Overview

Learn data science & change things

Quality learning comes at the intersection of diligence and curriculum. 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 Seattle Data Science Bootcamp.

Industry-Leading Curriculum

From the basics to the most complex data science practices, our 15-week data science program provides the full scope of skills you need to become a well-rounded data scientist. But we don’t stop there: you’ll leave the program with an understanding of the future of data science so you can embark on a fulfilling, lifelong career.

Interactive and Creative Classes

Flatiron School classes have one goal: to give students the ideal environment to become exceptional data scientists. Our Seattle Data Science Bootcamp is both highly selective and deliberately diverse to inspire the creativity and discipline necessary to shine in the field of data.

Dedicated Career Coaching

Graduate with a new job you love. Our proven job-search framework features 1:1 sessions with a dedicated career coach, a robust employer pipeline, résumé review, interview prep, and more. We’re committed to helping you secure a career in software engineering — learn about our Tuition-back Guarantee (see details) and hear from our graduates.

The WeWork Classroom

Studying inside our WeWork campuses, you’ll learn to code while becoming a part of the dynamic, bustling WeWork community of start-ups, entrepreneurs, students, and people who are changing things. Plus, enjoy WeWork’s complimentary amenities: high-speed WiFi, 24/7 building access, craft beer on draft, conference rooms, phone booths… plus that fancy fruit water and micro-brewed coffee to help you focus.

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
  • Post-work

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.

nothing

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.

nothing
Why Data Science?

Join the fastest-growing corner of the tech industry

More than ever before, companies are relying on data and behavioral patterns 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 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%

Not only are data scientist jobs increasing, but so are the various specialities under their umbrellas. After completing our Seattle Data Science Bootcamp, you’ll 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, you learn by building. You’ll graduate with an advanced Portfolio Project that provides the opportunity to demonstrate your technical expertise to future employers and to have 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

You’ll build credibility as a data scientist by attending and presenting at Flatiron School campus events.

Final projects will be unique for each student-pairing and determined in partnership with your 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, write proposal to use subset of algorithms to analyze the data
  • Build 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

At Flatiron School, we take teaching seriously and know that 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: Flatiron School students learn from the best.

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

Career Services

Navigate the Seattle job market with help from our Career Services team

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.  

Individual Career Coaching

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.

Job Search Framework

Through one-on-one guidance from our Career Services team and our tried-and-true job search framework, you’ll receive the skills and support you need to launch your career as a developer.

Vast Employer Network

We’ve built relationships with hiring managers at top companies across the nation, creating a robust employer pipeline for Flatiron School grads. Our best-in-class Employer Partnerships Team is constantly evangelizing our grads and helping you get in the door.

Tuition-Back Guarantee

Change careers with confidence thanks to our Tuition-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.)

Admissions

Take the leap and start your journey

Flatiron School 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 written application to our Admissions team. Tell us about yourself and why you want to learn to become a data scientist in Seattle.

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 Chat

Chat 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.

Aptitude

Flatiron School 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 pre-work. You’ll then be notified of your status within 4 business days.

Culture

We don’t just admit students, we craft a community. A barista, journalist, and lawyer will approach solutions differently, and bringing them together creates richer learning environment for everyone involved. Our goal is to make classes diverse and filled with people from all walks of life. We also look for individuals who have a passion for learning and an open mind to problem solving.

Application process

What we look for

Program Dates

Questions?

Talk with our Admissions team — we’re here to help.

Tuition & Scholarships

Find the right tuition plan for you

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

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

Full Tuition
Full tuition
$
15,000
ISA
Deferred Tuition

With our Income Share Agreement (ISA), after an initial deposit, you pay nothing toward your tuition until after you’ve graduated and accepted your first job offer. You don’t pay until you’re earning income. All we need is a refundable deposit before class begins to lock in your seat.

Scholarships

As a part of our ongoing effort to support diversity in tech, Flatiron School 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