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 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, conference rooms, phone booths… plus that fancy fruit water and micro-brewed coffee to help you focus.
You deserve to find a 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. 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. 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, Educational Coaches serve as the Community Lead for each cohort and can help you connect with your classmates and polish your study habits.
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.
Communicate with your 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. Your Educational Coach is a resource in addition 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.
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.
Speak with an Admissions representative 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.
The quiz was designed to give you the opportunity to self-assess the prerequisite knowledge needed to join the program, categorized into three main types: programming questions, statistics questions, and math questions.
The assessment consists of 8 questions, either multiple choice or short answer text. The test should take a minimum of 30 minutes to complete.
After completing the assessment, your admissions representative will follow up with you within two business days with next steps.
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.
What you'll learn: data science & machine learning
From Python to Machine Learning, our 15-week data science training program gives you the breadth and depth needed to become a well-rounded data scientist. You’ll also leave with an understanding of how to discover new techniques as your career progresses.
Every 3 weeks you’ll be introduced to a new module that builds off the learnings of the previous section while allowing you enough time to dive into each area for a thorough understanding of the subject matter.
The 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 you come in prepared and are 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. To organize your data, you’ll learn about data structures, relational databases, ways to retrieve data, and the fundamentals of SQL for data querying for structured databases. Furthermore, you’ll learn how to access data from various sources using APls, as well as perform Web Scraping.
Finally, we’ll conclude with a heavy focus on visualizations as a way to go from data to insights.
At the end of this module, students will use their newly learned skills to collect, organize and visualize data, with the goal to provide actionable insights!
Variables, Booleans and Conditionals, Lists, Dictionaries, Looping, Functions, Data Structures, Data Cleaning, Pandas, NumPy, Matplotlib/Seaborn for Data Visualization, Git/Github, SQL, Accessing Data Through APIs, Web Scraping
Having learned how to gather and explore data with Python and SQL you can now go deeper into analyzing that information with statistics. In this module, you’ll learn about the fundamentals of probability theory, where you will learn about probability principles such as combinations and permutations. You will go on and learn about statistical distributions and how to create samples when distributions are known. By the end of this module, you will be able to apply this knowledge by running A/B tests. Additionally, you’ll learn how to build your first (and important) data science model: a linear regression model.
Combinatorics, Probability Theory, Statistical Distributions, Bayes Theorem, Sampling Methods, Hypothesis Testing, A/B Testing, Linear Regression, Model Evaluation
Module 3 is all about machine learning, with a heavy focus on supervised learning. To start, you will go a little deeper into regression analysis, learning about extensions to linear regression, and a new form of regression: logistic regression. In building regression models, students will learn about penalization terms, preventing overfitting through regularization and using cross validation to validate regression model.
Next, you’ll learn how to build and implement the most important machine learning techniques. You’ll learn about classification algorithms such as Support Vector Machines and Decision Trees. Additionally, you’ll learn how to build even more robust classifiers using ensemble methods such as Bagged and Boosted Trees, and Random Forests.
Linear Algebra, Logistic Regression, Maximum Likelihood Estimation, Optimization Cost Function, Gradient Descent, K Nearest Neighbors, Decision Trees, Ensemble methods, Pipeline Building, Hyperparameter Tuning, Grid Search, Scikit-Learn
After a full module on supervised learning, this module focuses on a variety of advanced Data Science techniques. You will start with learning about unsupervised learning techniques such as clustering techniques and dimensionality reduction techniques. Next, you will be introduced to threading and multiprocessing to be able to work with big data. In doing so, you’ll learn about PySpark and AWS, and how to use those tools to build a recommendation system. Next, you will get an in-depth overview of deep learning techniques, learning about densely connected neural networks, enabling high-performing classification performance. Next, students will learn how to use regular expressions in Python, and how to manage string values, analyze text and perform sentiment analysis.
Dimensionality Reduction, Clustering, Time Series Analysis, Neural Networks, Big Data, Natural Language Processing, Text Vectorization, Natural Language Toolkit, Regular Expressions, Word2Vec, Text Classification, Recommendation Systems
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 course’s time constraints. At the end of the project, you’ll receive a grade based on various factors.
Upon project completion, you’ll know how to construct a project that gathers and builds statistical or machine learning models to deliver insights and communicate findings through data visualization and storytelling techniques.
Final Project
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.
After earning a Masters in Statistics from New York University, Fangfang worked as a data scientist in the public policy and start-up sectors. However, her love of teaching led her to join Flatiron School as a lead instructor.
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 a No-Brainer Tech Hire" and effective job seeker. With 1-on-1 career coaching, a robust employer network, and a proven job search framework, our Career Services team is committed to helping you launch a career in tech.
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 land your first job.
Change careers with confidence thanks to our Money-Back Guarantee. If you graduate, follow our job-search process, and don’t secure a job offer within 6 months of your job search start date, we’ll refund your tuition in full (see terms).
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 advocating for our grads and helping you get in the door.
Through 1-on-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
$479/mo
(for 36 months + $500 deposit)
Part-Time, finance for as low as
$479/mo
(for 36 months + $500 deposit)
Self-Paced, finance for as low as
$317/mo
(for 36 months + $500 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
Self-Paced Program
$9,600
Divide tuition into easy payments.
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.
When courses begin
| Cohort Start Date | Status |
|---|---|
| Jul 6, 2020 – Nov 27, 2020 | Open – |
| Aug 17, 2020 – Jan 15, 2021 | Open – |
| Sep 28, 2020 – Feb 26, 2021 | Open – |