Apply for our exclusive Data Science Fellowship

At Flatiron School DC, we bring together passionate instructors, uncompromising education, verified outcomes, and an ambitious class of students to fuel a highly productive learning environment.

Uncompromising Education

From Python to Machine Learning, our 15-week Data Science Fellowship gives students the marketable skills 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 Data Science Fellowship is both highly selective and deliberately diverse to inspire the creativity necessary to shine in this field.

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Free Tuition

The Data Science Fellowship is free for successful applicants. There is a shortage of Data Scientists with the necessary skills to work in the fast-growing tech industry. The Data Science Fellowship helps to close this gap.

Curriculum & Program Experience

What you’ll learn


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Foundations of Data Science Libraries

Students will gain an overview of the skills of understanding data structures, data gathering and data cleaning.

Python & SQL

Students will learn fundamental concepts in programming using Python and SQL to gather and clean data.


Linear Regression Model

Students will learn what statistical distributions are, and how to run a linear regression model.


Frequentist & Bayesian Statistics

Students will learn about Bayesian and Frequentist statistics so that they can go deeper into analyzing information with statistics.


Building & Validating Regression Models

Students will learn how the Ordinary Least Squares method and gradient descent in a linear regression work.


Repeated Random Sampling

Students will revisit experimental design techniques and apply their deeper statistical knowledge to A/B testing a website.


Supervised Learning: Non-parametric Algorithms

Students will learn about decision tree learning and about ensemble methods such as bagging, boosting and random forests.


Unsupervised Learning Techniques

Students will learn clustering techniques like k-means and hierarchical clustering, and dimensionality reduction techniques, such as Principal Component Analysis.


Time Series Modeling

Students will learn how to to analyze time series data (such as stock market prices, temperature, sales… over time) using Pandas.


Regular Expressions

Students will learn how to manage string values, analyze text and perform sentiment analysis in Python.


Big Data

Students will learn how to use PySpark, a distributed programming framework which makes working with huge datasets feasible.


Deep Learning Techniques

Students will learn about densely connected neural networks for highly performant classifiers, convolutional neural networks for image recognition, and recurrent neural networks for sequence modeling.

Create a large scale data science and machine learning project

Construct a project that gathers, explores, and builds statistical or machine learning models to deliver insights, and communicate findings with data visualization and storytelling techniques.

Why Data Science?

Create change through data-driven insights

More than ever before, industries, businesses, and non-profits are capturing data on a variety of behaviors and trends. Without data science, this information stays stuck — without a story to tell or insights to share.

In order to shape policy and determine business goals, more and more organizations are looking to data scientists to fill in the gaps and find opportunities never before considered.

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


Not only are data scientist jobs increasing, but so are the various specialties under their umbrellas. After completing the Data Science Fellowship, 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

A portfolio designed to get you a job

At Flatiron School, students learn by building. They build projects using the tools and techniques learned in each 3-week sprint, and also come away with an advanced Portfolio Project which provides the opportunity to demonstrate their technical expertise to future employers and get their first real big-data experience under their belt.

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 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

Meet your teachers

We take teaching seriously. Great teachers inspire us to understand topics on a profound level. With experience both in the field and in the classroom, our data science instructors are dedicated and thorough. 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 that you love

Flatiron students get jobs, period. From weekly 1:1 career coaching sessions, to mock interviews, to employer introductions, our seasoned Career Services team behind our student success stories is dedicated to helping our graduates launch fulfilling careers in Data Science. We work with each student individually to surface relevant opportunities based on your preferences.

Dedicated Career Coaching

Every student has a personal Career Coach who mentors them through an effective and successful job search.

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.

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Access to Hiring Companies

Why cold-apply when you can get an introduction? With our curriculum vetted by industry leaders, the Data Science Fellowship arms graduates with the leading skills in demand by employers. Our team works with you to develop an individualized job search plan, then provides leads and introductions to job openings. Gain access to the top-tier companies that we work with on a weekly basis.


Take the leap and start your journey

Flatiron curates a community by admitting students who bring creativity, ingenuity, and curiosity to the classroom.

Application process

What we look for

1. Apply

Submit a quick online application.


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 Chat

In your first chat with our Fellowship admissions team, we’ll be looking to understand whether you’ll be successful in this challenging course –  but not in a technical way. What are your goals and why do you want to be in this program? Does your schedule align with the rigor of the Fellowship? Talking with the Fellowship admissions team builds an understanding of whether your objectives align with what we deliver through the Fellowship.


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

Attend a live session (in person or via video call) with an instructor to assess your understanding of fundamental material. You’ll then be notified of your status within 2 business days.


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


Talk with Robin Terry, Account Executive for the Data Science Fellowship at robin.terry@flatironschool.com or schedule a chat.


You have questions; we have answers

  • Who is eligible?

    We’re looking for applicants with strong technical backgrounds (such as a Master or PhD degree in statistics, technology, engineering, or math). You do not need to currently be a student in order to apply. Faculty and postdocs are also welcome to apply.

  • What is the time commitment?

    The Data Science immersive is an intense 15-week program. You’ll be expected to be on campus from 9 am to 6 pm, from Monday to Friday, with the potential for some extra work outside of those hours.

  • What can I do to prepare for the Data Science Fellowship?

    Once you’re admitted to the Data Science Fellowship, we’ll make pre-work material available to you so that you can feel confident you’re coming in with a solid fundamental understanding.