Overview

Establish yourself as a data scientist

Effective learning doesn’t simply come from consuming educational content, but comes from connecting with people who are learning and teaching it. With that vision in mind, Flatiron School has brought together passionate, experienced instructors and ambitious students to achieve incredible outcomes since 2012. And now we’re bringing that vision to data science, with our NYC Data Science Bootcamp.

Uncompromising Education

From Python to Machine Learning, our 15-week data science training program gives students the breadth and depth needed to become well-rounded data scientists. Students also leave with an understanding of how to discover new techniques as their career progresses.

Career Services

With a proven framework for leading a successful job search, a robust employer network, and weekly 1:1 sessions with a dedicated career coach — our career services team is unparalleled at helping students secure the career they want upon graduation.

Exceptional Community

Flatiron classes are carefully crafted to bring out the best in students. Our NYC Data Science Bootcamp is both highly selective and deliberately diverse to inspire the creativity necessary to shine in this field.

Money-Back Guarantee

Flatiron students change careers with confidence thanks to our money-back guarantee: you’ll receive a job offer within six months, or we’ll refund your full tuition. (See eligibility terms.)

Curriculum & Program Experience

What You'll Learn: Data Science & Machine Learning

Our career-ready Data Science curriculum provides the technical skills, expertise, and tools necessary to think and work as a data scientist. Working in our WeWork classroom with our seasoned instructors, you’ll master a mix of software engineering and statistical understanding, then apply both skills in new and challenging domains.

Our robust Data Science program ensures not only job readiness for today’s growing job market, but the aptitude to continue learning and stay relevant in your career for years to come.

Week

  • Pre-work
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15

Our Data Science program moves quickly and our passionate students embrace that challenge. While no experience is necessary to apply, we require you to demonstrate some data science knowledge prior to getting admitted, then complete a prework course before Day 1. To help you prepare for our bootcamp, we provide a free introductory course. This prework ensures that you come in prepared and able to keep pace with the class.

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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.

Module 1 Topics

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 the fundamentals of SQL 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.

Module 2 Topics

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 AB tests.

Module 3 Topics

Combinatorics, Probability Theory, Statistical Distributions, Bayes Theorem, Naive Bayes Classifier, Sampling Methods, Monte Carlo Simulation, Hypothesis Testing, AB 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.

Module 4 Topics

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 asRandom Forests. Next, you’ll move onto unsupervised learning techniques such as Clustering, and dimensionality reduction techniques like Principal Component Analysis.

Module 5 Topics

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.

Module 6 Topics

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.

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Why Data Science?

Join the fastest-growing corner of the tech industry

More than ever before, industries are capturing data on a variety of topics, behaviors, and trends. Without data science, this information stays stuck – no story to tell and no insights to share. In order to determine business goals, more and more companies are looking to data scientists to fill in the gaps and find opportunities never before considered or understood.

Over the last 4 years, the rise of job opportunities for data scientists has become almost level to those traditionally seen for software engineering positions.

Growth in Data Science Jobs Since 2012

650%

As this area of expertise has grown, the positions within the field become more nuanced. After completing our NYC Data Science Bootcamp, students will not only be able to secure a job as a data scientist, but can also consider pursuing any of the following related positions:

  • Data Engineer
  • Machine Learning Engineer
  • Big Data Engineer
  • Back-End Engineer
  • Natural Language Processing

“Data Science is leading the way in changing how people and businesses make decisions. With that in mind, we’ve developed a curriculum that teaches the skills necessary for the field, but also creates an environment that cultivates the curiosity all great data storytellers share.”

Joe Burgess

Joe Burgess

VP, Education
Student Projects

Build your data science portfolio

At Flatiron School, students learn by building. Students will come away with an advanced Portfolio Project to demonstrate their technical proficiency and creativity to current or future job managers and hiring leads.

Our course dedicates three weeks towards completion of a large-scale data science and machine learning project where students work in groups of two. The project provides an in-depth opportunity for students to demonstrate their learning accomplishments and get a feel for what working a large-scale data science project is really like.

Active Github Profile

GitHub is the modern source of technical resumes. Students push every line of code they write at Flatiron School to GitHub through our proprietary platform, Learn.co, giving them an extensive profile to show employers and fellow data scientists.

Technical Presentation

Students build their credibility as data scientists and immerse themselves by attending — and challenging themselves to present — at Flatiron School campus events.

