Start Your Career as a Data Scientist
Our online courses teach a tried-and-true data science curriculum tailored to assure students graduate as well-rounded data scientists. Using real-world tools, students learn from some of the country’s top instructors on Flatiron’s proprietary platform, Learn.co.
From the basics of Python to the complexities of machine learning, our online data science program provides the breadth and depth needed to become a skilled and versatile 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.
Using your included one-year WeWork hot desk membership — valued at over $2,500 annually — you can connect locally and in person with other students to study in an energizing, beautiful workspace. 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.
Graduate with a new job you love. Our proven job-search framework features 1:1 sessions with a dedicated Ccareer Ccoach, a robust employer pipeline, résumé review, interview prep, and more. All of that is backed with our Tuition-back Guarantee — get a job in six months or your money back (see details).
Course Pace Options
Each course teaches a proven, living data curriculum that sets you up to be an adaptable and valuable data scientist when you graduate. Though each pace differs in time and structure, you’ll learn the same tried-and-true curriculum no matter which pace you enroll in.
Graduate and become a data scientist in 5 months with our fastest program pace: full-time. Students will work through the 45-50 hours per week of coursework and benefit from dedicated, full-time learning.
Students benefit from learning with a small, cohort and a dedicated Cohort Lead who teaches and mentors throughout the entire course schedule, at a 20:1 student:teacher ratio. Attend live study groups each week with your cohort lead.
Meet 1:1 for a full hour with your Cohort Lead weekly to get help breaking through tough technical concepts. Additionally, connect over 1:1 sessions with a dedicated Educational Coach every 2 weeks to review overall progress stay accountable for your progress. These sessions are in addition to real-time support from our Technical Coaches via our Ask A Question feature in Learn.co.
Graduate in 10 months at a pace that offers the same structure and rigor as our full-time option, but on a part-time schedule. Students work through a minimum of 25 hours per week of coursework and benefit from a structured schedule designed to keep them on track towards a career in code. While completing the program, students need to dedicate a part of their day-to-day in order to move through the coursework on schedule.
Students benefit from learning with a cohort of up to 40 peers, and a dedicated Cohort Lead who teaches and mentors throughout the entire program schedule. Students dedicate time to a live study groups with their cohort lead every day, with options to fit their schedule.
Meet 1:1 once a month with an Educational Coach to help you stay accountable to your goals, anchor to your motivation, and review your overall progress. Partner with your Coach on addressing challenges, and striking the right balance between your coursework and the rest of your life. These sessions are in addition to to real-time support from our Technical Coaches via our Ask A Question feature.
Our self-paced program provides the same rigorous curriculum and mastery standards of our full-time and part-time paces, but at a 100% flexible, self-guided schedule. This program most benefits students who have familiarity with data science and unpredictable schedules by providing access for up to 15 months as they guide themselves through the coursework.
Includes five sessions with an Educational Coach to help you set and stay accountable to your goals, and make steady progress at a pace that is right for you. Students also get on-demand access to Technical Coaches through the Ask A Question feature in Learn.
Collaborate with fellow students and teachers, join live study groups, pair and chat with your peers, and access a living course that features thousands of updates a year to reflect current industry trends.
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.
650%
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.
What you'll learn: data science & machine learning
Cutting-edge curriculum
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.
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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.
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: you’ll 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 nation, 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.
Making education possible
Loan
Full-Time, finance for as low as
$413/mo
(for 36 months + $1.5K deposit)
Part-Time, finance for as low as
$327/mo
(for 36 months + $1.5K deposit)
Self-Paced, finance for as low as
$248/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. Available only to those who qualify.
Pay Upfront
Full-Time Program
$15,000
Part-Time Program
$15,000
Can be paid in 10 monthly installments.
ISA
Flatiron School Online Income Share Agreement — Available for Part-Time and Full-Time Courses
You can now defer your full-time or part-time online tuition with the Flatiron School Income Share Agreement, available in select states. There is no deposit required when you enroll, and the remainder of your tuition is paid once you’ve left the program and are earning a minimum income of $3,333.33 per month, annualized to $40,000 per year.
What the application process looks like
Start your journey to a career as a data scientist by joining our inclusive, dynamic student community. At Flatiron School, we admit students who bring creativity, ingenuity, and curiosity to the classroom — all you need is passion and an open mind.
Submit your application. Share a bit about yourself and what’s driving you to start a career in code.
Speak 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.
Receive your acceptance decision from Admissions. This usually happens within a couple of days.
When you enroll, you'll choose the pace that best suits you. Our admissions team is happy to discuss this decision with you.
Join us on campus
| Cohort Start Date | Status |
|---|---|
| Sep 9, 2019 – Jul 17, 2020 | Closing Soon – |
| Sep 9, 2019 – Feb 14, 2020 | Closing Soon – |
| Oct 7, 2019 – Aug 14, 2020 | Open – |
| Oct 7, 2019 – Mar 13, 2020 | Open – |
| Nov 4, 2019 – Sep 18, 2020 | Open – |