for what working on a large-scale data science project is really like. You and your fellow students will each pitch three different ideas and then decide on your final project with your instructors. Instructors advise on projects based on difficulty and feasibility given the time constraints of the course. At the end of the course, you’ll receive a grade based on various factors. Upon project completion, you’ll understand how to construct a project that gathers and builds statistical or machine learning models to deliver insights and communicate findings through data visualisation and storytelling techniques.
Join the fastest-growing corner of the tech industry
More than ever before, companies are relying on data to make business decisions. Without data science, these industry trends stay undiscovered — no story to tell and no insights to share. In order to determine business goals, more and more companies are looking for data scientists to fill in the gaps. Data science is one of the fastest-growing and sectors of the tech industry.
Growth in Data Science Jobs Since 2012
The course will qualify you for a position as a data scientist or a data analyst. If you have a professional background in programming, you may also be able to get a position as a data engineer or a machine learning engineer.
Meet your teachers
At Flatiron School, we believe that great teachers help us understand topics on a profound level and inspire us to become continual learners. With experience in the field and in the classroom, our data science instructors are dedicated and thorough. Simply put: you learn from the best.
Sean Abu joined Flatiron School after working for IBM as a Data Scientist Consultant and as a high school economics teacher. As a lead instructor, he combines his past experiences to prepare students for their future.
After earning a Masters in Statistics from New York University, Fangfang worked as a data scientist in the public policy and start-up sectors. However, her love of teaching led her to join Flatiron School as a lead instructor.
A long-time instructor, Mike has taught data science on both coasts and developed curriculum for Machine Learning and iOS Development. He has an M.S. in Applied Science from Syracuse University.
Navigate tech's top opportunities with the help of our Career Services team
At Flatiron School, you won’t just learn data science. You’ll also learn "How to be a No-Brainer Tech Hire" and effective job seeker. With 1-on-1 career coaching, a robust employer network, and a proven job search framework, our Career Services team is committed to helping you launch a career in tech.
During your job search, you’ll meet weekly with your dedicated Career Coach. Coaches help with everything from résumé review to interview prep, and help you tell your story to land your first job.
Change careers with confidence thanks to our Money-Back Guarantee. If you graduate, follow our job-search process, and don’t secure a job offer within 6 months, we’ll refund your tuition in full (see details).
We’ve built relationships with hiring managers at top companies across the world, creating a robust employer pipeline for Flatiron School grads. Our Employer Partnerships team is constantly advocating for our grads and helping you get in the door.
Through 1-on-1 guidance from our Career Coaching team and our tried-and-true job-search framework, you’ll gain the skills and support you need to launch your career.
Find the right tuition plan for you
Defer your tuition with the Flatiron School Income Share Agreement (ISA). After a refundable payment when you enroll, the remainder of your tuition is paid once you’ve left the program and are getting paid at least a minimum income.
Join us on campus
|Cohort Start Date||Campus||Status|
|Jan 6, 2020 – Apr 17, 2020||Manhattan||Closing Soon –|
|Feb 17, 2020 – May 29, 2020||Manhattan||Open –|
|Mar 30, 2020 – Jul 10, 2020||Manhattan||Open –|
Take the leap and start your journey
Flatiron School curates a community by admitting students who bring creativity, ingenuity, and curiosity to the classroom.
Submit your application. Share a bit about yourself and what’s driving you to start a career.
Speak with an Admissions Advisor in a non-technical interview. This is an opportunity for us to get to know each other a little better. Nothing technical — just a friendly conversation.
After writing and submitting some code on Learn.co, you’ll attend a live interview session with an instructor to assess your understanding of the material.
Receive your acceptance decision from Admissions. This usually happens within 4 business days.
If accepted, you'll begin course pre-work to prepare for the first day of class.
Frequently asked questions
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