Final projects will be unique for each student-pairing and determined in partnership with their instructor. Specifically, projects are required to have the following items to meet the minimum bar of difficulty:

  • Retrieve data from outside sources  and organize data using Python
  • Organize data into at least three different tables or equivalent grouping
  • Explore data and write down multiple hypotheses for data, and write proposal to use subset of algorithms to analyze the data
  • Building machine learning API that outputs results of analysis
  • Use big data for at least one aspect of the project
  • Present techniques and conclusions about approach and analysis in write up

Examples of final projects could be building a better search engine for a travel site (such as Airbnb or Kayak) or an examination of a global interest utilizing data visualization and topic modeling.

Meet your teachers
Instructors

Meet your teachers

We take teaching seriously. Great teachers inspire us to connect with topics on a profound level. With experience in the field and the classroom, our data science instructors are unmatched. Simply put: students learn from the best.

Teaching is a craft. Our students learn from the best instructors in the industry.

Career Services

Get a job within six months of graduation

Flatiron students get jobs, period. As a graduate of our Data Science Bootcamp, get a job within six months of graduation or get your money back (see eligibility terms). From weekly 1:1 career coaching sessions, to mock interviews, to employer introductions, the seasoned Career Services team behind our student success stories is dedicated to helping our students launch lifelong careers in data science.

Dedicated Career Coaching

Every student receives a dedicated career coach who will mentor students through an effective job search via resume review, mock interviews, and strategies for building a job opportunity pipeline and getting a foot in the door at top-choice companies.

Money-Back Guarantee

Flatiron students change careers with confidence thanks to our money-back guarantee: you’ll receive a job offer within six months, or we’ll refund your full tuition. (See eligibility terms.)

A Proven Job Search Framework

After years of helping students get hired, we’ve developed a proven framework for leading a successful job search. Nearly every single student who has followed these guidelines has been hired.

Admissions

Take the leap, apply now

We don’t just admit individual students to our program; we curate a community. Our students come from a myriad of backgrounds and previous career paths — insuring that a diversity of thought, experience, and perspective are not only invited, but actively sought.

Application process

What we look for

1. Apply

Submit a written application. Tell us about yourself and why you want to join our community.

Passion

We love data science. We bring together people who see it as a craft and want to be great at it — not just for their careers or as a means to an end, but as an end in and of itself.

2. Admissions Interview

Chat with an Admissions Advisor in this explicitly non-technical interview . This is an opportunity for us to get to know each other a little better. Nothing technical — just a friendly conversation.

Aptitude

Flatiron students are driven. While we look for students with an ability to transfer between different skill sets easily — above all, aptitude for data science is built around having an innate curiosity for the world and how people live in it.

3. Technical Review

After writing and submitting some code on Learn.co, you’ll attend a live coding session with an instructor to assess your understanding of the material covered in the prework. You’ll then be notified of your status within 4 business days.

Culture

We don’t admit students. We craft a class. A lawyer, journalist, and pro athlete will do more interesting things together than three of any one background.

Application process

What we look for

Program Dates

Questions?

Talk with our admissions team — they’re here to help.

Tuition & Scholarships

Find the right tuition plan for you

Loan
Finance for as low as
$
380 /month
(for 36 months + $3k deposit)

Dedicated to making our programs more accessible, we offer competitive financing options through Skills Fund and Climb, two industry-leading accelerated learning financing companies.

Full Tuition
Full tuition
$
17,000

Scholarships

As a part of our ongoing effort to support diversity in tech, Flatiron is pleased to offer various scholarships to qualified women, minorities, and veterans, as well as merit-based scholarships. Schedule a Q&A with our Admissions team to hear more about our open scholarships.

FAQ

You have questions; we have answers

  • Can I visit the Manhattan campus?

    We offer tours and info sessions on a regular basis, and you can register on our community page. If our event times don’t work for you, please email admissions@flatironschool.com to schedule an appointment at an alternative time.

  • Can I apply for financing?

    Once you have been accepted into the program, you may apply for financing through one of our lending partners. We work with Skills Fund and Climb, who have helped many Flatiron students secure loans. We recommend waiting until after you have been accepted into the program before applying for a loan.

  • Can I chat with someone on the Admissions team?

    Absolutely! You can schedule an appointment with someone from the Admissions team here: http://www.meetme.so/flatironadmissions.

  • Do I need a computer?

    For the in-person courses, we work on Mac laptops with the latest OS installed. If you don’t have one already, we can provide a loaner. For online courses, you do need your own computer, but it can be either a Macbook or a PC. You can view the minimum required technical computer specs for all courses here.