Flatiron School’s proven, industry-leading curriculum has helped thousands of students start new careers as data scientists. Learn the skills necessary to reinvent yourself as a professional data scientist. Don’t just change your career – change your life.
At Flatiron School London, 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 an authentic and collaborative learning environment.
From Python to Machine Learning, our 15-week data science training program gives students the breadth and depth needed to become well-rounded data scientists.
With deferred tuition and our dedicated career services team, students can enroll with security. Enroll today and only pay once you’er hired and earning income. We just need a refundable £1,000 deposit before you start.
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 dedicated to helping students secure the career they want upon graduation.
Over 15 challenging weeks at Flatiron School’s campus, students will learn how to gather data, apply statistical analysis to answer questions with that data, and make their insights and information as actionable as possible. Our course load ensures not only job readiness for today’s market, but also the skills to keep learning and stay relevant.
Students will gain an overview of the skills of understanding data structures, data gathering and data cleaning.
Students will learn fundamental concepts in programming using Python and SQL to gather and clean data.
Students will learn what statistical distributions are, and how to run a linear regression model.
Students will gain an overview of the skills of understanding data structures, data gathering and data cleaning.
Students will learn fundamental concepts in programming using Python and SQL to gather and clean data.
Students will learn what statistical distributions are, and how to run a linear regression model.
Students will learn about the Ordinary Least Squares method and gradient descent in a linear regression.
Students will learn about Monte Carlo simulations and Markov Process. Then students will revisit experimental design techniques and apply their deeper statistical knowledge to A/B testing a website.
Students will learn how the Ordinary Least Squares method and gradient descent in a linear regression work.
Students will learn about the Ordinary Least Squares method and gradient descent in a linear regression.
Students will learn about Monte Carlo simulations and Markov Process. Then students will revisit experimental design techniques and apply their deeper statistical knowledge to A/B testing a website.
Students will learn how the Ordinary Least Squares method and gradient descent in a linear regression work.
Students will learn about decision tree learning and about ensemble methods such as bagging, boosting and random forests.
Students will learn clustering techniques like k-means and hierarchical clustering, and dimensionality reduction techniques, such as Principal Component Analysis.
Students will learn how to to analyze time series data (such as stock market prices, temperature, sales… over time) using Panda.
Students will learn about decision tree learning and about ensemble methods such as bagging, boosting and random forests.
Students will learn clustering techniques like k-means and hierarchical clustering, and dimensionality reduction techniques, such as Principal Component Analysis.
Students will learn how to to analyze time series data (such as stock market prices, temperature, sales… over time) using Panda.
Students will learn how to manage string values, analyze text and perform sentiment analysis in Python.
Students will learn how to use PySpark, a distributed programming framework which makes working with huge datasets feasible.
Students will learn about densely connected neural networks, for highly performant classifiers, convolutional neural networks for image recognition, and recurrent neural networks, for sequence modeling.
Students will learn how to manage string values, analyze text and perform sentiment analysis in Python.
Students will learn how to use PySpark, a distributed programming framework which makes working with huge datasets feasible.
Students will learn about densely connected neural networks, for highly performant classifiers, convolutional neural networks for image recognition, and recurrent neural networks, for sequence modeling.
How to construct a project that gathers, explores, builds statistical or machine learning models to deliver insights and communicate findings with data visualization and storytelling techniques.
Flatiron School has over five years of working with passionate students and helping them launch fulfilling careers in tech, we’ve developed a keen understanding of what goes into getting that first data scientist job – both on our end and what you’re empowered to do on yours.
During the job-search phase, students meet with a dedicated coach every week, to ensure an effective job search via resume review, mock interviews, and developing the right job-search collateral to tell their story.
At Flatiron School, you won’t just learn to code. You’ll also learn how to be an effective job seeker and no-brainer tech hire. Through one-on-one guidance from our Career Services team and our tried-and-true job search framework, you’ll launch your career in code far beyond the bootcamp.
For over six years, our Employer Partnerships team has been developing relationships with hiring partners across the country to help Flatiron School grads get in the door. Our dedicated Employer Partnerships team is the best in the industry — constantly evangelizing for our graduates at companies across the world.
After you’ve been admitted and enroll you’ll pay a £1,000 deposit and then tuition becomes due once you get a job/start earning income.
After graduation, our students have gone on to contribute to some of the most exciting and impactful companies in London.
Every day is unique at Flatiron School, and our curriculum team and instructors develop new lessons that build off past experiences. But structured, consistent learning is also crucial to student success. Below is what you can expect on a daily basis in a Flatiron classroom.
Students’ questions start a morning conversation, used to review new skills and program materials from the day before to ensure each student is up to speed.
Students learn key concepts from their expert instructor through interactive exercises and collaborative discussion.
Two students will work together to build statistical analyses and robust coding strategies.
Students will apply lessons learned from lecture to solve real business problems — take messy data, clean it, and gain actionable insights from the numbers.
Students end the day reviewing concepts and strategizing the next steps in their personal projects.
Since day one over five years ago, we’ve taken teaching seriously. Great teachers inspire us to connect to 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: students learn from the best.
Learn from full-time, seasoned, passionate instructors who teach students both the hard and soft skills they need to change their lives.
As a part of our ongoing effort to support diversity in tech, Flatiron 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.
Our school brings together people who see data science as a craft, and want to be great at it. Our students come from myriad backgrounds and previous career paths — ensuring that a diversity of thought, experience, and perspective are not only invited, but actively sought. At Flatiron School, all you need is passion and an open mind.
Apply to the course. Tells us about yourself and why you want to start a career in code.
Speak with a member of the Admissions team about your interests and ambitions. There is no technical preparation for this one.
The technical review assesses your basic grip of coding and how it interacts with us daily. Showing proactiveness by completing steps on Learn, our learning platform, also helps.
Receive your acceptance decision from Admissions. This usually happens within a couple of days, and would be your final step!
All scholarships to our in-person program are granted after a student is admitted. Scholarships are granted on a need and merit basis, with preference given to underrepresented minorities in tech, including women and military veterans. If you feel that describes you, you can share your story with us on the scholarship application once you are admitted into the program.
We recognize that sometimes “life happens” and that students who have been admitted to one class may need to defer and start with us at a later date. Students may defer up to three start dates beyond the class to which they are admitted. If you must defer farther out than that, we may ask you to repeat some or all of the admissions process to ensure your readiness for the later start date. Students may defer only once without reapplying.
Flatiron School’s application process is rigorous, and sometimes students who don’t get accepted the first time around are able to ‘study up’ and get accepted the second time around. As such, students are invited to re-apply after three months have passed from initial decision. Students are permitted a total of three application attempts, so re-applicants are advised to use that time building their skills (both professional and technical) and to submit a second application that is materially different from the first one, showcasing your hard work and improvements over that time.
Admissions are conducted on a rolling basis, so we continue to accept new applications until the course is filled. Therefore, there is no deadline to apply by – though the sooner you get your application in, the better we are able to prioritize it. Because our classes fill up well in advance of the start date, we recommend applying at least 8 weeks before your desired course date. This allows us 2-3 weeks to conduct the application process and accounts for time to complete the mandatory 100 hours of pre-work, which most students report takes at least 3 weeks.
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