Attend the most comprehensive data science bootcamp in San Francisco
At Flatiron School San Francisco, we offer more than just a data science program. We bring together passionate instructors, uncompromising education, verified outcomes, and an inclusive student community to fuel a productive learning environment.
From Python to Machine Learning, our 15-week data science program provides the full scope of skills you need to become a well-rounded data scientist. You’ll leave the program with a deep understanding of fundamental statistics, Natural Language Processing, and a complete toolkit that will make you well prepared for a career as a data scientist.
Studying on our WeWork campus, you’ll learn to code while becoming a part of the dynamic, bustling WeWork community of start-ups, entrepreneurs, students, and humans who are changing things. Plus, enjoy WeWork’s 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 — all complimentary.
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 data science — learn about our Money-Back Guarantee and hear from our graduates.
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.
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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.
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 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.
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.
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.
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.
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 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 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
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.
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 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 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.
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 as a software engineer.
Companies that have hired our graduates
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 in code.
Meet a member of our Admissions team about elaborate on your interests and ambitions. There is no technical prep needed for this step.
The technical review assesses your basic grip of data science and how it interacts with us in our daily lives. Showing proactiveness by completing steps on Learn, our learning platform, definitely helps.
Receive your acceptance decision from Admissions. This usually happens within a couple of days.
If and when you are accepted, the next step is course pre-work so that you’re ready for day one.
Cohort Start Dates
| Cohort Start Date | Status |
|---|---|
| Nov 18, 2019 – Mar 6, 2020 | Open – |
Find the right tuition plan for you
Loan
Finance for as low as
$529/mo
(for 36 months + $1K 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.
Pay Upfront
Full Tuition
$17,000
ISA
The Flatiron School ISA: Pay After You Get Hired
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.