Apply for our exclusive Data Science Fellowship
At Flatiron School D.C., we bring together passionate instructors, uncompromising education, verified outcomes, and an ambitious class of students to fuel a highly productive learning environment.
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.
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
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.
Where our graduates work
What you'll learn: data science & machine learning
Our Curriculum Designers make complex concepts accessible, providing you with a strong foundation and instruction on the latest technologies. And with our focus on project work and learning how to learn, you’ll build the skills required to build a lifelong career as a professional data scientist.
Start thinking like a data scientist
Our program provides students with the tools, skills, and expertise 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 both skills 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 program moves quickly and our students embrace that challenge. No experience beyond high school algebra and comfort with computers is necessary to apply. But we require students to demonstrate the aptitude to learn data science by completing our pre-work before day one. The prework also introduces you to foundational math and Python programming skills required to succeed in the course.
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 highest-paying 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 instructors
At Flatiron School, we believe that great teachers inspire us to understand topics on a profound level and inspire us to become continual learners. With experience both in the field and in the classroom, our data science instructors are dedicated and thorough. Simply put: Flatiron School students learn from the best.
Alison has private and public sector experience as a statistician, consultant, and Data Scientist. She’s taught data mining and built curriculum at Carnegie Mellon for the Masters of Science in Information Technology students.
Lore earned her PhD in Business Economics and Statistics at KU Leuven, Belgium and has a thorough background building out R and Python data science curriculum.
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.
Land a job in six months or less
At Flatiron School, you won’t just learn data science. You’ll also learn how to be an effective job seeker and no-brainer tech hire. With one-on-one career coaching, a robust employer network, and a proven job search framework, our Career Services team is committed to helping you land the job you want.
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 get the job you want.
Change careers with confidence thanks to our Money-Back Guarantee. If you graduate, follow our job search process, and don’t secure a job 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 evangelizing our grads and helping you get in the door.
Through 1:1 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.
Take the leap and start your journey
We curate a community by admitting students who bring creativity, ingenuity, and curiosity to the classroom.
Submit a quick online application.
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.
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.
Join us on campus
|Cohort Start Date||Status|
|Oct 7, 2019 – Jan 24, 2020||Closing Soon –|
|Nov 18, 2019 – Mar 6, 2020||Open –|
|Jan 6, 2020 – Apr 17, 2020||Open –|
|Feb 17, 2020 – May 29, 2020||Open –|
|Mar 30, 2020 – Jul 10, 2020||Open –|
Frequently asked questions
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